Feedoptimise Blog

Introducing First AI Agent for Feed Management and Optimisation

Wed, 04 Mar 2026 10:00:09 +0000

2026 is the year AI agents start doing operational work, and feed management is no exception. We’ve built the AI Feed Agent that can interpret your feed configuration, answer questions about rules and mappings, and help you diagnose and resolve issues faster. Why We Built an AI Feed Agent for Feed Management? Managing product feeds at scale is not just about exports. It is about understanding how data flows from source to mapping to channel, and being able to react quickly when something breaks.Most teams lose time switching between feeds, rules, Merchant Center diagnostics, and documentation. The goal of the Feedoptimise AI Feed Agent is to reduce that friction by giving you a single interface where you can ask questions, run diagnostics, and perform actions.In the video, we walk through exactly how that works. Asking Questions About Your Feeds A basic expectation from an agent is that it understands the platform and the current setup. Instead of opening multiple feeds, you can ask the agent to find specific rules across the system.You can see how the agent locates a rule inside a Google Shopping UK feed, explains the logic behind existing custom labels, and summarises how values are calculated. It can also show who changed a rule and when.The agent is not limited to explaining what already exists. It can suggest new rules, like stock-based segmentation, and guide you step by step through setting them up inside Feedoptimise. Diagnosing Feed and Channel Issues The agent can run a full feed audit and check whether the feed complies with Google product feed specifications. It highlights missing attributes, value distribution for fields like availability or gender, and quality indicators such as title length.From the audit summary, you can drill into detailed results for each analysed field and see exactly which items are affected.The diagnostics go beyond Feedoptimise itself. In the video, we connect a test Google Merchant Center account and pull real disapprovals. The agent groups issues by cause, explains why they are happening, and suggests what needs to change to fix them.You can also diagnose individual products. In the example shown, a product with three variants is analysed, and all errors and warnings are pulled for each variant.For issues like mismatched prices, the agent can even check website structured data to confirm what Google’s crawler is actually seeing. Performing Actions Through the Agent The final section of the video focuses on actions. The agent can help with simple operational tasks like blocking products from a feed. In the example, the most expensive dress in a Shopify store is blocked after a quick confirmation.It can also handle configuration tasks, such as setting import schedules. Instead of configuring this manually, the schedule is set through a prompt.After changes are made, the agent can force an immediate source or feed update so you do not have to wait for the next scheduled run. Get Access to the Feedoptimise AI Feed Agent The video shows the agent in action using real feeds and real scenarios. To explore it yourself, you can sign up for a free trial and get hands-on access to the Feedoptimise AI Feed Agent.This lets you ask questions about your own feeds, run diagnostics, and try agent-assisted actions directly inside the platform. What does the future hold? This is just the beginning. We started with a highly practical assistant that can walk you through setups, answer diagnostic questions, run audits, update feeds and schedules, and block items when needed. Next, we are expanding it so it can also create new feeds end to end, including channel templates, mappings, and initial rules, then safely edit and maintain those rules over time (not just explain them). We are also working on report analysis, so it can interpret feed and channel diagnostics over time, highlight trends and recurring issues, and recommend the highest-impact fixes based on what is changing in your catalog. On top of that, we are building support for overrides and supplemental feeds, so you can apply targeted attribute fixes or channel-specific adjustments without touching the source data. The agent will be able to create and connect these supplemental layers to the right primary feeds, keep the relationships clear, and validate the combined output before it is sent to each channel.Longer term, we want it to support full migrations, for example moving from another feed tool or rebuilding a complex setup across multiple channels, with checks, validation, and a clear change log so teams can review what changed and why. Stay tuned for the latest AI tools and features for feed management and optimisation.

Create structured product specifications and details for your Google Shopping feed using AI

Mon, 02 Feb 2026 09:24:55 +0000

High-quality product shopping feed includes a comprehensive set of attributes, including product details and specifications. This has become even more important following Google’s recent announcement about Google Shopping becoming more agentic. To be considered by semantic algorithms and AI agents, it’s important to provide as many relevant details as possible so they can answer questions like “How heavy is this item?” or “Show me all vegan sports shoes in size 38.” Often, this data already exists in your current product content or images, but it isn’t explicitly stated and needs to be inferred. Using Feedoptimise AI tools, product specs can be easily extracted and formatted in the way Google Shopping and other channels understand. To give you an idea of some of these details, we’ve listed the top 10 across six popular categories: Apparel (clothing) Material / fabric composition (%) Colour Size system (UK/EU/US) Fit (slim/regular/oversized) Key measurements (chest/waist/hip/inseam/length) Stretch level Pattern / print Closure type (zip/buttons/pull-on) Care instructions (wash/iron/tumble) Certifications (organic/recycled/OEKO-TEX) Footwear Size system (UK/EU/US) Upper material Sole material Lining/insole material Colour Heel height (or stack height) Closure type (laces/slip-on/buckle) Width fitting (narrow/standard/wide) Waterproof / water-resistant rating Grip / outsole type Consumer electronics Dimensions + weight Display (size/type/resolution/refresh rate) Processor / chipset RAM Storage capacity Battery life + battery capacity Charging (W) + port type Connectivity (Wi-Fi/BT/NFC/5G) Ports / I/O OS / compatibility Home & kitchen appliances Dimensions + weight Capacity (L/cups/basket size) Power (W) + voltage (V) Settings/programmes count Temperature range/control type Material (especially food-contact parts) Performance metric (suction/CADR/pressure etc.) Noise level (dB) What’s included (attachments/accessories) Warranty length Beauty & personal care Skin/hair type suitability Key ingredients (actives) Full ingredients list (INCI) Strength/concentration (where applicable) Shade/colour (if relevant) Finish/texture (matte, dewy, gel, cream) Scent/fragrance info Allergens/sensitivity notes Size/volume (ml/g) How to use (frequency + routine placement) Food & drink Ingredients list Allergens (contains/may contain) Nutrition (per 100g/ml and per serving) Serving size + servings per pack Net weight/volume Storage instructions (ambient/chilled/frozen) Dietary suitability (vegan/halal/gluten-free, if true) Country of origin Preparation/cooking instructions Use-by/best-before guidance (where applicable) How can Feedoptimise help enrich your feed with product details and specifications? Feedoptimise provides a complete set of AI tools that enable you to optimise titles, descriptions, categories, images, and other attributes, as well as extract additional data such as product specifications. We have dedicated tools specifically for this. Below, you can see it in action using the Product Details Extractor Modifier powered by OpenAI models. You can pass any attributes as input, and it will extract all relevant product detail specifications: The modifier above lets you extract specifications and details from your content and pass them to the Google Shopping feed, helping the agentic experience understand your data and reach more potential customers, increasing your return on investment. And of course this can be used for your other feeds too helping you to improve your overall feed quality and campaign performance.

Google Shopping is getting more agentic, and your feed is becoming its knowledge base

Fri, 16 Jan 2026 16:38:33 +0000

Google is rolling out a set of changes that shift product discovery from keyword matching to conversational, intent-driven experiences. AI will directly suggest your products to potential buyers. Two parts matter most for retailers and agencies managing Google Shopping at scale: Business Agent on Google Search Business Agent is a new conversational experience on Google Search where shoppers can “chat” with a brand, like a virtual store associate. Google positions it as an evolution of the earlier brand-profile Q&A experience, with more customisation and deeper data integration. It can use Merchant Center shopping data plus information from your website to answer questions, and Google notes additional capabilities like training the agent with business data and performance insights are coming. New Merchant Center data attributes designed for conversational commerce Google also announced dozens of new Merchant Center attributes aimed at improving discovery in conversational surfaces like AI Mode, Gemini, and Business Agent. These attributes “complement” existing feeds and go beyond traditional keywords, including things like answers to common product questions, compatible accessories, and substitutes. The practical takeaway is simple: if a shopper can ask it, you should be able to represent it in structured product data within your Google Shopping feed. Why this makes feeds more important (and more operational) Historically, Google Shopping feeds were a blend of compliance (meet spec, avoid disapprovals) and performance tuning (titles, product_type, labels, GTIN coverage, pricing accuracy). That still matters a lot, but conversational shopping adds a new requirement: The “answer” often needs to be constructed from product data, not inferred from a thin title. Eligibility and relevance will increasingly depend on attribute coverage, consistency, and specificity. Your feed becomes the system of record for what an AI assistant can safely and confidently say about your catalog. Google’s own Merchant Center documentation already frames correct formatting and completeness as essential, since inaccurate or missing product information can prevent ads and listings from showing. With agentic interfaces, the penalty for weak data is not only reduced reach, it is also weaker answers, fewer qualified clicks, and more “wrong product” conversations. What “conversational attributes” really mean in practice From Google’s announcement, the direction is clear: move beyond keywords and expand structured information that maps to natural questions: Product specifications (structured specs that typically live on PDPs) Q&A lists (common questions and answers that shoppers ask) Feature lists (bullet highlights, differentiators) Compatibility information (accessories, spare parts, substitutes) Additional descriptive facets like shapes, flavors, themes (category-dependent) This changes how you should think about feed optimisation. It is less “make the title longer” and more “make the product understandable to an assistant”. Examples of shopper questions your data should answer“Is this case compatible with iPhone 15 Pro Max?” “Does this sofa fit through a 76 cm doorway?” “Is this supplement vegan and gluten-free?” “What’s the difference between Model A and Model B?” “What spare filter fits this air purifier?” If you cannot represent those answers in clean attributes (or you represent them inconsistently), the assistant either will not show your item, or it will give a generic answer that might not convert that well. Feed readiness checklist for Business Agent and AI surfaces Here is a practical checklist you can use to assess your current Google Shopping feed and catalog data. 1) Get the basics right This is still non-negotiable because Merchant Center eligibility depends on it. IDs, prices, availability, links, image_link consistency GTIN/MPN/brand coverage where relevant category mapping (google_product_category), product_type hygiene shipping and tax configuration accuracy If these are unstable, any higher-order enrichment becomes fragile. 2) Treat PDP content as a data source, not just copy Most of the “conversational” value already exists somewhere on your site, but it is unstructured: specification tables bullet lists FAQs size guides compatibility lists manuals, ingredient lists, care instructions product images The task is to extract, normalise, and publish it into dedicated fields that can travel through Google Merchant Center and into AI experiences. 3) Build a compatibility layer (accessories, parts, substitutes) Google explicitly called out compatibility and substitutes as examples of the new attribute direction. For many verticals, compatibility is where conversion is won or lost: electronics (cases, chargers, mounts) appliances (filters, spare parts) automotive (fitment) fashion (styling, matching sets) beauty (shade equivalents, refills) Even a lightweight version helps: parent-child relationships, “works_with” groupings, or curated accessory mappings. 4) Create Q&A that matches real shopping language Business Agent can answer product questions in your brand’s voice and use Merchant Center plus website information. That implies you should: standardise “common questions” per category ensure answers are consistent with policy and PDP truth avoid ambiguous claims (especially regulated categories) 5) Put governance and QA around enrichment As soon as you start generating or extracting attributes at scale, you need controls: validation rules (allowed values, regex, length limits) confidence thresholds for extracted attributes audit trails for what changed and why sampling workflows (spot-check by category, brand, price band) monitoring for drift when PDP templates change Where Feedoptimise fits: enrichment, extraction, and feed operations Feedoptimise is built around the idea that feed management is ongoing data work, not a one-off export. On the platform side, that shows up in three capabilities that are directly relevant to Google’s agentic shift: 1) Scale feed modifications and channel-specific rules Feedoptimise supports bulk data modification using rules and formulas for converting, extracting, validating, merging from remote files, and more, with changes reviewable in real time. This is the foundation for building “conversational attribute” outputs that differ by category or market without rewriting your source catalog. 2) AI-powered attribute extraction and completion Feedoptimise explicitly supports AI-powered feed content generation and AI-assisted extraction to fill missing attributes (examples given include color, material, gender), alongside bulk generation and rewriting of titles, descriptions, categories, and other attributes. That’s the key unlock for conversational commerce: you can turn messy PDP text into structured fields at scale, without waiting for engineering to remodel your product database. You can also extract missing PDP information from product images, where it’s present. Practical use cases for the new Google attributes include: extracting spec pairs from description and spec tables into a normalized spec block extracting specs or missing informations from images using vision AI generating a clean feature list from long-form descriptions drafting Q&A answers grounded in your PDP and policy constraints deriving compatibility tags from model names and fitment notes inferring missing attributes (material, pattern, finish, use-case) to improve match quality 3) QA, reporting, and controlled experimentation Feedoptimise includes built-in quality assurance reporting and monitoring for feed attributes, plus the ability to A/B test data variants by toggling modifications. For conversational attributes, this matters because you will want to test questions like: Do generated feature lists improve CTR versus manufacturer bullets? Does stricter compatibility data reduce returns? Which spec formats lead to better visibility in AI surfaces? The new optimization baseline: “Can an assistant sell this item correctly?” Google is aligning Search, Shopping, and Gemini toward conversational discovery, and it is explicitly investing in Merchant Center attributes and Business Agent as the data and interaction layer. That raises the bar for feed quality. A good feed is no longer just compliant and keyword-rich. It is complete, structured, and answerable. If you want Feedoptimise to support this, the most valuable starting point is usually an enrichment audit focused on: which high-intent questions your products should answer which attributes you already have vs. can extract what validation and QA you need before scaling AI enrichment across the full catalog if you need any help, make sure to get in touch using our contact page.

What to Expect From the Best Feed Management Platforms in 2026

Wed, 07 Jan 2026 10:52:23 +0000

Feed management in 2026 is less about producing “a feed” and more about running a data product: you are continuously modifying attributes, enriching missing fields, validating quality, and connecting changes to performance outcomes. The winning tools tend to behave like workflow engines, not export utilities. This post is a practical checklist of capabilities to expect when you evaluate feed management and optimisation platforms in 2026. It is written so you can lift sections directly into vendor requirements, demo scripts, or an internal selection scorecard. 1) An in-platform Agent that can explain and execute feed operations In 2026, the most useful feed tools will include an Agent-style assistant that works inside the platform, understands your catalogue context, and helps teams move faster without turning everything into a support ticket. You should expect an Agent that can explain what’s happening, guide setup, and trigger the right actions when you need them. Feedoptimise includes an in-platform Agent designed to cover these day-to-day workflows, so teams can troubleshoot, set up, and maintain feed operations directly inside the product. What to look for: Explain mappings and transformations in plain language, including why an attribute looks wrong Assist with setup by proposing mappings, suggesting rule chains, and helping validate outputs Force updates and reruns safely (for example reprocessing rules, regenerating AI fields, refreshing exports) Answer operational questions like “is this SKU excluded”, “what changed since the last export”, “which products are missing required attributes” Recommend fixes based on common symptoms, such as disapprovals, missing fields, or underperforming segments Produce change summaries so teams can review what was updated before it goes live Run full audits across your feed mappings to flag potential issues (missing attributes, policy risks, formatting problems), explain how to address them, and suggest additional improvements. 2) A visual rule engine you can easily debug, not a black box The expectation in 2026 is a visual pipeline where you can chain modifications and understand the transformation path. Feedoptimise explicitly positions its Modifiers as “visual data flow pipelines” and highlights that you can trace modifications and work with an easy-to-follow data flow. What to look for: A clear “value lineage” for a given SKU, attribute by attribute The ability to reorder, disable, and isolate steps without breaking everything 3) Real-time previews and safe testing on real items You should not need to run a full export just to check whether a rule works. Real-time previews let you test changes across items before committing. Feedoptimise calls out “real-time previews” and testing rules across items in your catalog. What to look for: Item sampling, filtering, and quick validation on edge cases Clear diff views between “before” and “after” 4) First-class support for rich data structures Channels increasingly expect structured data, not just strings. A modern feed tool should let you work with lists and nested objects as naturally as any other field. Feedoptimise supports lists and nested objects which can be easily query and are easy to work with in its Modifiers UI. What to look for: UI support for arrays, nested objects, and multi-value attributes Mapping and transformation that does not require custom scripts 5) Multi-model AI support (choice is a feature) In 2026, “AI support” is not meaningful unless you can choose models based on task, cost, and quality. Feedoptimise provides support for multiple generative AI providers, including OpenAI ChatGPT, Google Gemini, Anthropic Claude, and xAI Grok. What to look for: Different models for different jobs (rewrite vs classify vs translate vs audit) A governance model for who can run what, and at what scale 6) Full prompt control and templates as part of the workflow A competitive platform treats prompts like reusable assets, not one-off experiments. Feedoptimise provides “full prompt control” with a visual WYSIWYG prompt template editor that can use feed attributes. What to look for: Prompt templates per category/brand/channel Versioning, approvals, and repeatability 7) Cost-aware AI at scale (caching and refresh logic) AI enrichment can get expensive fast. Tools should help you avoid re-running generations when nothing changed. Feedoptimise caches AI responses and only refreshes them when settings change. What to look for: Transparent “what will reprocess and why” Controls for batch size, scheduling, and prioritisation 8) Bulk attribute extraction from text and images Retailers still inherit incomplete supplier data. The best tools let you extract missing fields from product copy and images and apply the result in bulk. Feedoptimise provides extracting attributes/specs from text or images, filling gaps, and applying extraction to selected items or an entire catalog. What to look for: Structured outputs (fielded attributes), not just ordinary text Targeting options (by category, margin tier, availability, performance bands and more) 9) AI-driven feed quality audits that point to affected SKUs Quality auditing should identify problems, explain them, and list impacted products so the fix is actionable. Feedoptimise provides AI-powered data audits with semantic understanding, reasoning-style explanations, and audit reports that highlight affected product IDs. What to look for: Clear remediation guidance (not just “fail”) Repeatable audits so you can track improvements over time 10) Built-in A/B testing for feed content changes The point of optimisation is measurable improvement. In 2026, you should expect tools to support testing for titles, descriptions, categories, and other generated content. Feedoptimise provides support for scheduling A/B tests to identify top-performing versions. What to look for: Control vs variant handling and test duration controls Success metric selection (CTR, CVR, ROAS, revenue, margin-weighted ROAS) 11) Item-level reporting with custom metrics and formulas If reporting cannot connect feed changes to performance, you are optimising blind. Modern platforms should consolidate metrics and let you create new ones. Feedoptimise provides importing performance data from multiple platforms and creating new metrics using custom formulas. What to look for: Easy joins between product IDs and performance sources Calculated metrics you can use in rules, labels, and alerts Advanced time-range aggregation, including trend detection and correlation analysis between price fluctuations and performance metrics. 12) A “personal AI data analyst” layer for querying reports Teams want insight without needing a BI queue. Feedoptimise provides a “Personal AI Data Analyst” that uses natural language interaction to turn reports into actionable insights. What to look for: Traceability, what data was used, what time window, what definition Safe defaults that do not invent metrics that were not imported 13) Fair, transparent pricing where you only pay for what you use Pricing should be easy to understand and predictable in practice. The best feed management tools avoid opaque bundles and hidden overage fees, and instead make it clear what you’re paying for and why. That’s why Feedoptimise offers a pay-per-use pricing model based on parent product count (where applicable), rather than charging for variants. What to look for: Usage-based or clearly tiered pricing that maps to real value (catalog size, exports, enrichments, etc.) Transparent unit costs for add-ons (for example AI or image processing) No lengthy notice periods, lock-ins, or complicated cancellation terms, you can scale up or down as your needs change No charges for variants, since this can make your plan requirements very different compared with pricing calculated at the parent level. 14) Managed support included for migrations and ongoing optimisation A feed platform is rarely a clean-slate project. Most teams need to migrate from an existing feed tool, rebuild mappings, validate outputs, and re-establish performance baselines. In 2026, “support” should mean hands-on help, not just documentation links, and Feedoptimise does just that - already includes a managed service in every plan at no extra cost to support migrations, setup, and ongoing feed operations. What to look for: Assisted migrations (mapping rebuilds, channel setup, validation and QA) Help with edge cases like custom attributes, category logic, and multi-country feeds A support model that includes implementation guidance and ongoing troubleshooting, not only ticket handling 15) Change history, revision tracking, and easy rollbacks Feed optimisation is continuous, which means you need a reliable audit trail. The best feed management tools should make every change traceable and reversible across mappings, rules, AI enrichments, and exports, and Feedoptimise supports this with change history and rollback capabilities built into the platform. What to look for: A clear change log showing who changed what, when, and why (ideally with notes and links to affected entities) Versioned revisions for mappings, rule sets, templates, and prompts Diff views so you can compare revisions before publishing One-click rollback/revert to a previous known-good state Environment-style workflows (draft vs live), or at minimum a safe publish process to reduce accidental changes 16) A platform catalogue view to inspect any item across every feed, with custom filters and semantic search In 2026, you should not need to download a feed file just to diagnose one SKU. The best feed management tools include a platform-wide catalogue view where you can open an item and see how it resolves across each source and channel feed, including the final values after mappings, rules, and enrichments, and Feedoptimise provides this item-level visibility directly in the platform. What to look for: Item-level diagnostics across sources and individual feeds, so you can verify the final output per channel without exporting files Custom filters to quickly isolate problem sets (missing GTIN, policy flags, out of stock, high spend/low ROAS, brand/category segments, etc.) Fast search, ideally including semantic search (meaning-based queries, not only exact keyword matches), so you can find groups like “black running shoes under £100” and take bulk actions on the result set Inline overrides so you can fix or supplement attributes at item level when the source data cannot be changed quickly 17) Creative automation and templated imagery is now part of feed ops In 2026, creative operations are no longer separate from feed operations. If your image treatments and overlays live outside the feed workflow, you get slow iteration, inconsistent branding, and limited ability to test what actually moves performance. The better platforms treat imagery as another feed-driven asset, so you can template, enrich, validate, and deploy images in the same loop as titles, prices, and categorisation. Feedoptimise supports this approach through its built-in Image Editor, keeping creative changes tied to feed operations. What to look for: Templating built for catalogue scale, not one-off editing Dynamic attribute injection (for example price, discount, delivery messaging) Safe placeholder logic so overlays do not collide with the product subject Automated resizing/cropping rules that work across varied photography styles Controlled experimentation and scheduling, so you can A/B test creative variants without manual production cycles Predictable, usage-based cost controls for image processing at scale AI-assisted image enhancements, including upscaling, cleanup, and background transformations such as studio-to-lifestyle and lifestyle-to-studio, plus “reshoot-style” generation for consistent presentation across the catalogue What questions to ask every vendor in 2026 Ask the vendor to screen share and do each task live using the same set of SKUs, including a few edge cases (variants, missing attributes, disapprovals, multi-country). Your goal is to confirm the platform is fast to operate, safe to change, and measurable. Agent and operations Show me the in-platform Agent answering: “is this SKU being excluded?” and “What changed since the last export?” Can the Agent explain a mapping end to end (source field → modification steps → channel output)? Can the Agent propose a mapping/rule setup for a new channel, and what human review steps exist before publish? Demonstrate a forced rerun, for example reprocess rule changes and refresh outputs, and show what gets recomputed and why. Ask the Agent to run a full audit across feed mappings, flag potential issues (missing attributes, policy risks, formatting problems), explain how to address them, and suggest additional improvements. Platform catalogue, search, and filtering (no feed downloads) Show me a platform catalogue view for a single SKU, then show how it appears across each feed/channel output without downloading any files. Can I see final resolved values after mappings, rules, and enrichments for each destination? Demo custom filters for troubleshooting (missing GTIN, disapprovals, out of stock, high spend/low ROAS, brand/category segments). How does search work, do you support semantic search (meaning-based queries), or is it keyword-only? From a filtered or searched product set, can I trigger bulk actions (apply rule, run enrichment, force refresh, schedule a test)? Rules, previews, and structured data Trace one SKU through the full rule pipeline, and show where each attribute changed. Show real-time previews on a filtered subset (top sellers, clearance, out of stock). Demonstrate handling lists/nested objects (for example multiple images or multi-value attributes) without custom scripting. Change control, revision history, and rollbacks Where is the change log, and does it show who changed what, and when? Can I compare revisions (diff view) for mappings, rules, prompts, and templates? Demo a rollback to a previous version, and show what happens to live exports. Is there a draft vs live workflow, or a safe publish process with approvals? AI enrichment and governance Which models/providers can I choose from, and can I set model per task? Show prompt templates, versioning, and how attributes are injected into prompts. What caching exists, and what triggers regeneration? Demo bulk extraction from text and from images, and show structured output into fields. Quality and experimentation Show an audit report that lists affected product IDs and recommended fixes. Demo an A/B test for titles or descriptions, how variants are assigned, how success is measured, and how results are applied. Can experiments be scheduled and limited to segments (category, margin tier, performance tier)? Reporting and insight Show item-level performance, then create a custom metric with a formula (and use it to segment products). Demonstrate natural-language querying over your reporting, and show how the answer references underlying data definitions and time windows. Pricing and support Walk me through your pricing using our catalogue size and expected volumes, what exactly changes if we scale up or down? Are there long notice periods or lock-ins, and what does cancellation look like? What managed support is included for migration, mapping rebuilds, QA, and go-live, and what is considered out of scope? If you can’t find clear pricing on the website, ask why. The cost should map to workload and usage, not who you are as a brand. Creative and image operations Demo a template with dynamic overlays (for example price, discount, delivery messaging), and show safe zones/placeholders preventing overlap across different product images. Show automated resizing/cropping rules for multiple channels, and how failures fall back safely. Demo AI-assisted image enhancement workflows, including upscaling and cleanup, plus background transformations such as studio-to-lifestyle and lifestyle-to-studio, and reshoot-style generation for consistent catalogue presentation. If you support creative testing, show how you run and measure image variant experiments, including how variants are scheduled and rolled out. Where Feedoptimise fits (for readers comparing options) If you are mapping vendors to the 2026 expectations above, Feedoptimise is an example of a platform that covers the full surface area, including visual data flow Modifiers with real-time previews and rich structures, multi-model AI for content enrichment and extraction with prompt templates and caching, audit reporting with affected product IDs and A/B testing, item-level reporting with custom formulas and an AI data analyst, plus image templating with dynamic attributes and safe placeholders. It also includes an in-platform Agent to explain mappings, assist with setup, force updates/reruns, and answer operational questions, plus a platform catalogue view that lets you inspect each item across each feed without downloading outputs, using custom filters and semantic search to find and act on product subsets. Finally, it pairs the product with managed support included (for example migrations and mapping work), and fair, transparent pricing where you pay only for what you use without lengthy notice periods. In 2026, the best feed management tools reduce iteration time. They make changes safely, enrich data at scale, test improvements, and tie results back to item-level performance, ideally without forcing teams into separate systems for rules, AI, reporting, and creative. Finally, please keep in mind that these are just some of the features Feedoptimise offers, and there’s plenty more where this came from. If you’d like a demo or to discuss your use cases, please feel free to contact us.

Using Google Competitor Price Analysis Data To Label Products And Optimise Shopping Campaigns

Tue, 02 Dec 2025 11:01:54 +0000

Google Price Benchmark Report is one of the most actionable data sources available inside the Google Merchant Centre. It highlights how your product pricing compares to competitors and indicates the potential traffic uplift achievable if prices are adjusted. It's available for your popular or cross sold items and when this data is integrated directly into your product feed, it becomes a powerful lever for campaign structuring and bid management. Feedoptimise connects to Google Merchant Center via API and automatically pulls all relevant price benchmarks. You can utilise those metrics into dynamic labels that can be fed into your Shopping setup. Below is a breakdown of how these metrics can be utilised: 1. Labelling Products Based on Price Competitiveness Google provides benchmark price and benchmark price ratio values for each eligible product. These allow you to categorise items by how competitively they are priced. On top of that, Feedoptimise platform tracks these prices and their changes, enabling you to see the duration of your current competitiveness state - for instance, knowing you’ve been the cheapest, but only since yesterday. Common labelling structures include: a. Cheapest in the market Using benchmark ratio, items where your price is below the market average can be flagged. Typical label logic: Price ratio < 0.9 → “price_cheapest” Price ratio < 1.0 → “price_below_market” You can also apply duration restrictions to prevent overbidding on items that are consistently the cheapest. For example, you might choose to prioritize a product only if it has been the cheapest for less than two months. These labels help: Identify products likely to convert efficiently Allocate higher bids or include them in more aggressive campaign tiers Support margin-aware strategies by combining price data with stock or profit rules b. Not price competitive Products priced above benchmark can be labelled to allow for more cautious spend or placement in lower-priority campaigns: 1.0 ≤ ratio < 1.1 → “slightly_above_market” ratio ≥ 1.1 → “uncompetitive_price” This segmentation prevents overspending on products unlikely to win auctions due to price disadvantage. 2. Labelling Products by Potential Traffic Gain from Discounting Google also estimates how much additional traffic a product could receive if its price were reduced to match market levels. These “potential traffic uplift” values enable a more tactical pricing-driven bidding strategy. Feedoptimise can pull these uplift metrics and convert them into tiers such as: a. High potential uplift Products showing a significant increase in impression share or click volume if discounted Example: uplift > 50% → “high_uplift” These can be prioritised for promotional adjustments or included in campaigns focused on revenue expansion. b. Moderate potential uplift 20% < uplift ≤ 50% → “moderate_uplift” Useful for opportunistic discounting when margin permits. c. Low or negligible uplift uplift ≤ 20% → “low_uplift” Ideal to leave at current pricing or allocate to conservative bidding strategies. This approach helps retailers align pricing decisions with expected paid-search outcomes rather than relying on gut feeling or broad discounting. How Feedoptimise Enables Automation and Consistent Label Updates Feedoptimise integrates directly with Google Merchant Center using the official API. Through its reporting functionality the platform automatically retrieves: Benchmark price Benchmark price ratio Price competitiveness signals Potential traffic uplift estimates This data can be processed into dynamic feed attributes or custom labels. Labels update automatically whenever Google refreshes the benchmark values, so Shopping campaigns always operate with current market information. You can then use these labels in your bid strategies and campaign structure: Tiered campaigns based on competitiveness Custom label filters for Performance Max asset groups Rule-based bidding logic in external bid tools or Google Ads scripts Data-driven discounting workflows integrated with your feed Practical Campaign Applications Once the labels are in place, google shopping advertisers can apply them in several ways across their feeds: a. Priority-structured Shopping campaigns High-priority for competitive products Medium for neutral Low for uncompetitive b. Performance Max segmentation Separate asset groups or listing groups using price-based labels to control budgets and signals more precisely. c. Pricing optimisation loop Using uplift labelling to identify products where discounting would have the largest impact, then feeding these insights back into pricing tools or promotional planning. d. Smarter bidding With labels indicating competitiveness and traffic potential, manual or automated bidding can be tuned around real market opportunities instead of treating all items equally. Final Thoughts Integrating Google Price Benchmark Report data into your Google Shopping product feed provides a measurable advantage in campaign optimisation. Feedoptimise makes this process seamless by pulling benchmark metrics directly from Google Merchant Center and modifying them into actionable product labels. These labels allow retailers and digital marketers to align bids, discounting, and campaign prioritisation with real market competitiveness.

The Importance of Correct Product Structured Data Markup and Its Impact on Google Shopping Feeds

Sat, 01 Nov 2025 10:36:37 +0000

Structured data markup (such as schema.org and similar formats), plays a crucial role in helping Google Shopping and other platforms understand the content displayed on your product pages. While search and shopping bots can extract information from visible content, they might misinterpret what’s shown - for example, confusing a cross-sell product’s price for the main product price. Properly implemented structured data markup eliminates that ambiguity. When your product pages include accurate structured data, Google can confidently match your website content with the product data in your Merchant Center feed. It also enables Google to apply automatic item updates, ensuring your prices and availability stay consistent between your feed and your site. To make the most of this, it’s important to have your structured data configured correctly. Here’s what to check: Only the first static page load countsGooglebot doesn’t wait for JavaScript-rendered content. Any price or variant data that appears after the page loads dynamically is ignored. Make sure that the correct structured data is present in the initial HTML so that Google can read it reliably. Each variant has the correct ID and priceIf your product page displays multiple variants, ensure that each one has its own unique identifier ( sku ) and corresponding price within the structured data. This allows Google to match each variant with the correct item in your feed, avoiding mismatched or duplicate entries. The structured data price matches the preselected variantOn most product pages, a single variant (colour, size, etc.) is preselected. The structured data should represent the price of that variant - not of a different option or a range of prices. Consistency here helps Google correctly validate the data when it crawls your landing pages. Currency must matchIf you have a multi-currency setup, ensure that the currency defined in your structured data matches the one displayed on the page and the one specified in your product feed. Any mismatch can cause Google to flag or disapprove your listings due to inconsistent pricing information. What Happens When Structured Data Doesn’t Match? Incorrect or inconsistent structured data can lead to serious issues: Google might override your feed data with what it reads on the page source, even if that data is wrong. Products can get disapproved inside the Google Merchant Center due to price or availability mismatches. Automatic item updates may fail, leading to inaccurate listings or reduced visibility. Sale prices and promotions may be missed. If your structured data doesn’t correctly include sale or promotional pricing, Google may fail to recognise active discounts. This can cause your sale events - such as Black Friday or other limited-time offers - to be displayed at the regular price or missing sale badges, reducing visibility and conversion during key sales periods. If you can’t ensure your structured data accurately reflects what’s displayed on the page, it’s better to remove it altogether (but only as a last resort, if it truly can’t be corrected) as in that case, bots will rely solely on the visible content rather than conflicting structured data. How to Validate Your Structured Data? Feedoptimise includes a built-in validation mechanism that lets you preview and verify your structured data directly. Currently supported schemas include: Schema.org and Meta’s Open Graph Protocol (og:). This helps identify inconsistencies early and ensures your product pages and feed data remain aligned. You can also use the official Schema.org Markup Validator at validator.schema.org to check how your structured data is read by crawlers. Just enter a product URL or paste your code snippet to confirm that at least the key attributes such as product name, price, availability, and currency are correctly recognised. In summary: Accurate structured data helps web crawlers to better understand and trust your product information. It strengthens the connection between your website and your data feeds - reducing disapprovals, maintaining pricing accuracy, and improving your visibility across Google Shopping and various other channels.

How to Set Up Weather Prediction-Driven Feed Campaigns

Wed, 15 Oct 2025 11:21:14 +0000

Do you sell products that are sensitive to weather conditions - such as sunglasses, umbrellas, windproof clothing, or supplements and vitamins? Wouldn’t it be great if you could automate your product feed campaigns to boost certain items ahead of weather changes, helping you reach the right intent at the right time - before your competitors do? If so, you’re in the right place. Below, we’ll show you how to do this easily using Feedoptimise platform and the Weather Prediction Modifier. We’ll demonstrate the setup using two examples: sunglasses and raincoats, and cough syrup. In this example, we’ll use the UK as a reference (averaging weather between London and Manchester). Of course, you can configure your own settings as needed. The weather-based campaign setup (whether Manual Shopping, PMax, Meta, or any other platform that supports custom labels) will only activate for products labeled as push-more-weather. If there are no items in the feed with that label, the campaign will remain idle. This ensures it only runs when the specified weather conditions are met - otherwise, your standard campaigns continue to handle traffic as usual. Example 1: Boost sunglasses when it’s sunny or raincoats when it’s raining To achieve this, we’ll use the cloud coverage metric for the next 7 days. With that data, we can create a custom label based on conditions such as: If the cloud coverage is below 5% over the next 7 days and the item category contains “sunglasses,” return the push-more-weather label. This triggers the weather-based campaign to activate for those products: We can apply the opposite logic for raincoats, but instead of using cloud coverage, we’ll use the rainfall metric (e.g., more than 50mm of rainfall expected in the next 7 days). This ensures the campaign activates only when actual rain is predicted, not just cloudy conditions. Example 2: Boost cough syrups when temperatures are dropping In this case, you’ll need two metrics: The current average temperature The predicted average temperature for the next 7 days Then calculate the temperature change: (Average temperature over the next 7 days) - (Current average temperature) = Temperature difference If the temperature is expected to drop by more than 5°C, you can set the condition so that the push-more-weather label applies only to cough syrups. This will automatically trigger your campaign to promote those products when colder conditions are expected. Summary With the setup above (or a similar one), you can create automated, weather-driven campaigns across any platform that supports custom labels - including Google Shopping, Meta, Bing, Pinterest, TikTok, and more. Feedoptimise provides all the tools you need to execute weather-based strategies efficiently, no matter how complex your setup may be.

List Your Products in OpenAI ChatGPT Using a Product Feed

Tue, 30 Sep 2025 15:18:35 +0000

It’s official - in the US (and soon in other countries), you can now list your products with OpenAI ChatGPT using a product feed. And yes, we support it from day one! What this means: Your products can now: Appear in ChatGPT search results Be purchased directly inside ChatGPT How to get started: To get your products featured in ChatGPT search results and enable direct purchases inside ChatGPT, you’ll need to: Sign up as a Merchant Provide the feed in one of the supported formats: CSV, TSV, XML, or JSON via a URL. Feed structure: We’ve prepared ready-to-use feed templates for all formats, complete with initial mappings - so you can get started fast. The feed contains standard product attributes with names as seen inside Google shopping specs, plus two custom/OpenAI-specific flags: enable_search – Controls whether the product can appear in ChatGPT search results enable_checkout – Allows direct purchase inside ChatGPT Please see here for the official OpenAI feed specification. Why it matters: Feed management now has a new channel to consider: ChatGPT. Unlike traditional platforms, it is more semantically aware, which means it interprets and surfaces products based on how well they’re described. Because of this, clear and accurate product information is more important than ever. The more detailed your items are, the easier it is for ChatGPT to present them to the right audience. Our AI Feed Optimisation tools can help refine your feed so your products are represented accurately in ChatGPT results - leading to better performance and broader reach. If you have questions or need guidance, please contact us.

10 Ideas to Enrich and Scale AI Product Image Edits in Your Feeds using Feedoptimise & Google Gemini aka Nano Banana

Mon, 01 Sep 2025 13:41:14 +0000

In this guide, we will demonstrate 10 ideas for bulk AI image optimisations using our platform and Google Gemini: colour variants, new angles, lifestyle ↔ studio swaps, upscaling, smart crops, dynamic sale tags, subtle realism boosts, “add to look,” and full Shop-the-Look sets; everything powered by Feedoptimise + Google Gemini aka Nano Banana and saved/hosted directly under your own Google Cloud Storage account. Why this matters Great images convert better. But reshoots are slow, expensive, and impossible to scale across thousands of SKUs. With Feedoptimise Gemini Prompt Image Editor Modifier, you can generate consistent, on-brand product imagery from a single base photo, perfect for PDPs, ads, and social, without booking a studio. This post guides you through the exact workflow we demonstrate in the video, along with the 10 edits we rely on most for real e-commerce data pipelines. Prerequisites (quick) Feedoptimise account Gemini API key Google Cloud Storage (GCS) bucket + access keys Recommended setup: Provide your original image as a fallback (in case a run fails) Provide your base image for the actual edit Select your GCS bucket and set the output path to AUTO to mirror your input folder structure (easy to find & reuse) Alternatively, define a custom path pattern (e.g., by SKU or item ID) for versioning The 10 AI edits (that actually ship) 1) Colour variants from one image If you sell multiple colour options but only have one photo, generate the missing variants in minutes.Good for: Feed completion, PDP consistency, A/B image tests.Example prompt: Change the item color to red. 2) New angles without extra photography No back or side shots? Ask for a rotated view.Good for: Back-of-product views, detail callouts, “360° feel” without full spin sets.Example prompt: Rotate the item to show the back. 3) Studio → Lifestyle Make plain studio shots feel aspirational and real—great for ad creatives and hero images.Good for: Paid social, newsletters, campaign landing pages.Example prompt: Place this product in a realistic tropical beach lifestyle scene. 4) Lifestyle → Studio Need a clean, background-free image inside your feed? Convert lifestyle back to studio.Good for: PDPs, marketplaces, comparison pages.Example prompt: Convert to a studio image (no model), show the front of the item, neutral background. 5) Upscale tiny images Rescue small legacy photos. Upscale while preserving realism.Good for: Feeds with legacy images, supplier images, and marketplace compliance.Example prompt: Upscale this image. 6) Smart crop for product-first compositions Centres the subject. Remove distractions. Export the aspect ratio you need.Good for: Square PDP thumbnails, ad placements, email modules.Example prompt: Crop to focus solely on the main furniture item. Remove other elements, center the item, zoom to fill, output 1:1. Tip: If legs/edges get trimmed, add: “Ensure the full item is visible; do not crop off edges.” 7) Dynamic sale tag (auto-price from attributes) Overlay a promo badge and pull the price from your product data.Good for: Seasonal promos, flash sales, automated campaign variants.Example prompt (with attribute): Add a prominent sale tag with price: ${{Price}}. 8) Subtle realism & polish Minor changes, major perceived quality: contact shadows, soft drops, and controlled reflections, while maintaining the same perspective.Good for: Achieving a premium feel without altering the product identity.Example prompt: Add physically plausible contact shadows, soft drop shadows, and glossy table reflections. Keep the original perspective. 9) Add a product to a model look Blend a bag, hat, or shoes into an existing model photo convincingly.Good for: Cross-sell, “complete the look,” editorial banners.Example prompt: Add this item to the model’s look in a natural way. 10) Shop-the-Look (multiple items, same person) Dress the same model with several items, while preserving identity and consistency.Good for: Outfits, bundles, lookbooks.Example prompt: Edit the same model by adding all provided items. Preserve the person’s identity: same face, features, expression, hair, body type, and skin tone. Only change the wardrobe naturally. Cost & observability Feedoptimise displays token usage and an estimated cost for every call. In typical runs, images cost a few cents each; however, your price will vary depending on the image, prompt length, and output size. Monitor the per-call readout to budget effectively at scale. Folder structure that scales AUTO path: Mirrors your input file tree—fast and tidy for most teams. Custom path: Version by SKU/Variant/Date or SKU/Channel to keep marketplaces and campaigns separate. Fallback originals: Keep the unedited image available for rollbacks and QA. QA checklist before publishing Product shape, proportions, and critical details (logos, stitching, texture) intact Correct colour and finish (e.g., matte vs glossy) Clean edges after crops—no parts cut off unless intended Aspect ratio and file size match your channel specs Promo tags use the right price and currency for the market Feedoptimise + Google Gemini compresses weeks of product photography into minutes. From a single base image, you can produce on-brand variants, new angles, lifestyle/studio versions, smart crops, and upscales, saved automatically to GCS and ready for PDPs, ads, and marketplaces. Transparent per-image costs and reusable presets enable teams to scale creative efforts without additional headcount, accelerating launches and boosting CTR and conversion rates while reducing the need for reshoots.

How to set up localised country feeds for your Meta catalogue

Fri, 22 Aug 2025 17:06:18 +0000

Looking for a way to handle international product details automatically (with GeoIP) inside your Meta catalogue? Localised Meta feeds are your answer. A country or language feed is an extra data feed that you add on top of your primary feed. These local feeds hold translated and country-specific versions of your product data, including currency, pricing, and landing page preferences. When your catalogue includes this information, Meta can automatically show the correct version in your ads or shop for each user. For example, people in the US see English copy and USD pricing, while people in France see French copy and EUR pricing. Which feed do I need, and when? Below is the difference between the feed types you can provide to localise your listings. Primary feedFollows Meta or Google Shopping specs and includes full product details with your default currency and language. If Meta can’t find a localised option for an item, it will fail over to the primary feed. It’s required at all times - your main/default data source for a working catalogue. Country feedLets you provide currency-specific information for a given country (e.g., price, sale_price, and the landing page URL that shows the correct currency/price). You can include more than one country per item in this feed.Optional with respect to the catalogue’s primary data; however, it’s required for multi-currency support. Language feedLets you provide translations (e.g., localised title, description, etc.) so, for example, French-speaking audiences see French copy instead of your primary language. You can include more than one country–language combination per item here as well.Optional with respect to the catalogue’s primary data; however, it’s required for multi-language support. Set-up essentials Match item IDs across every feed.The item IDs in your Country and Language feeds must exactly match the IDs in your Primary feed. Include all items from your Primary feed.Even if a product is out of stock or unavailable in certain countries, still list it in the local feeds with out of stock status. If an item is missing from a local feed, Meta will fall back to your primary data for that item. Get those IDs aligned, cover each item in your local feeds, and you’ll have a clean, reliable localisation setup that shows the right language, currency, and URL to every user.

Grok 4 Meets Feedoptimise: 10 Ideas using AI to Optimise Product Data Feeds

Mon, 21 Jul 2025 12:01:38 +0000

If you spend your days tweaking product titles, checking Google Shopping disapprovals or wondering why last week's hero SKU suddenly dropped, you already know the chores of feed optimisation. What happens when you hand those tasks to a multimodal large‑language model? In a new YouTube walkthrough, Feedoptimise puts xAI's Grok 4 through 10 real‑world tasks. The result isn't just another AI demo; it's a blueprint any merchant or performance marketer can use today. Below is the play-by-play, along with a few key takeaways on where Grok-grade models fit into the growing stack of retail media tooling. 1. Title Optimisation for Maximum Visibility Product titles are the first hook for shoppers, but they're often bland or keyword-deficient. Using Grok 4's Title Optimiser modifier in Feedoptimise, you can feed in attributes like gender, description, category, size, color, and brand. The AI restructures the title to incorporate more relevant keywords, making it more appealing for search algorithms. For instance, a basic title might evolve into something richer and more SEO-friendly, boosting click-through rates on Google Shopping. 2. Auditing Titles with Precision Scoring Once optimised, how do you measure success? The video introduces a Grok Prompt modifier to audit titles against key criteria, including keywords, eye-catching appeal, relevance, and an overall score. By pasting a custom prompt that requests a flat JSON output with scores, merchants get quantifiable insights. Then we can use Analysers to track the history of scoring our changes. 3. Richer Product Types Google’s product_type field is heavily underused. A two‑word string like “Women’s pants” barely nudges the algorithm. Grok 4 steps in to expand them using prompts that draw from title, description, and category attributes. The result? A keyword-laden product type optimized for Google Shopping, such as one improved with terms related to style, material, and seasonality. This subtle tweak can improve categorization and discovery. 4. Seamless Translations for Global Reach Expanding to international markets? AI translations are fast and reliable. A straightforward prompt translates descriptions to languages like German, returning only the cleaned-up text. In an era of borderless e-commerce, this feature democratises global selling without the need for costly human translators. 5. Extracting Attributes from Images Text data has limits, but images tell a fuller story. Grok 4's image analysis shines here: Provide a product image URL in a prompt, and it extracts Google Shopping attributes like age group, brand, color, gender, material, product type, and size, outputting them as JSON. The demo accurately identifies even subtle details, such as branding, demonstrating AI's prowess in visual data mining. This is particularly useful for feeds with incomplete text attributes, filling gaps to meet platform requirements. 6. Picking the Best Creative for Social Ads Not all product images are created equal, especially for social ads. With multiple image URLs, Grok 4 can evaluate and select the best one based on context, choosing the most engaging lifestyle shot. This automation could revolutionize ad creative workflows, ensuring visuals align with Facebook or Instagram campaign goals without manual review. 7. Automated Policy Compliance Google Shopping disapprovals often stem from non-compliant language, such as misleading health claims. AI acts as a policy checker: Input a description, and it scans for issues (e.g., under "Healthcare and medicine" guidelines), then rewrites it by removing or rephrasing risky terms. The video demonstrates how this preserves essential product information while making the text safe, preventing feed rejections, and saving merchants from costly downtime. 8. Extracting Review‑Driven Keywords Product reviews are goldmines for insights. Using AI, you can prompt it to search for reviews based on a product's title and GTIN, then compile a comma-separated list of positive keywords (e.g., what buyers love about comfort or durability). These can fuel ad copy or title rebuilds, aligning marketing with genuine customer sentiment. It's a smart way to turn user-generated content into actionable data. 9. Forecasting Performance with Data Analysis Why guess when AI can predict? Feed Grok 4 a 7-day performance report (including metrics like CTR and conversions), and it calculates a potential score for the next week. We suggest using this in custom labels to prioritize high-potential items in campaigns. While not a crystal ball, this AI-assisted forecasting automates what was once manual analysis, helping allocate budgets more effectively. 10. Near‑Real‑Time Competitor Pricing The most ambitious idea: Enable live web search in Grok 4 to scout US market offers for a product title, returning a list of the top 5-10 deals. You can then compare their prices via rules in Feedoptimise. This experimental approach highlights AI's potential for real-time competitive intelligence. The Bigger Picture: AI Future is here! What truly sets Feedoptimise apart is its deep integration with AI models, making it the only platform on the market offering such advanced AI capabilities tailored specifically for product feed optimization. This unique combination enables merchants to leverage cutting-edge AI for a wide range of applications, from automated audits to image analysis, surpassing the generic tools commonly found in competitors. As e-commerce increasingly relies on smart data strategies, Feedoptimise's pioneering approach delivers unmatched efficiency, compliance, and performance insights, positioning it as an essential tool for forward-thinking retailers. If you're in the space, diving into this integration could transform your operations and give you a real edge in a crowded market.

AI-Powered Feed Optimisation: How to Stay Ahead in Google Shopping and Meta Ads in 2025

Tue, 08 Jul 2025 11:50:59 +0000

Google Shopping and Meta Ads continue to evolve rapidly in 2025. With Performance Max campaigns, dynamic audience signals, and AI-driven ad platforms taking centre stage, feed quality and structure are now more critical than ever. Merchants who rely solely on traditional static feed management are at risk of losing visibility and ROAS while competitors using advanced optimisation techniques continue to scale. This article explores how AI-powered feed optimisation can help your store or agency stay ahead, ensuring your feeds drive measurable performance across Google Shopping and Meta Ads in 2025. Why Traditional Feed Optimisation Is No Longer Enough Historically, merchants would set up their product feeds with basic attributes: titles, descriptions, images, and GTINs. Occasional updates to pricing and availability were sufficient to maintain a steady presence. Today, platforms like Google and Meta expect more: Dynamic attribute testing to match real-time search trends. Audience signal alignment within feed structures to maximise relevance. Enhanced creative assets in feeds for higher CTR in PMax and Meta Advantage+. Feeds are no longer static data containers; they are dynamic data drivers that directly impact your campaign efficiency, CPCs, and ROAS. AI-Powered Strategies for Feed Optimisation Here’s how AI-powered feed optimisation can give your campaigns a competitive edge: Title Optimisation and Testing Using AIAI models can generate variations of your product titles, focusing on high-intent keywords dynamically aligned with trending searches. For example, adding seasonality, best-selling keywords, and long-tail variations can improve your visibility in Shopping and Meta feeds. Image Enhancement and Variant TestingAI can automatically enhance images, remove backgrounds, and A/B test product image variants to identify which visuals drive higher CTR and conversions. Product Type Optimisation Using AIAI can analyse your product catalogue and automatically generate or refine your 'product type' attributes to match structured taxonomy and trending search categories. By aligning product types with how users search and how Google and Meta categorise products, you can improve relevance, feed quality scores, and campaign segmentation without manual guesswork. This ensures that your campaigns are structured efficiently and your products are discoverable in relevant queries, driving higher quality traffic to your store. Extracting Missing Attributes Using AIAI can scan your product titles, descriptions, and images to identify and extract missing attributes such as colour, material, size, and gender. By automatically enriching your feed with these attributes, you enhance the quality of your feed, campaign targeting, and filtering capabilities across Google Shopping and Meta platforms. This process reduces manual data entry while ensuring your products appear in more relevant searches, enhancing discoverability and improving conversion rates. Google Categories Mapping Using AIAI can automatically map your products to the most accurate Google Product Categories by analysing your titles, descriptions, and product attributes, ensuring alignment with Google’s taxonomy, which is also used by Meta for product categorisation. Automating category mapping with AI saves time, reduces errors, and ensures your feed has complete and accurate category data, improving feed quality and campaign eligibility across both Google Shopping and Meta platforms. Practical Implementation for Merchants and Agencies You don’t need a full in-house data science team to leverage AI-powered feed optimisation: Utilise tools (like Feedoptimise) that allow rule-based dynamic attribute testing combined with AI insights and generations. Regularly audit your feed performance and A/B test title and image variants. Sync feed strategies with your Performance Max and Meta campaigns to maximise the signal quality and relevance. Feeds are the new creative layer for Shopping campaigns and should be treated as an active testing and optimisation area within your paid media strategy. How Feedoptimise Helps You Stay Ahead At Feedoptimise, you can integrate AI-powered optimisation and rule-based automation to ensure your product feeds remain competitive, structured, and aligned with the latest trends. From title testing and dynamic price updates to seasonal keyword alignment and content writing and optimisation using AI, our platform helps you transform your feed into a performance engine that maximises your ROAS across Google and Meta. Final Thoughts AI-powered feed optimisation is not a future concept; it’s your competitive advantage in 2025. As Google Shopping and Meta Ads become increasingly automated, your product feed remains one of the few controllable levers you can use to improve campaign performance. Start testing, automating, and enhancing your feeds now to capture higher-quality traffic and conversions before your competitors do. Need help implementing AI-powered feed optimisation? Contact Feedoptimise today.

Introducing Revision History: Changes Tracking, Versioning and Recovery

Tue, 24 Jun 2025 09:16:43 +0000

Have you ever wondered who changed what and when in your product feeds or sources? Or wished you could simply roll back a change that didn’t quite go to plan? We get it, and we’ve got your back. That’s why we’re excited to introduce a brand new feature in Feedoptimise: Revision History. This powerful update brings full change versioning, activity tracking, and snapshot recovery right into our platform, so you never lose track of your feed changes again. Keep Track of Every Change With Revision History, you can now easily see: Who made a change What was changed When the change happened Whether you’re part of a large team or managing feeds solo, having full visibility into your edit history removes the guesswork and makes collaboration more transparent. Roll Back with Confidence Mistakes happen. A feed rule goes wrong, a source is accidentally deleted, or something is updated that shouldn’t have been. No stress - you can now restore any feed or source to a previous point in time. Feedoptimise automatically stores a history of all changes (including deletions), so you can confidently experiment without fear of losing your work. We call these saved points snapshots, and they make recovery as simple as a few clicks. How to Access Your History To view the history of a particular source or feed: Open the feed or source you’re working with. Navigate to the History tab. Browse through the list of changes or restore to an earlier version instantly. And if you've accidentally removed an entire feed or source, don’t worry - you can restore deleted items too: Head over to your list of feeds or sources. Click the delete icon (🗑) to view recently removed items. Select the one you want to bring back, and just like that, it’s recovered. Designed for Peace of Mind We built Revision History to give you peace of mind. You’ll always know that your work is safe, traceable, and reversible. This new level of control not only boosts your efficiency but also builds trust in how your feeds are managed over time. Whether you're fine-tuning a complex shopping feed or collaborating with multiple stakeholders, knowing you can undo and track changes lets you work smarter. Try Revision History today and experience a more secure, transparent way to manage your product data. Have questions or feedback? We’d love to hear from you. Drop us a message and let us know what you think!

OpenAI's GPT-4.1 API-only Models Now Supported within Our OpenAI Modifiers

Tue, 15 Apr 2025 07:22:05 +0000

OpenAI unveiled its latest GPT-4.1 series: GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano yesterday, and we have already integrated all those models seamlessly into our platform's OpenAI modifiers so that you can take advantage of them straight away. This release delivers substantial improvements in coding, instruction following, and long-context processing, offering unparalleled cost efficiency and speed, establishing it as the most cost-effective and high-performing model available exclusively via the OpenAI API. Summary of the enhancements: Superior Coding CapabilitiesGPT-4.1 excels in real-world coding tasks, scoring 54.6% on SWE-bench Verified, a 21.4% improvement over GPT-4o. It handles complex software engineering challenges, produces reliable code diffs, and minimizes extraneous edits (down to 2% from 9%). For front-end development, it creates more functional and visually appealing web apps, preferred 80% of the time over GPT-4o. Enhanced Instruction FollowingThe series shines in following complex instructions, achieving 38.3% on Scale’s MultiChallenge benchmark (10.5% better than GPT-4o) and 87.4% on IFEval. This reliability powers robust agentic systems, enabling tasks like automated customer support or data extraction with minimal errors. Our modifiers make it easy to customise these capabilities for specific use cases, ensuring consistent outputs. Massive Context WindowAll GPT-4.1 models support a 1-million-token context window - equivalent to eight React codebases - ideal for processing large documents or code repositories. They excel at retrieving and reasoning across long contexts, scoring 72.0% on Video-MME for long video understanding and maintaining accuracy in multi-hop tasks like Graphwalks (61.7%). Our platform’s modifiers allow users to tap into this for legal analysis or codebase navigation applications. Cost efficiency and speed- GPT-4.1: Priced at $2.00 per 1M input tokens and $8.00 per 1M output tokens, it’s 26% cheaper than GPT-4o for median queries. With 75% prompt caching discounts, costs drop further, making it economical for high-volume tasks.- GPT-4.1 mini: At $0.40 per 1M input tokens, it cuts costs by 83% compared to GPT-4o while halving latency, matching or surpassing GPT-4o in intelligence evals. Perfect for low-latency needs like real-time analytics.- GPT-4.1 nano: The fastest and cheapest at $0.10 per 1M input tokens, it delivers exceptional performance for lightweight tasks like classification, with first-token latency under five seconds for 128,000 tokens. By supporting GPT-4.1 models in our modifiers, we empower users to build sophisticated, cost-effective AI solutions tailored to their needs. Whether automating code reviews, processing vast datasets, or creating responsive agents, these models deliver top-tier performance with lower costs and faster responses. The integration ensures flexibility, letting users fine-tune outputs as easily as tweaking a formula, unlocking new possibilities for innovation. OpenAI’s GPT-4.1 series, now supercharged by our platform, sets a new standard for AI-driven productivity. Explore these models today and transform your workflows with precision and efficiency.

How to Remove Watermarks from Product Images to Fix "Text on Image" Disapproval in Google Merchant Center

Thu, 27 Mar 2025 17:06:32 +0000

Struggling with the frustrating "Text on Image" disapproval issue in Google Merchant Center (GMC)? Thankfully, you can quickly resolve this using our powerful Gemini Watermark Removal modifier in the Feedoptime platform. This easy-to-use tool efficiently removes unwanted watermarks, logos, and text from your product images, helping your items comply with GMC guidelines and other marketing channel requirements. How Does the Gemini Watermark Removal Modifier Work? The Gemini watermark removal modifier automates the entire process: Download: Automatically retrieves your original product image using the provided URL. Process: Connects to the Gemini API to accurately remove watermarks and any unwanted text. Upload: Stores the cleaned image in your designated Google Cloud Storage bucket. Generate Link: Returns a new, watermark-free image URL that you can immediately use in your product feed. Step-by-Step Guide to Removing Watermarks from Your Images: 1. Create a Gemini API Key: Go to Google AI Studio and generate a Gemini API key. 2. Create a Google Cloud Service Key: Access your Google Cloud Console and generate a service key with access to Google storage. 3. Provide Your Gemini API Key in the Modifier: Enter your Gemini API key directly into the Gemini Watermark Removal Modifier. 4. Enter Your Google Cloud Storage Service Key: Enter your JSON key file in the modifier. 5. Select Bucket and Path (Optional): Specify your preferred bucket and path within Google Cloud Storage, or leave it default. 6. Run the Modifier: Execute the Gemini watermark removal modifier to quickly generate a clean, compliant product image. By using this straightforward solution, you'll easily eliminate GMC "Text on Image" disapprovals, improving your product feed quality, ad visibility, and ultimately, your sales performance.

How to set up Automotive/Vehicle ads feed for Google, Meta and TikTok

Wed, 12 Mar 2025 17:36:29 +0000

More and more marketing and advertising channels are opening up to new ad formats and placements tailored for car dealerships and manufacturers, enabling them to promote their vehicles and reach new potential customers. Who is this feed format best suited for? This format is designed with car sellers in mind and applies to both new and used cars. Is the feed specification different from the usual retail catalogues? Feed specifications for Vehicle ads are slightly different from regular e-commerce catalogues due to the need for additional data points such as make, model, year, mileage driven, body style, and dealership addresses, to name just a few. Is it easy to set? Thanks to the robustness and flexibility of the Feedoptimise platform, creating feeds is a very simple process. We already offer ready-to-use templates that meet the specifications and requirements for automotive ad feeds across Google Vehicle Ads, Meta Automotive Inventory Ads, and TikTok Automotive Ads. Our platform can import all product details to create relevant feeds and update them using various integration methods, such as APIs and preexisting inventory files. Alternatively, we can crawl your website to collect all car details and keep them up to date directly from your site. We can also extract attributes from various sources and isolate them as standalone data points. For example, details like year or mileage can be pulled from descriptions if not already available independently. Additionally, your dealership’s geolocation (latitude and longitude), which Meta and TikTok require, can be inferred by our platform from your postcode. Is that available across all countries? At the time of writing this post, Google Vehicle ads are in open beta in the US, Canada, United Kingdom and Australia; Meta Automotive Inventory Ads are available worldwide, and TikTok Automotive Ads are available globally. How can I set up the Vehicle Ads feed? To get started, you simply need to create an account under the Feedoptimise app. Once set up, you can connect your source and generate a Vehicle Ads feed from it. Alternatively, after signing up and creating your account, you can request our team to handle everything for you. Feel free to get in touch with us if you have any questions.

Create deeply nested and highly relevant Google shopping product types using AI

Tue, 18 Feb 2025 10:03:49 +0000

The product_type attribute is used to pass the product categorisation inside the Google Shopping feed. It helps Google determine the type of the item and enriches your data quality with extra keywords. Now, if your categorisation is already deep and has nice keywords, you can most likely use it as is. However, if it has only one or two levels and you think it could be improved, then you are looking in the right place. We have just introduced a new AI modifier, AI Product Type Creator, which allows you to create deep, nested, and highly relevant product types from any content. Currently, it's based on the Qwen 2.5 open model from Alibaba, see a few examples in action below: Based on the title alone: Based on the title and description, with a nesting limit set to 10 levels We hope that this and our other AI feed optimisation tools will help you improve your feed optimisation and management tasks.

How To Fix Black Background Image Transparency and Unsupported Image Type Issues in Google Merchant Center

Fri, 24 Jan 2025 13:29:55 +0000

This article will help address two common issues related to images and their processing by Google Merchant Center. Wrong Transparency to Color Conversion in Google Shopping The very first issue we would like to focus on is how Google treats transparency in some cases. From account to account, you might notice black background images appearing in places where store imagery relies on a transparent background. We are unsure why that happens since it might not always affect all items. For example, our image editor can correctly handle affected items as a solution proxy. We can only assume that some signal in the image's binary code confuses the Google system into applying a black background. How to fix: Make sure to use solid background-color images. For example, you could convert the background to white or another color that works well with your website graphic. Use image editing tools that can reapply the background using solid colors, like our Image Editor, allowing you to handle the conversion of the transparency directly on our side and providing Google with solid background images instead. Unsupported Image Type This issue can occur if your image format, its metadata, or image-hosting server headers are not aligned or are sending confusing or corrupted data. To begin with, ensure your images are in one of the supported image formats per Google's requirements. JPEG, PNG, WebP, BMP, TIFF, or GIF. If it is and you still get that error, you could try fixing it using one of the two following methods: How to fix: Assuming it's not a Google bug, you likely have some data corruption in your image’s source, and you might be wondering why Google Merchant Center can’t see your image correctly if your browser does. This is because browsers can ignore some of these types of issues, which is why it can be displayed in your browser. However, Google bots can’t parse it, or it’s simply a bug on Google's end affecting only specific images under specific circumstances. If you would like to fix it on your end instead of using the method below, we recommend regenerating the image or even converting it to a different format to try and fix whatever Google bots deem broken. Also, make sure your server response headers don't have issues, e.g., suggesting the wrong image size. This is by far the easiest and most scalable option: use our Image Editor, which can deal with most image corruption issues and response header issues. It serves as a proxy between your image and Google bots and fixing it in the middle. We hope this article helps solve the issues, and if you have any questions, please get in touch with us via our contact form.

Optimise Your Product Feeds Content with Free AI open models

Tue, 07 Jan 2025 13:56:27 +0000

As you may already know, AI is revolutionising various industries. Our platform already offers many commercial models, such as ChatGPT, Gemini, and Anthropic, that you can use to optimise your data feeds with AI.Today, however, we are pleased to add support for our first open model, which we host directly on our servers. It is free to use and included in all our plans. Your data doesn't leave our servers, providing 100% privacy. The first open model we are now supporting and are hosting directly under our platform is: Alibaba Qwen 2.5 - https://github.com/QwenLM/Qwen2.5 As AI is going to be (and in some respect already is) a primary force behind campaign optimisations, we decided to accelerate the adoption curve by hosting and providing some open models for free to our entire customer base. Now, we provide three new modifiers powered by this model: AI Title Optimiser It enables you to create new or optimise existing titles by simply providing attributes an AI should learn more about your item from. AI Descriptions CreatorAs with the title optimisation, it enables you to create new or optimise existing descriptions by simply providing attributes an AI should use. AI Attributes ExtractorSimilarly to the two above, this modifier works against whatever text you would provide to it at the input; for example, it extracts the attributes from the description alone. If you have any questions, please contact us using our contact form or our ticketing platform. Otherwise, we hope you will find that those three new modifiers improve the performance of your various contextual campaigns (Google Shopping, Bing …, etc.) without much effort.And when new open models are released, we will, of course, update our modifiers to use the best-performing ones we can host on our end and continue providing free of charge to all our customers.

Optimise Google Shopping and Facebook Product Feeds during Black Friday using xAI Grok Modifier

Mon, 18 Nov 2024 09:25:00 +0000

Black Friday is the pinnacle of the shopping season, and for e-commerce businesses, it's a golden opportunity to maximise sales. Effective feed management is crucial during this period to ensure your products stand out on platforms like Google Shopping and Facebook Ads. One tool that can elevate your feed management strategy is our new Grok Modifier. In this blog post, we'll explore how using Grok Modifier can enrich your product descriptions for Google Shopping and create Black Friday-specific titles for Facebook Ads. The Importance of Feed Management During Black Friday Feed management involves the organisation and optimisation of product data feeds to improve visibility and performance on shopping channels. During Black Friday, competition is huge, and consumers are overloaded with offers. Proper feed management ensures that your products are accurately represented, searchable, and attracting to potential buyers on platforms like Google Shopping and Facebook Ads. Enhancing Google Shopping Product Descriptions with Grok Modifier Grok Modifier (powered by xAI's Grok model) allows you to enrich your product descriptions by adding relevant keywords, enhancing the chances of your products appearing in search results. By tailoring descriptions to include Black Friday-related terms, you increase the relevance of your ads during this high-traffic period. Example of use: Prompt: Refactor the following product description to make it more compelling for Black Friday shoppers in google shopping feed. Include: Key features and benefits, A 50% Black Friday discount, Additional details such as material,SEO keywords: 'Black Friday deal', 'limited-time offer', 'best price', A persuasive tone that creates urgency, A clear call to action. Please write the description in plain text without any markdown formatting, asterisks, bullet points, headings, or special characters. Current description: Crafting Black Friday-Specific Titles for Facebook Ads Facebook Ads offer a unique opportunity to reach a broad audience with targeted messages. Grok Modifier can enhance your Facebook Ads strategy by Generating attention-grabbing titles that include Black Friday-specific keywords to increase click-through rates and bring more attention to your deal. Example of use: prompt: Refactor the following product title to make it more compelling for Black Friday shoppers for Facebook ads. Keep it eye-catching, short. Include: A 50% Black Friday discount,A persuasive tone that creates urgency, A clear call to action. Please write the title in plain text without any markdown formatting, asterisks, bullet points, headings, or special characters. Current title: Black Friday presents a significant opportunity for e-commerce businesses to boost sales, and effective feed management is the key to standing out in a crowded marketplace. Using Grok Modifier to enrich your product descriptions for Google Shopping and create Black Friday-specific titles for Facebook Ads can give you a competitive edge.

Google Merchant Centre Shopping Promotions - What are they, how do they work and how to set them up?

Mon, 04 Nov 2024 10:49:14 +0000

Do you have a discount code you'd like to showcase to attract more potential buyers and make your ads stand out? Google Merchant Center promotions are designed to help you do just that! At the time of writing this article, Google supports the following promotion types: Amount offa) Amount off - Offer a fixed monetary discount on products.b) Buy quantity of products, get amount off - Provide a fixed monetary discount to customers when they purchase a specified quantity.c) Buy quantity of products, get the same item at a discount - Offer a discount on an additional item of the same product when customers purchase a specified quantity. Percentage offa) Percentage off - Apply a percentage discount on products.b) Buy quantity of products, get percent off - Provide a percentage discount when customers purchase a specified quantity of products.c) Buy quantity of products, get the same item at a percent off - Offer a percentage discount on an additional item of the same product when customers purchase a specified. Free gifta) Get a free gift - Include a free gift with the customer’s purchase. A minimum purchase amount or quantity can be set as a requirement.b) Get a gift card - Offer a free gift card with the purchase. A minimum purchase amount or quantity can be specified.c) Give a free gift from your inventory - Add a free product from your inventory to the customer’s basket. A minimum purchase amount or quantity can be specified. Free deliverya) Free standard delivery - Provide free standard shipping.b) Free overnight delivery - Offer free overnight shipping.c) Free 2-day delivery - Provide free two-day shipping. How to set it up? To set up your promotion, go to Google Merchant Center under Marketing > Promotions (https://merchants.google.com/mc/promotions/list) and click the Create promotion button. This will open a form where you can enter your promotion details: (If you don’t have access to this section, ensure it is enabled in the Add-ons section - https://merchants.google.com/mc/addons) Country and language of your promotionThis setting allows Google to determine the country and language for displaying the promotion, as well as to identify the appropriate feed and items to attach the promotion to. Title This is the callout for your promotion, e.g., "Get 10% off when spending £100." Promotion id This is a unique identifier for your promotion, useful for advanced filtering within your product feed. It can include only letters, numbers, hyphens, and underscores, and is not visible to customers. Unlike a coupon code, this ID is unique and cannot be reused after the promotion expires. Filters/items applicabilityThere are currently three ways to specify which items a promotion applies to:a) All products - Apply the promotion to all items in your feed.b) Attribute filters - Filter applicability based on specific attributes: - Feed label - Item id - Product type - Brand - Item_group_id Promotion id This method allows you to determine eligibility by including a matching promotion_id in the product feed alongside relevant product details. It is the most flexible option for complex or dynamic promotions, such as applying only to full-price items. You can set detailed rules and assign relevant promotion IDs through our platform. Promo/coupon codeThe code required for customers to redeem your promotion at checkout. Start and end date The dates when your promotion begins and ends. After saving, Google requires at least 24 hours, and up to 72 hours, to review your promotion before it can go live. Common rejection reasons Despite your best efforts, some promotions may be rejected. Below are some of the most common rejection reasons and how to resolve them: Unmapped itemsThis occurs when the applicability filter does not match any items. For regular filters, this might be due to a typo or the absence of the specified attribute in your product feed. If using promotion_id, it could be due to Google reviewing the promotion before their system has updated items with the relevant promotion_id. If you’re confident that the promotion_id was assigned correctly, you can request Google to re-test it. In most cases, this resolves the issue and gets the promotion approved. Your landing page price is the same as the checkout priceIf the discount is already applied on the product page, and there’s no additional discount at checkout, the promotion may be rejected because it does not offer anything extra for the user at checkout. This usually happens with regular sales or if the coupon code isn’t working and no additional discount is applied at checkout. In such cases, verify that your coupon code is working and request Google to re-test the promotion. If it's a regular sale, note that promotions may not apply, though a sale badge might display depending on your price history. For more information, refer to this guide on Google Shopping sale badges and price annotations. We hope this article helps you set up successful Google Merchant Center shopping promotions, but if you need further assistance, please get in touch with us.

Extract your Shopify’s store navigation menu breadcrumbs with ease

Tue, 01 Oct 2024 11:58:14 +0000

We are excited to announce support for Shopify's newly released API feature, enabling access to navigational breadcrumbs within Feedoptimise or its app. Enabling this feature in Feedoptimise is easy. Simply ensure that collections import is enabled in your source settings. After the next import, the collections list will include an additional attribute, indicating whether it is a navigational collection — breadcrumbs — for example: We have also built it into our Shopify’s source mapping shortcuts as well as under Modfier group ones, making it very easy to add to your existing setup, simply: Add a field where you want your navigational breadcrumbs to appear Drag and drop your collections attribute into the designated field: Add the Group Modifier preset: Shopify Nav Breadcrumbs Collections ExtractThis will generate the following output, based on the example provided in this post: This feature is highly beneficial for passing relevant product types in your feed to Google Shopping, Meta, TikTok and other marketing channels that support merchant categorization in their feed specifications. Of course, if you need assistance with implementation or have any questions, our team is here to help. Just contact us or open a ticket through our platform. Users who installed our app before October 15 (prior to the navigational menu being available through Shopify’s API) may need to re-authorize the app to grant the necessary permissions for pulling breadcrumbs data.

Introducing the Feed Labelizer: A Powerful Feature to Enhance Campaign Item Segmentation and Labeling

Wed, 04 Sep 2024 16:20:09 +0000

Whether you’re optimizing product listings, refining targeting strategies, or maximizing ROI, the ability to fine-tune your Google Shopping and any other marketing campaigns can make all the difference.We’re excited to share a new feature in our feed management platform aimed at improving and maximizing your campaign management capabilities - the Feed Labelizer. What is Feed Labelizer? It is an innovative set of tools designed to help you better segment and categorize the items using dynamic custom labels in your campaigns based on specific conditions, rules, and their performance.By automating the process of item categorization/labeling based on their marketing performance, Labelizer empowers you to achieve more granular control over your campaigns, ensuring that each product or listing is managed according to its unique characteristics and performance metrics. Why Use Labelizer? The benefits of using Labelizer are manifold: Enhanced Precision: With more refined segmentation, you can ensure that each item in your campaign receives the attention it deserves based on its performance. Time Savings: Automating the labeling process allows you to focus on strategy rather than manual data sorting and categorization. Better Performance Management: By quickly identifying which items are hitting or missing targets, you can make informed decisions that boost overall campaign performance. Scalability: Labelizer is especially valuable for managing large feeds, where manual segmentation would be impractical. How Does Labelizer Work? At its core, Labelizer allows you to set up complex "IF-THEN-ELSE" conditions for each item in your feed. For instance, you can define rules based on various fields such as ROAS (Return on Ad Spend), clicks, impressions, or conversions.Depending on whether an item meets the criteria you’ve set, Labelizer will automatically assign the following labels to it, which can then be used to tailor your campaign strategies more effectively. New item - an item is new and has no performance data yet Target - an item is meeting your performance targets Over target - an item is exceeding your performance targets Near target - an item is close to meeting your performance targets Under target - an item is falling short of your performance targets Below data Threshold - an item does not have enough data to make a decision You can tweak the following thresholds for each label to match your specific campaign goals and KPIs, ensuring that your items are segmented in a way that aligns with your overall strategy. click_multiplier It is used to calculate the minimum number of clicks required for an item to be considered for a label (Over target and Target), it is counted from the formula: min_clicks = 100*click_multiplier/avg_conversion_rate.With a very low avg_conversion_rate or amount of clicks consider lowering the click_multiplier (you can use float numbers eg 0.1) or set it to 0 to disable the min clicks threshold. roas_over_target the minimum ROAS required for an item to be labeled as “Over target” roas_target the minimum ROAS required for an item to be labeled as “Target” roas_near_target the minimum ROAS required for an item to be labeled as “Near target” min_conversions_threshold the minimum number of conversions required for an item to be considered for a label min_impressions_threshold the minimum number of impressions required for an item to be considered for a label new_items_days the number of days an item needs to be considered “New” You can also change or add your conditions to personalize Labelizer to your needs even further. Implementing Labelizer in Your Campaigns Connect Google Ads Reports with our platform Create a new report with metrics: Clicks, Conversions, Impressions and ROAS Go to your feed and select a custom label to use. Add Labelizer from modifiers group shortcuts Adjust the below thresholds settings to your needs For each empty field, click the modifier button and connect the relevant metric from your report.Use variant metrics if you set it on the variant level per each variant or parent metrics if you set it on the parent level. Add a default analyser to your custom label field. Save and rerun the feed. Open the feed stats panel to see the split of your labels. If needed, make adjustments to your thresholds and rerun the feed stats to see the changes. Track your campaign performance and see what Labelizer brings for you. By introducing advanced, automated item segmentation, our platform provides the tools enabling you to optimize your campaigns with unparalleled precision. Whether you're aiming to maximize ROAS, improve targeting, or streamline your feed management processes, Feed Labelizer is here to help you achieve your goals. We invite you to explore Feed Labelizer in our feed management platform today and discover how this powerful tool can elevate your campaign strategy to new heights. If you have any questions, feel free to get in touch using our contact page.

Automatically Validate all your XML Feeds

Sun, 25 Aug 2024 08:45:37 +0000

XML (eXtensible Markup Language) is a commonly used feed format providing structured data across various applications and feed channels. Whether for data feed interchange between systems, configuration files, or document storage, XML provides a flexible and readable way to represent information. However, with this flexibility comes the need for validation to ensure that XML data is structured and formatted correctly. In this blog post, we'll explore what XML validation is, why it’s essential, and how our platform handles it for you automatically. What is XML Validation? XML validation is the process of verifying that an XML document/feed conforms to a predefined structure, set of rules, or schema. The validation process ensures that the XML feed is both well-formed and valid: Well-formed XML: A well-formed XML feed adheres to the basic syntax rules of XML. This includes properly nested elements, matching opening and closing tags, correct use of attributes, use of non-reserved characters inside node names and its attributes, and a single root element. Valid XML: A valid XML document goes a step further by conforming to a specific schema or Document Type Definition (DTD). The schema defines the structure, element types, data types, and relationships within the XML document. Common XML validation methods include using DTDs (Document Type Definitions), XML Schema (XSD), or RELAX NG. These provide the rules that an XML document must follow to be considered valid. Why XML Needs to be Validated? Data Integrity and AccuracyOne of the most critical reasons for XML validation is to ensure data integrity. By validating XML against a schema, you can guarantee that the data adheres to the expected format and structure. For instance, if your XML document is expected to include a date in a specific format, validation ensures that any data outside of this format is flagged as incorrect. This prevents errors from propagating through your system and ensures that the data you work with is accurate and consistent. Error PreventionValidation helps catch errors early in the data processing pipeline. Without validation, malformed XML can cause downstream issues, such as application crashes, data corruption, or failed data exchanges between systems. For example, if your XML feed is used to provide product details to your partners, an invalid configuration or a character could lead to unexpected behavior, system failures, or a lack of updates on the receiving side. By validating XML, you can prevent such issues before they become critical. InteroperabilityXML is often used as a medium for data exchange between different systems or applications. When multiple systems rely on the same XML structure, the data must adhere to a common standard. Validation ensures that the XML document meets these standards, enabling smooth communication between systems. Without validation, you risk incompatibility issues, leading to failed integrations. How Do We Do It? Now, for the best part—at Feedoptimise, XML validation is automatically handled for you. Every time we sync your XML feed, we run it through the XML validation process, ensuring that your XML feeds remain valid at all times. Whenever you make changes to your mapping or add new items to your website, which then flow to your XML feed, we validate it for you. In case of an issue, our feed management platform promptly notify you and continue using a backup version of your valid XML feed from the most recent successful run. This ensures that your partners receive a valid and stable XML file, even while the underlying issue is being checked and resolved.

Show Customers More Products from Your Website on Google: What Does It Mean?

Fri, 24 May 2024 09:57:56 +0000

You might have received an email from Google Merchant Center recently stating that you have missing items that will be automatically added as of June 22, 2024. In most cases, especially if you have your integrations done with us, you don’t want this feature to be enabled. Fortunately, even though it will be enabled by default, it won’t include those items in your Shopping campaigns. However, we recommend disabling it when you have the chance. The reason this feature is not necessary is that those items are either excluded from the feed due to optimization needs, or being out of stock, or because the item ID you use for those items in the feed might differ from the item ID your microdata shows in the source code of your product pages. For example, in the case of Shopify, where this issue is most common, people often use the prefix shopify_{COUNTRY}_{parentid}_{variantid} due to legacy settings when enabling things using Google’s Shopify app, but their microdata often uses SKUs. Google's bots determine whether an item is included based on the IDs they see in your source code. If different IDs are used as described above, Google is not actually adding new items but duplicating those already there with different IDs. Often, they also add these items without any extra attributes such as gender, age group, GTIN, etc., because these are often not available to their bots. In conclusion, that email is often incorrectly stating that you are missing something when, in fact, you are not. This is why we recommend keeping that feature disabled when possible to prevent duplications or items with incomplete attributes from being included. If you have any questions, feel free to get in touch using our contact page.

Tracking Google Ads conversions without connecting Shopify to Merchant Centre

Sun, 12 May 2024 10:39:22 +0000

Suppose you use a feeds management platform like Feedoptimise to manage and optimise your Google Shopping feed in Google Merchant Centre. In that case, you will usually need to disconnect the Shopify Google sales channel/app from the Merchant Centre so it doesn’t override the data by syncing on top. That often means your conversion tracking might stop if you added tracking via Google’s Shopify app as the tracking pixel is bundled with the app, however, at the time of writing this article, app settings are not letting you have just the tracking part without syncing products to GMC. But worry no more, below we have listed 2 methods of how you can bypass that and be able to manage and optimise your feed using a feed management platform like ours and be able to track conversions without overriding and losing your feed optimisation efforts. Installing the pixel directlyIntegrate it using the direct approach via customer events as documented here. This option is by far the cleanest and most independent way of integrating Google Ads conversion tracking as you don’t need to connect Shopify with Merchant Centre at all and all the data in there comes from just 1 place plus you can control and add all extra options and parameters google ads tracking has to offer. Using Shopify App + feed labelContrary to the post title, with this option, you connect Shopify back and change the feed label on the optimised feed you are using the feed management platform for to a different one than Shopify's Content API is using. That way they will not clash and override each other, but you'll need to update your campaign setting to load the data from the relevant feed label.If you opt-in for this option, we recommend re-connecting using a destination other than shopping ads and free listings to prevent diagnostics issues and suspension warnings in case of missing attributes, e.g. you could use Google Cloud Retail as a destination for it. We hope this article will help get your Shopify's Google Ads conversion tracking back in order and if you have any questions, feel free to contact us.

Enhance Your Google Shopping Strategies with Dynamic Price Competitiveness Labeling and Feedoptimise Prisync Integration

Wed, 24 Apr 2024 13:53:44 +0000

In the dynamic world of online retail, optimising your Google Shopping feed and campaign performance is crucial for driving superior ROI and increasing sales on your website. Key strategies involve ensuring your listings are aligned with customer searches by fully populating relevant attributes and structuring campaigns effectively to maximize budget utilization. This approach ensures that high-conversion items receive adequate funding. However, exceptional content and optimized data might fall short if your pricing isn’t competitive. Potential customers might click on a competitor's advertisement instead, even if yours ranks higher. That's where our new partnership with Prisync becomes game-changing. We're thrilled to offer our clients the ability to harness Prisync’s comprehensive price benchmarking insights, seamlessly integrated into the Feedoptimise platform. This integration enables the creation of dynamic, automated custom labels, enhancing your feed management across platforms like Google Shopping, Bing Shopping, and Meta, thereby amplifying your campaign's effectiveness and boosting your return on ad spend (ROAS). With the Prisync modifier, creating custom labels that categorize products based on price competitiveness becomes straightforward. This allows for strategic budget allocation to competitively priced items, optimizing your campaign's overall performance. Additionally, you can import extensive metadata regarding your product’s pricing, including competitive pricing comparisons and discrepancies, and generate insightful visual reports with analysers or dive deep into analytical science behind pricing to performance correlation using AI data analyst. The data points you can integrate into your campaigns include: My Position: A numerical indicator of your pricing competitiveness, with 1 representing the most competitive price. My Diff to the Cheapest: The monetary difference between your price and the lowest price available. My Diff to the Cheapest (%): The percentage difference between your price and the most competitive price. Summary: A comprehensive JSON list encapsulating all the above metrics. To start leveraging Prisync’s data for optimizing your feed campaigns, all you need is an account with Prisync and an API token, available in your account settings. Should you have any queries about the Prisync modifier or require assistance in setting up your custom labels, don't hesitate to contact us through our website’s contact form.

Optimise Shopping Campaigns with an Advanced Set of Reporting Tools

Wed, 13 Mar 2024 15:05:02 +0000

In the rapidly evolving world of e-commerce advertising and shopping campaign optimisation, staying ahead of the curve is not just an advantage, it's a necessity. Our newly developed advanced reporting system is set to redefine how businesses monitor and boost their performance across various marketing channels, including Google Shopping, Bing Shopping, Meta, TikTok, Pinterest, and more. This innovative set of tools stands out as a game changer in the industry, offering unparalleled insights, forecasts, and analytics previously out of reach for most online retailers. Unveiling the Power of Advanced Reporting Our system brings a suite of use cases that exemplify its capability to transform raw data into actionable insights, empowering e-commerce businesses to scale new heights. By leveraging our reporting system, users can expect to make informed decisions, optimizing their marketing strategies for maximum ROI. Here’s how: 1) Discovering Top Best Sellers Determining your top best-selling products is an essential first step for any e-commerce business aiming for success. This pivotal report highlights the products generating the most revenue and conversions, offering a clear view of what drives your business forward. However, the crucial question is: which criteria should we use to define best-selling items? The answer varies across industries. In fashion, the number of items sold might be a good enough metric, whereas in electronics, revenue, or even better, Return on Ad Spend (ROAS), could provide more insight. A straightforward approach might be to set a baseline for conversions, such as "conversion > X." However, as product offerings expand, this criterion may need adjustment. To ensure adaptability, we recommend focusing on the top 20% of products by conversions, allowing for a dynamic response to an ever-growing inventory. In our approach, we've highlighted the top 20% of products with the best ROAS, tagging them as "best sellers" in our feed. This enables more strategic bidding on these items in Google Ads campaigns. Our Reporting System allows for an extensive range of criteria to define best sellers, including metrics not originally available in source reports, such as ROAS which is not available in Google Ads Reports or Google Analytics. These can be easily calculated with modifiers and then applied within reports to pinpoint your most valuable products. 2) Trends: A Powerful Tool for a Broader Perspective Sometimes, the flat, snapshot data from standard reports isn't sufficient to understand the full scope of changes. The Click-Through Rate (CTR) is a metric particularly challenging to interpret based on just the latest results, as numerous factors can influence it and it can fluctuate significantly from day to day. It's beneficial to have access to a report that illustrates the CTR trend over time, enabling actions to be taken based on the trend's slope, representing the average daily change. In the provided example, we observe that CTR experiences spikes over weekends. By examining the trend over the last 30 days, we can determine whether the CTR is trending upwards or downwards. If we notice the trending line ascending with a slope of 0.01, it indicates a daily increase in CTR by 0.01, equating to a 1.86% increase in the average CTR over the past month. Such insights are invaluable, as monitoring CTR trends allows identifying patterns and the optimal timing for campaign adjustments. This deeper understanding enables businesses to fine-tune their strategies, engaging audiences more effectively by capitalizing on the most opportune moments, and ultimately driving improved performance. 3) Deciphering the Price-Clicks Correlation: A Strategic Insight Observing a rise in clicks prompts an important question: Is this surge due to a price change, or is another variable at play? Our report highlights the intricate relationship between price adjustments and click-through rates. It reveals that a modest reduction in price, say by 2 pounds (~5%), can lead to a significant surge in clicks, in this case, an increase of over 220%. But how can we leverage this insight? By identifying the items for which this price-click correlation is most pronounced, we can pinpoint specific variants attracting the most attention. Conversely, the report highlights scenarios where price adjustments have little to no impact on click volumes. This could suggest that our pricing is already competitive, presenting an opportunity to increase prices without sacrificing market position. This analysis not only enhances our understanding of market dynamics but also empowers us to make informed decisions. By strategically adjusting prices based on this correlation, we can optimise our product offerings for both engagement and revenue, ensuring that our pricing strategy aligns with consumer behavior and market trends. 4) Strategic Insights: Focusing on Best-Selling Sizes In the realm of product variations, understanding which sizes resonate most with your audience can significantly impact your sales strategy. Our analysis dives deep into product size preferences, revealing the most popular sizes based on conversion rates. For instance, we've found that sizes 10 and 13 stand out as particularly appealing, driving the highest number of conversions. What actions can we take with these insights? A practical approach involves integrating a custom label into our feed, allowing us to allocate more resources to advertising these popular sizes. By prioritizing sizes 10 and 13 in our bids, we enhance visibility for the products most likely to convert. Further refining our strategy, we sort variations by conversion rate and implement a rule to highlight the top 20% of sizes achieving conversions above the average. These sizes are then tagged as "Bestseller variant" in our system. This method not only streamlines our marketing efforts but also ensures that we are focusing on the product variations that truly resonate with our customers, optimizing both engagement and sales. Transform Your E-Commerce Strategy Today! Our advanced reporting system is more than just a tool; it's your gateway to unlocking the full potential of your e-commerce business. By providing insights that were previously inaccessible, we're empowering retailers to optimise their marketing efforts, enhance their product offerings, and ultimately, drive unparalleled growth. If you need any help setting up the labelling process for you, get in touch with us via our contact us page.

Enhance Google Shopping performance with automated AI-driven A/B tested product titles

Fri, 19 Jan 2024 15:53:58 +0000

We are excited to share an innovative approach to optimizing Google Shopping feeds using Feedoptimise and OpenAI's ChatGPT. We focus on leveraging AI to significantly boost traffic and conversions by optimizing item titles. Strategy Overview: The goal is to identify high-potential items and experiment with three different title versions. We'll analyze each version's performance and automatically adopt the title that delivers the best results. We'll target items with a high conversion rate (numerous conversions per click) yet a low Click-Through Rate (CTR). This usually indicates that while these products are popular upon viewing the product page, they are often overlooked in ads, potentially due to suboptimal titles. Process Steps: 1. Identifying High-Potential Items and set the first condition to initiate A/B testing: The above condition has 4 rules: Keeps the final best-performed title post-testing. Maintains the current title during the testing cycle (lasting 7 days). Generates a new title for testing or for the subsequent cycle when applicable. Defaults to the original title as a baseline. 2. Generating Title Variations: Version 1 - Basic Optimization: Utilizes OpenAI's Title Optimizer to craft a straightforward title combining the product title, brand, and category. Version 2 - Enhanced Attributes: This version incorporates additional attributes such as color, material, and a dynamically rotating variable for the season (Winter, Spring, Summer, and Autumn). Version 3 - Custom Creative Approach: Here, we take a more unique route by crafting a custom prompt. The goal is to create an optimized title for the Google shopping feed, particularly appealing to younger audiences, using the given title and item description. Prompt: Use the below item title and description to build the best possible optimised title for Google shopping feed that will bring the attention of young people | title: “{title}” and item description: {description} 3. Performance Measurement and Selection We automatically record each title's performance over a 7-day testing period into an override sheet using our reporting functionality and Google Ads integration. At the end of the test, we implement a rule to adopt the title with the highest performance. This approach, in certain instances, has led to over 700% improvement in CTR, significantly boosting sales for the best-converting items. This method offers a scalable, fully automated solution that can be effortlessly replicated across multiple campaigns and websites. By harnessing the power of AI, we're not just optimizing titles – we're revolutionizing the way products are presented and discovered in Google Shopping.

Enrich your Product Feed Custom Labels and Product Sets with Weather Forecasts

Tue, 19 Dec 2023 15:12:48 +0000

We are pleased to present the Weather Forecast Modifier allowing you to segment your Google Shopping, Meta or any other shopping or social campaigns across a new set of dimensions enabling you to respond to changing marketing intents faster. There are lots of use cases on how that modifier can empower your campaigns, the umbrellas and sunglasses labeling is just a quick example of how you can effectively respond to changing weather conditions and be ahead of the competition: You can also create your regions by combining the cities - to represent the average temperature for the south of England, you could combine the following cities: To give you lots of control over the forecasts, we are supporting up to 14 days ahead window for the following metrics: Temperature Feels-like temperature Rain Snow Clouds Pressure Wind speed Wind gust Wind direction We believe that the Weather Forecast Modifier can greatly improve your various marketing campaign ROAS when combined with other signals such as content or performance inputs.

How to Improve Pixel Match Rates across Meta Catalogs

Fri, 17 Nov 2023 12:38:56 +0000

Meta catalog ads are a great way to showcase your products to potential customers however, if your item IDs are not properly matched, you may be missing out on valuable insights and opportunities. Item ID pixel matching is the process of ensuring that the item IDs in your Meta product catalog match the item IDs as sent by Meta pixel installed on your website. This is crucial since it allows Meta to accurately track what items your website visitors are interacting with and in return enables you to remind them about those items when browsing through the Meta ecosystem thus improving the performance of your catalog campaigns. When item IDs are not properly matched, Meta may not be able to track the visits and sales of your products, which can lead to inaccurate reporting and lost ad spend as well as a low pixel matching rate. There are a few things you can do to improve your item ID pixel matching: 1) Make sure to implement required events such as ViewContent, AddToCart, and Purchase. 2) Make sure that the item IDs in your Meta product catalog are unique and consistent and are matching with your content_id as passed by pixel. 3) Make sure your content_type matches up: use product_group if you communicate parent IDs and want to match with item_group_id in your feed use product, if you communicate variant IDs - content_id should match with your item ID in the feed. 4) Use the Facebook Pixel Helper to verify that your item IDs are being properly matched. By following these tips, you can improve your item ID pixel matching and get the most out of your Facebook and Instagram catalogs and relevant marketing campaigns.

How to display Google Shopping Sale Badge aka Strike-through price aka Sale Price Annotations across your Google Shopping Ads

Tue, 10 Oct 2023 12:53:14 +0000

We all know strike-through was price/sale price is something making listings more visible and something that anyone would naturally like to show across Google Shopping Ads and pass into the Google Merchant Centre to make items more visible, especially during the holiday season such as Black Friday, Cyber Monday and Xmas sales. How to do it? Below are the data feed attribute and data requirements to receive a sale badge: Sale_price - make sure your feed includes the sale_price attribute which should be set to your new price value and the price attribute which should be set to your was price amount. Make sure your “was price” has been charged for a total of at least 30 days (it doesn't need to be consecutive) in the past 200 days prior to your current sale price Discount right - Google only considers sale prices discounted no more than 90% and no less than 5% Troubleshoot in case your strike-through is not showing: Audit the number of items with the badge - go into merchant center - Products > All Products and select a filter to show any items with the sale badge on so you can check against your expectations. Right feed’s data but Google bots are seeing old prices? - microdata - make sure your website’s microdata is reflective of your sale price and not featuring old ones. Correct feed data and microdata but the sale badge is not showing. - check for the price history as per point 3 from the requirements list above. Get in touch with us if you are still not sure why it might be happening. Also, Google sometimes takes a bit of time to update so make sure not to check if it worked too quickly, they will need an hour or so to process your latest feed first.

How to fix the “Limited performance due to missing value GTIN [gtin]” warning in Google Merchant Centre

Tue, 15 Aug 2023 10:52:26 +0000

Let's start with a bit of background - GTINs are unique and globally recognizable item identifiers developed by GS1 (gs1.org) allowing comparison engines, stores, and barcode scanners (to name just a few) to identify items and group sellers selling the same items together. GTINs provide their own 14 digits format and are also acting as an umbrella/unifying format spanning across various already preexisting barcode standards such as EAN in Europe, UPC in the United States or JAN for Japan, and more e.g. ISBN for books, etc. GTINs / barcodes are widely used with barcode scanners as well as price comparison engines due to their unique quantities allowing them to group items together or have a safe and stable unique and global item reference as mentioned above. Now what to do if they are missing in your store and you get the warning inside Google Merchant Centre diagnostics report? - here are some solutions that hopefully might allow you to source them and add them to the Google merchant center feed: Probably the most obvious but not always a quick solution is to type them down from product boxes - they should be featured around the barcode label area. In case you don’t stock your items or the scale of how many you have is too big to do it box by box throughout your inventory, the 2nd option would be to check with your suppliers. As GTIN is a global standard and widely used across the industry for years, your suppliers most likely have them and hopefully can provide them to you in a way helping to assign them to your items - you can easily add them under our platform if you have them in some spreadsheet format. Buy them - if you are a manufacturer yourself and items you list in the Google merchant center are showing a missing GTIN warning, that means Google thinks you should have it and as such you might need to buy them from GS1. However, if you are not a manufacturer, buying unassigned GS1 barcodes and assigning them to branded items might be risky as Google often knows the ranges that a brand purchased and can asses yours against them. Probably the quickest way but also it’s the last on the list as it's not really a solution - if you can’t use any of the steps above - just ignore the warning, it's not a blocker, your items are active and we are not really sure how big an impact it has on the performance since when you think about it, actual google shopping ads (not listings) are not grouped with each other anyway. And yes, it might give Google some extra signals but it's not like most aren’t already there - title, brand, MPN, etc. However please note, this is only if you can't get the real barcodes or it is a very long and time-consuming process that will take ages. But why Google doesn’t require it across all my items? - that is because although you can buy them from GS1, GS1 doesn’t have a database that clearly assigns a barcode to an item, the assignment is usually managed under each of the manufacturer databases so Google assumes those things based on how many sellers sell given brand, or what category you list under and how often GTIN is being pushed there by others. Sometimes brands provide Google with barcode ranges using Google Manufacturer Centre or other means Google provides them with for that. OK, but someone told me about using identifier_exists google shopping feed attribute as a possible alternative - Well, you could remove the missing GTIN warning by using identifier_exists = no however only if you would also remove brand and MPN attributes from the feed alongside, meaning - compromise feed over a warning which doesn’t really block your items anyway. Can I make my own? - Nope, you can have lots of issues by making up yours since GTIN has a self-validation mechanism that Google checks, and in case it doesn’t pass, it won’t be a warning but straight disapproval so it best not to send one and then send an invalid one, and even if you could somehow make it valid by guessing it, you might end up merging your items with completely different ones due to GTIN collisions.

CSS not selected for this destination - How to fix Free Listings in Google Merchant Center (GMC)

Sat, 01 Jul 2023 11:13:40 +0000

With the emergence of Comparison Shopping Services inside the Google Ads ecosystem, a few things might be affected when choosing to work with a CSS provider other than Google Shopping - Two of them being the free listings and placements of product listings outside of Europe. If you are affected by one of these issues, don’t worry; it's very easy to fix, but you might wonder why you need to fix it in the first place since it has either started showing just now and you didn’t have that issue before or after a switch to another CSS. Well, this is because, as Google has put it under their help article, due to the following reasons: Each merchant domain can select only one CSS to represent them for placing products in ads and free listings outside Europe. So in case, you have not elected free listings eligibility before, or you switched recently to some CSS, Google might change the free listings association, thus removing it from the account you are using and assigning it to some other CSS. Now, the fix - all you need to do is: 1) Have a verified and claimed Google Merchant Centre account - the one you switched to another CSS doesn’t count anymore in that sense, the only one which can control the settings is a Google Merchant Centre, which says - CSS: Google Shopping (google.com/shopping) - like on the screen sheet below If you don’t have a Merchant Center associated with Google Shopping CSS, you’ll need to create a new one and verify it using an email address that hasn’t been assigned to any other Merchant Center before. 2) Go under Settings > Shopping Ads setup > Comparison Shopping Services If your menu on the left looks different (Google is currently A/B testing a new UI), you can find it under: Settings > General > Comparison Shopping Services. If this is a newly created Merchant Center account, give it about 5 minutes. If you don’t see the section above right away, Google sometimes takes a little time to fully apply settings for brand-new accounts. 3) Nominate - Scroll down to the Comparison Shopping Services (CSSs) selection table, where you can see and nominate the Merchant Centre account you wish to have eligibility for a) Free listings b) Placements of product listings outside of Europe And that is all - 24h to 48h later, the issue in your desired merchant center will be fixed. You might now wonder what to do with that Google Merchant Centre you used to nominate the free listings eligibility if you want to run the ads and listings via another CSS. Well, the answer is nothing, just keep it verified for future needs but otherwise, you can keep it empty and run ads and feeds via the other CSS one.

Google Merchant Center Misrepresentation Suspensions - Common Causes

Fri, 09 Jun 2023 11:21:10 +0000

In case your Google Merchant Center got suspended due to misrepresentation, and you wonder how to fix the issues causing the suspension, we have listed a few common reasons worth investigating. Google usually doesn’t explain exactly what caused that type of suspension, so you are left on your own trying to figure it out, and although there are probably more reasons as to why that suspension might happen, we have listed a few issues which we have seen is the reasons in those relevant cases. Condition mismatch - this happens when the item condition such as new/used/refurbished in the feed doesn’t match with what the website shows. For refurbished items, it's also recommended to ensure that product titles include the condition status such as refurbished. Also, it's important to make sure that your microdata also shows the right condition as in case it says New but it's a Used item, Google bots will be comparing all against microdata. Image mismatch - this might happen when you sell items available in different options e.g. multiple colors but your feed uses only an image of a 1st variant color rather than assigning relevant image color to each variant separately. Similar rules apply to e.g. sizes, packs, etc, always make sure that the item’s image reflects correctly what the item is about. Content mismatch - this can be caused by using the wrong titles for the wrong items, e.g. you are advertising item A but your title says it's an item B. Invalid policy pages - you are missing pages such as privacy policy, terms & conditions, and refund policy - make sure your website does include those pages and they are easily accessible, usually website’s footer is the best place for them. Placeholder/unfinished/under construction pages - make sure that you don’t have such pages and if you do, hide them from the public until it's all finished as those pages can cause misrepresentation due to suggesting your website is not finished yet or something doesn’t work correctly there. Non-working preselection - you have items available in different options but your URL doesn’t preselect the correct variant causing users to see not the one they clicked on - make sure your URL preselection works and the user is seeing the right option immediately on the landing page without any extra clicks. Those are just ideas we would recommend to check and of course, feel free to contact us if you need help or would like to sign up for one of our plans and we can then try to help you figure it out.

Product Feed Management - What It Means and How To Do It

Fri, 02 Jun 2023 10:40:01 +0000

Product feed management is an iterative process that aims to improve overall feed quality and the ROAS of your campaign. Its results can be measured by the impact of these changes on campaign performance enabled by the feed. The product feed management process can be divided into three main tasks listed below, all of them aiming to optimize and improve overall feed quality: 1) Filtration This process enables you to exclude unwanted items, such as out-of-stock items, discontinued items, hidden items, low-stock items and more thus making sure your campaign's budget is being consumed by the items you want to advertise and know should convert the best resulting in better ROI. Various filters and exclusion rules can be easily accomplished using our product feed analysers. 2) Labelling Labeling allows you to group your items into subsets with common characteristics, such as bestsellers, new items, and sale items, and allocate relevant budgets and campaign management strategies to such groups accordingly. Here are some ideas for highly effective and dynamic custom labels. 3) Content optimization Content is what drives your traffic and informs users about what you have to offer. You should ensure that your product titles contain relevant keywords (words people usually search for when looking for items you sell), product types are deeply nested (website categorizations are good to be used here assuming they are deep), and all other relevant attributes are supplied for a highly optimized feed. When it comes to Google Shopping feeds, we also recommend looking into attributes such as product_detail - allowing you to add lots of specification-like data as well as product_highlight - allowing you to list unique selling points for your items. A/B testing is the next step during the content optimization step which ensures that the best content is used to reach your marketing goals. With our platform, changing and enriching your content is easy thus enabling you to test different listing content variations. You can easily set up bulk extract and override rules using modifiers, or provide your copies using supplemental sheets or catalog overrides. -- By following these three product feed management principles, you can significantly improve your campaign performance and ensure that your product feed is of high quality. It's important to regularly review and optimize your product feed to ensure that it meets the requirements of your audience and platforms, such as Google Shopping. With the right tools and strategies, you can turn your product feed into a powerful marketing tool for your business.

Integrate Global-e with your store and create currency-specific Google Shopping product feeds

Thu, 11 May 2023 15:46:07 +0000

Global-e provides cross-border e-commerce solutions allowing you to sell globally and helping with currency conversion and more. Feedoptimise can easily integrate with Global-e API and make sure all the relevant country product feeds inside the Google Merchant Centre are up to date as per the relevant currencies and exchange rates and make sure that the feed's prices are matching up with what your website displays thus keeping the Google policy team happy. We support Global-e connectivity across any shopping cart - Shopify (legacy and markets), BigCommerce, Magento, WooCommerce, Presta, Salesforce and all other platforms including the custom build ones. For all platforms except Shopify markets integration, you can import relevant currencies using the Global-e modifier: For Shopify markets-based integration (non-legacy one), the relevant Global-e currencies can be imported directly at the source settings level: Once you have relevant country currencies implemented, the last part is to make sure your feed contains relevant country tags so your website can display relevant currency as per Google Shopping stable landing page policy. So, in order to do it you need to tag your URLs using a URL tagger modifier with relevant values which are: 1) Shopify markets - ?country= e.g. 2) Other stores/legacy Shopify integrations - ?glCurrency for currency and glCountry= for the country: And of course, our team can assist you with all the setup if needed, just let us know.

Boost your Google Shopping campaign with dynamic custom labels

Mon, 08 May 2023 11:52:22 +0000

Google Shopping custom labels are powerful Google merchant center's feed custom attributes allowing you to slice and dice your campaigns into specific segments based on arbitrary conditions such as profitability, performance, seasonality, and more. Below we have listed some popular and handy-to-know custom label ideas and some examples and a few of many ways, how it might be implemented in Feedoptimise. Profitability as the name suggests, it allows you to split items by the amount of profit you receive when they convert. Obviously, items with high profitability can work under less strict budget limits than those with smaller profitability margins. With Feedoptimise, you can easily implement such labels as long as you either have a cost stored in your e-commerce platform or can provide it to us using external sheets, you can then use a profit margin calculator modifier to assist with the formula and the resulting numbers allowing you to set up the label. Price brackets that label is used to split items into price-driven buckets e.g. cheaper and more expensive such as anything with prices between 0 - 10, 10 - 50, 50 - 100, and so on. That way, you can make sure your Google Shopping campaign structure is dependent on how expensive items are. You can accomplish that in many ways but the fastest one would be by using Range labels modifier: Performance once you connect your Feedoptimise account with your Google Ads or Google Analytics you have the ability to create various dynamically updated custom labels allowing you to flag bestsellers, low impression items, high clicks and low conversion ones, and more. Seasonality very handy label to have when it comes to seasonal items such as e.g. Valentine Day's gifts, Halloween or Black Friday discounted items. It's very simple to implement, you can do it using IF modifier scanning against content that might contain relevant words such as title, description, tags, etc. Stock ratio/fragmentation very popular custom label used across items with options such as sizes, colors, flavors... - it enables you to automatically filter out items that might have popular sizes not available based on a simple formula e.g. if out of 10 sizes, only 4 are in stock, that means a 40% stock ratio. you can of course make it more aware of popular sizes by defining them and counting them separately. Freshness A handy label that allows you to boost items based on when they have been released. Very easy to implement in Feedoptimise yet very powerful in optimisation opportunities it might create.

Optimise your product feeds using ChatGPT AI models

Tue, 02 May 2023 17:55:09 +0000

In today's digital age, with so many online stores competing for customers, it can be challenging to stand out from the crowd. This is where ChatGPT comes in handy. ChatGPT is an AI-powered chatbot from OpenAI that can help optimize content for online stores and their marketing campaigns and activities, making it easier for businesses to attract and retain customers. Feedoptimise provides an easy way to take advantage of ChatGPT so you can improve your content automatically. You can easily improve your product titles by using our Title Optimiser modifier: Or product descriptions via Description Creator modifier: And of course, the more attributes you will pass as signals on input, the more influenced the results will become. All of that can be achieved with very minimal input and at very low cost at scale - you can use it for 1 or millions of items. Also, our platform will only utilise your OpenAI API calls and will ask ChatGPT when actual input changes so e.g. if you already used ChatGPT for a given title, we will keep using that copy without asking ChatGPT again and again unless your title actually changes thus preserving your budget with OpenAI.

Disconnect Shopify's Google App from Google Merchant Centre

Wed, 01 Jun 2022 11:08:48 +0000

If you use a feed management platform like ours and are connected with the merchant centre using our feed, you don't need to sync product details on top using the native Shopify app, as all it does is duplicate or override data we send to Google Merchant Centre already potentially causing issues with attributes you optimise using our platform. Assuming you have connected to Google Merchant Centre using Shopify's Google App/Sales Channel, you can disconnect it as follows: Go under - Sales channels > Google > Settings > Google Merchant Centre account then, if connected via the Google sales channel, you should see the option to disconnect from Google Merchant Centre as presented below: ** Please note, that disconnecting under Google Merchant Centre alone will cause Shopify to relink itself unless disconnected under Shopify as explained above. * Please ensure you've reviewed this article before disconnecting if you're using this connection to transmit conversion data to Google Ads too. In case you can't see the option to disconnect, that means you might have connected with Merchant Centre using a 3rd party app instead of Shopify's Google Sales channel as presented above in which case, you will have to disconnect as explained by the relevant app's developer.

How to link a data feed with your Meta (Facebook/Instagram) catalog

Wed, 04 May 2022 11:44:44 +0000

1) In Facebook Business Manager select the catalog you wish to link your feed to. 2) Go to “Data sources” from the left-hand side menu which assuming you don't have any feeds there already, should show the following window in which you should select the Data feed option and press the Next button: ** in case some feeds already exist as a data source, its recommend to delete them since if you have more than 1 feed with the same items they might be conflicting (if you are connected using API e.g. Shopify directly, make sure to add our feed under new catalog instead and use that to prevent conflicts as you can't easily disconnect API based connections plus you want to keep it there for the pixel reasons), but in case you have some other feed and it has different items than the one you are trying to upload with your new feed, in order to see the window above, click on the Add Items button and then select the Add Multiple Items option: 3) Next, under Are you ready to upload your spreadsheet or file - select the Yes and press the Next button: 4) Next, make sure to select the Use a URL option at the top and enter your Facebook feed URL under the Enter the URL where your file is hosted section: ** username and password should be kept empty 5) Next window allows you to control your update schedule, we recommend a Daily update with the time set a few minutes after your Source schedule setup inside our system, you might need to have a more often schedule in place in case you update your feed more regularly than once a day: 6) Next, you can review your settings, update your Default currency if needed and press the Next button to go to the final screen: 7) In here you can confirm all settings are correct and then press the Upload button which will link it all up for you and start processing the feed the first time: After that, you will be presented with a processing screen which you can close if needed, at that point schedule is being set and Facebook will keep processing all for you automatically. ** Please note, that it's important to make sure you have Replace Schedule in place which is added by default by following the steps above. The reason Replace Schedule is needed is because it's the only type of schedule that will make sure that feed-deleted/excluded items will also get deleted from your Facebook catalog after processing your latest feed, you can make sure you have Replace Schedule in place set up for your source under Catalog > Data sources, in there you can see your feed and the schedule it has setup:

Why Rich Snippets for Reviews can Improve Ranks and how they shape future of the search

Fri, 23 Mar 2012 11:33:33 +0000

There have been many discussion recently about the future of the web search and why it will rely on semanticity. The first moves towards that goal are already defined and can be seen in Rich Snippets implementations but before I will start explaining how to implement Rich Snippets let me describe what they are and what is their true purpose. Rich Snippets are small pieces of code, mainly normalised HTML tag attributes (i.e. item-prop or classes) which once added to your page's source code can help search engines (but not only) to better determine what your page is about. In other words, they help to semantically describe what each page section stands for and to avoid any ambiguous definitions i.e. Avatar the movie vs. avatar the icon. Rich snippets help to explain to search bots i.e. how many reviews a product has received, which otherwise is hard to assume based on not normalised data and just the free-text. Now back to implementation, the rich snippets are very easy to implement as they are just addition to your current HTML source code rather than modification and since they operate in the metaspace of your site they won't change your site design nor user experience but will add a semantic boost to help better understand your sites by bots. So to make it clear let me demonstrate you a simple implementation of the reviews snippets on the following example using HTML5 recommended schema.org. Single review code before implementation:

Nikon D90 SLR Reviewed by Anonymous on Feb 26. D90 - Nice camera I would recommend to friends! Rating: 4.5

Single review code after rich snippets implementation:

Nikon D90 SLR Reviewed by Anonymous on Feb 26. D90 - Nice camera I would recommend to friends! Rating: 4.5

Once we have rich snippets for reviews implemented on our pages its worth to add one more thing - Review-aggregate rich snippets attributes which will count average rating and add a star rating to our organic results in Google: The Review-aggregate rich snippets are even easier to implement than rich snippets for single reviews.

Show 979 user reviews for Nikon D90

Once your implementation is deployed you can test all with Google Rich Snippets Testing Tool. More pieces of information and examples can be found on Google's site or on Schema.org - in the Review and AggregateRating sections. There is also one more benefit of implementing rich snippets for reviews as the reviews count and average rating can be included into your Google Shopping product data feed which will make your products more attracted to the potential buyers.