Choose a practical task and work through the inputs, decisions and checks needed to complete it. These guides help ecommerce, retail-media and analytics teams move from an observed problem to a testable plan with clear ownership and a defined measure of success.

• How to reduce zero-result searches: Diagnose zero-result searches using genuine query events, catalogue checks and constraint-aware recovery. Measure useful results without hiding failed searches.
• How to launch a retail media network: Build a retail media launch plan covering inventory, suppliers, eligibility, measurement and billing. Use readiness gates and a practical pilot checklist.
• How to measure retail media incrementality: Plan a retail media holdout with stable assignment, comparable outcomes and visible uncertainty. Separate incremental sales from attributed ROAS and brand halo.
• How to monetise ecommerce site search: Plan sponsored search with eligibility, query coverage and charged-event economics. Test shopper outcomes and keep retargeting scope clearly defined.
• How to evaluate ecommerce site search: Evaluate ecommerce search with representative queries, calibrated relevance labels and clearly defined metrics. Validate commercial impact with a planned live test.
• Increase sponsored-search fill rate on long-tail queries: Diagnose sponsored-search coverage by query, traffic and failure reason. Separate relevance gaps, advertiser demand, budgets and delivery before increasing fill.
• Audit ecommerce search merchandising rules: Find conflicting search boosts, outdated pins and global rules. Use a practical audit to protect shopper intent while keeping merchandising decisions measurable.
• Build or buy ecommerce search? A three-year cost model: Compare the full cost of building or buying ecommerce search: engineering, infrastructure, data, merchandising, measurement and migration over three years.
• Ecommerce search migration checklist: test, switch and roll back: Plan an ecommerce search migration with catalogue and event parity, query evaluation, a controlled release and explicit rollback criteria for your team.
• Store-specific grocery search: stock, assortment and availability: Design grocery search around store assortment, local offers and stock freshness. Explore a worked data model, changed-store behaviour and availability checks.
• How to measure supplier-funded promotion incrementality: Separate the effect of an offer from paid visibility. Plan holdouts, calculate incremental orders and reconcile supplier funding, fees and contribution.
• Why retail media campaigns underspend: a diagnostic checklist: Find why retail media campaigns underspend. Check status, eligible products, budgets, bids, pacing and delivery before changing campaign settings.
• Retail media billing and reconciliation checklist: Reconcile retail media bookings, delivery, billable amounts, invoices and cash. Use a worked example with clear owners for credits, makegoods and disputes.
• Sponsored recommendations on product pages: similar, complementary and bundle placements: Design sponsored product-page recommendations for alternatives, accessories and complements. Define eligibility, compatibility, billing and measurement.
• Store-level availability in retail media: avoid advertising unavailable products: Design sponsored-product eligibility around store, fulfilment and stock freshness. Define stale-data behaviour, campaign permissions and delivery checks.
• Exact SKU and part-number search alongside semantic search: Test exact SKUs, model numbers and descriptive product queries together. Use a labelled fixture to expose identifier collisions, false matches and weak retrieval.
• Product recommendations for new products with no sales history: Handle recommendation cold start for new products, shoppers and sites. Compare catalogue evidence, images, contextual signals and carefully measured fallbacks.
• Dietary and attribute search in grocery: enforcing shopper constraints: Design grocery search with verified dietary attributes, explicit exclusions and careful missing-data handling. Separate semantic relevance from eligibility.
• Compatible accessory recommendations for electronics retailers: Build accessory recommendations around verified product compatibility, clear evidence and useful basket context. Explore a data model and practical test fixtures.
• How to make Shopify metafields searchable: Map Shopify product and variant metafields, sync the right data and test search and facets with examples for materials, specifications and compatibility.
• Price matching vs dynamic pricing vs price affirmation: Compare public repricing, policy-approved price matching and evidence-based price affirmation. Choose the right action with a practical retail decision matrix.
• Sync retail media campaigns with live promotions: Design a promotion-feed contract for retail media with product scope, effective dates, funding controls, expiry behaviour and reconciliation keys.
• Programmatic ecommerce landing pages without duplicate or empty pages: Build useful ecommerce collection pages with clear demand, valid assortments, unique guidance, controlled facets, canonical rules and retirement decisions.
• How to A/B test ecommerce search and recommendations: Design credible tests for search and recommendations: randomisation, sample size, exposure, uncertainty and commercial guardrails, with clear decision rules.
• How to use an LLM as a judge for ecommerce search: Build a calibrated LLM judge for ecommerce search. Define intent facets, create reference labels, test agreement and preserve evidence before trusting scores.
• How to enrich product attributes for ecommerce search: Improve product attributes for search with a controlled AI enrichment workflow. Define evidence, review uncertain values and map approved data into your catalogue.
• How to measure brand visibility in AI shopping: Build a repeatable AI-shopping visibility study with defined prompts, surfaces, identities and denominators. Separate observed presence from traffic and sales.
• How to improve retail media yield: Diagnose retail media yield across eligibility, demand, engagement, charges and collected cash. Use a weekly review table with explicit denominators.
• An ecommerce search pilot playbook: Plan an ecommerce search pilot with a baseline, catalogue checks, instrumentation, relevance tests and rollback criteria. Decide rollout using agreed evidence.
• How to implement closed-loop retail media attribution: Connect retail media events to purchases with explicit identity, scope, windows, deduplication and returns. Reconcile attributed ROAS without calling it iROAS.
• How to troubleshoot SKU visibility in ChatGPT shopping: Diagnose product and variant visibility in ChatGPT shopping with identity, offer and feed checks. Separate integration errors from a product absent in one result.
• How to build a dynamic joint business plan: Build a joint business plan that can adapt through approved changes. Keep promotion funding, media budgets, evidence and ownership clear with a planning table.
• Retail MCP product search with Adaptive Transformer Search: Connect Adaptive Transformer Search to ChatGPT, Claude and onsite assistants through PA's open-source MCP server, with retailer-controlled ranking and checkout.
• Sponsored products in agentic and conversational AI: How retailers run sponsored products inside the AI assistants they operate: relevance-first ranking, labelling, CPM and CPC pricing, pacing and holdout measurement — and why ChatGPT and Claude apps are discovery-only.
• How to monetise an AI shopping assistant: To monetise an AI shopping assistant, connect it to a product search that returns relevant sponsored products, label them in answers, cap how many appear,.
• How to launch Sponsored Prompts: Launch Sponsored Prompts by choosing one AI surface, inviting advertisers to pick products or collections, setting the relevance threshold and sponsored.
• How to run AI ads without keywords: To run AI ads without keywords, let the advertiser pick products and score each product against the meaning of every incoming request. High-scoring.
• How to label sponsored products in AI answers: Label sponsored products in AI answers with a visible word such as "Sponsored" next to each product, say it in voice answers, and instruct the assistant.
• How to measure ads in conversational commerce: Measure conversational ads by defining an impression, click and sale for chat, reporting them separately from site inventory, and running holdout tests in.
• How to sell CPM in AI chat: To sell CPM in AI chat, define an impression as a sponsored product shown and labelled in a delivered answer, cap sponsored products per answer, forecast.
• Retail media and ChatGPT apps: what is allowed: Why a retailer's ChatGPT app is a product-discovery integration, not an ad surface: OpenAI's rules prohibit advertising in apps, so sponsored products run on assistants the retailer operates.
• How to add retail media to a voice assistant: Add retail media to a voice assistant by limiting answers to one or two options, naming any sponsored option as sponsored, and asking the shopper to.
• How to build a brand AI discovery app: A brand AI discovery app is a conversational app built on a brand's own recipes, routines, how-to content and products, showing live price and stock at.
• How to connect retailer feeds for live price and stock: List the retailers that stock your products, map each retailer's product feed to your catalogue, and refresh price and availability so the app only.
• How to fund ads that surface a brand app: Pick the products or app content to promote, add optional include and exclude terms, choose a pricing option and a budget. The app card shows only when.
• How to choose prompts and exclusions for a brand campaign: Start with no terms and let meaning-based scoring find relevant requests. Add include terms for themes you must be considered for, and exclude terms.
• How to measure brand AI ads with holdout tests: Hold back a random share of matching requests from the sponsored placement and compare sales between exposed and held-out shoppers. The difference is.
• How to show local stock and pickup in AI answers: Connect store-level stock and price feeds from participating retailers, match them to the shopper's location, and offer pickup today only where the.
• How to run AI ads for an electronics brand through retailers: Fund sponsored prompts that surface your products or brand app in relevant AI answers, with live stock and price from the retailers that sell your.

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