Direct answer: before replacing the search engine or adding a chatbot, replay failed queries and separate five causes: incomplete catalog data, unrecognized customer language, conflicting filters, unavailable supply, and poor result ordering. Fix data and rules first. Use AI when it improves intent interpretation or relevance without dropping essential constraints. Validate orders and contribution per eligible session, not merely fewer empty screens.
For a CMO, this is a particularly frustrating leak: advertising brought someone who stated what they wanted, the item is available, and the store returns an unhelpful answer. This is a conversion project, not automatically a technology purchase. Unlike recommendations, which suggest what to explore, search answers declared intent. Its first obligation is to respect that request before promoting a higher-margin item.
Map the failure before choosing a model
- Missing from the index: check publication, searchable fields, freshness, and product-to-variant mapping. Owners: catalog and engineering.
- Unrecognized vocabulary: compare customer terms with product names and attributes. Owners: merchandising and content; AI may propose equivalences for approval.
- Filters eliminate valid choices: reproduce category, size, price, market, and inherited filters. Owners: product and search.
- Genuinely unavailable offer: check the sellable variant, stock, and delivery coverage. Owner: commerce; explain the limit or present a valid alternative.
- Results exist but are unhelpful: inspect relevance, position, image, price, and clarity. Owners: experience and merchandising; test ranking changes.
Algolia identifies filters as a possible cause of searches with no results. Shopify separates no-result and no-click reports and notes that its search reports exclude predictive-search interactions. A single dashboard therefore may not cover every discovery surface. Specify which surfaces, queries, and events enter the denominator before comparing rates.
An example: a “65W USB-C charger”
Suppose a buyer enters that phrase and the store has a suitable charger, but wattage is absent from the indexed attribute. AI query rewriting does not repair the missing fact. Dropping “65W” to return more products could surface a lower-powered option that fails the requirement. Verify the specification from an authoritative product source and the sellable variant first. If device compatibility remains ambiguous, expose verified compatibility information or ask one useful question rather than claiming certainty.
For a US storefront, test everyday terms, model numbers, dimensions, and US size conventions against real queries. Do not silently treat US and UK sizes as interchangeable. Preserve exact codes and hard constraints, even when a semantic match looks plausible. When no reliable match exists, explain that and label alternatives clearly. An honest, useful no-match response is better than a plausible result that produces a return.
Climb only as far as the problem requires
- 1. Data and configuration: repair attributes, indexing, variants, visibility, and filters.
- 2. Existing capabilities: test typo handling, approved equivalent terms, and available controls in the current engine.
- 3. Assisted relevance: use AI to cluster queries, propose catalog improvements, and evaluate candidates with review.
- 4. Semantic or hybrid retrieval: compare natural-language interpretation with the current baseline, preserving exact matching where required.
- 5. Conversation: ask a question only when ambiguity materially blocks a useful choice; do not turn every search into an interview.
Current Shopify documentation describes semantic understanding already included in native search and informed by product information. Before purchasing another layer, establish which engine your storefront actually uses and which capabilities are already active. Separate relevance from commercial promotion: merchandising boosts should not conceal a mismatch with the request. AI must not invent availability, benefits, materials, compatibility, or delivery promises.
Run a six-week pilot in one category
In week one, choose a category with sufficient demand, known contribution economics, and accessible catalog data. Collect 30 days of queries, filters, results, clicks, and orders; remove personal information visitors may have typed. In week two, build an evaluation set covering common queries, long-tail needs, typos, exact codes, filters, and cases with no valid offer. Category experts define acceptable products and constraints that cannot be relaxed. The same AI should not be the sole judge of its own output.
During weeks three and four, repair basic causes and test a small number of interventions alongside the current flow. In weeks five and six, run a controlled experiment where volume permits, assigning visitors consistently to avoid mixing versions. Predefine duration, the primary metric, and limits for latency, errors, and incompatible results. If traffic is too low, use offline evaluation and a gradual release as operational evidence, not proof of causal revenue lift.
Deliver a cause-based diagnosis, prioritized backlog, corrected attributes, evaluation set, query-linked events, versioned configuration, dashboard, and rollback procedure. Merchandising owns relevance; catalog teams own facts; engineering owns integration and monitoring; analytics owns measurement; commerce owns availability and economics. Marketing contributes customer language but should not inherit sole responsibility for running search.
Measure the whole journey and protect contribution
Capture the query, filters, engine version, displayed products and positions, search identifier, click, and business outcome. Algolia requires queryID for search-related events to enter its click and conversion calculations. Field names vary across stacks; the operating principle is to retain the connection from query to exposure to outcome. A received event that cannot be tied to search can create a misleading diagnosis.
- Diagnosis: no results, no clicks, reformulation, and abandonment by cause and device.
- Relevance: evaluated queries with an acceptable option among the first results, plus hard-constraint violations.
- Operations: latency, index freshness, failures, cost per query, and review effort.
- Business: orders and contribution per eligible session, with cancellations and returns observed after sufficient maturation.
- Guardrails: slower search, incompatible products, extra clicks without orders, and disproportionate exposure for promoted items.
Do not simply compare people who search with people who do not: their intent may differ. Model pilot value as eligible sessions × incremental order-rate improvement × average contribution per order, minus operations, technology, and amortized implementation cost. Use company evidence or explicitly labeled scenarios for every input. Eliminating empty results by returning anything does not establish recovered sales.
What changes the investment
Cost grows with variants, languages, markets, inventory freshness, attribute quality, frontends, integrations, and response-time requirements. A well-described category may need configuration alone; a fragmented catalog needs product and data work before AI. Compare exact-match coverage, relevance controls, diagnostic logs, experiment isolation, fallback behavior, and reversibility. There is no universal price or automatic case for replatforming.
This work deepens conversion without replatforming at https://makinai.co/insights/en/ai-improve-website-conversion-without-replatforming, complements recommendations at https://makinai.co/insights/en/ai-product-recommendations-ecommerce-conversion-margin, depends on reliable availability at https://makinai.co/insights/en/inventory-aware-ai-marketing-paid-media-crm, and benefits from the experiment discipline at https://makinai.co/insights/en/ai-marketing-capacity-valid-experiment-operating-system.
Bring an anonymized query sample, applied filters, and the products that should have appeared to a discussion of https://makinai.co/services/en/crm-ecommerce-commerce-transformation. MAKINAI can help scope one category, identify the dominant cause, and test the smallest intervention that could improve conversion. The map and pilot are editorial recommendations; the sources describe platform behavior, not client outcomes or guaranteed growth.