Retail search zero-result analyst
Turn failed on-site searches into specific catalog fixes.
- For
- Specialist online retailers
- Solves
- Unanswered site searches reveal unmet needs but are ignored.
- Delivers
- Search failure merchandising brief
- Built in
- about 4 weeks of creation time, MVP in 5 days
- Investment
- $13,500 for the MVP, $46,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For specialist online retailers, turn aggregated internal search logs and product catalog into search failure merchandising brief.
- Group zero-result queries.
- Detect spelling variants.
- Match existing products.
- Flag missing synonyms.
- Identify unanswered intent.
- Export merchant actions.
What goes in, what comes out
- Aggregated internal search logs
- Product catalog
AI drafts, people review. Evidence-backed analysis and reporting workspace.
- Search failure merchandising brief
How it works
The workflow
- InStart with
Aggregated internal search logs and product catalog
- 1
The buyer creates a project
- 2
Supplies aggregated internal search logs and product catalog
- 3
Confirms scope and access
- OutFinish with
Search failure merchandising brief
AI does the heavy lifting, people stay in charge
Cluster intent without inventing stock or demand certainty. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.
What your team sees
Key screens: Query clusters, Catalog gaps, Merchant actions. Open with a compact overview and filters for the relevant period or segment. Let users drill from each theme or metric into underlying records. Keep source definitions and missing-data notes near the result. Use an action panel to assign investigations and record what was learned. Open with query clusters; move into catalog gaps for the detailed task; finish in merchant actions for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.
Accounts and administration
Dataset permissions, field mappings, metric definitions, source drill-down, saved filters, reviewer annotations, recurring reports and action ownership. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.
Integrations and data access
Approved brand material, campaign exports and authorized customer research. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. Begin with uploads and exports of aggregated internal search logs and product catalog. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
How we build it
We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.
- 1
Scoping call
Day 1Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.
- 2
MVP
5 daysOne buyer segment, one recurring use case; first modules: group zero-result queries; detect spelling variants. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
6 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
10 daysSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.
Why we start with an MVP
An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.
- Pick the riskiest assumption. Here: will specialist online retailers use it to solve "unanswered site searches reveal unmet needs but are ignored"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
- Measure, then decide. Track resolved zero-result queries and merchant acceptance. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Costed pilot: Aggregated queries; no individual profiling. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: group zero-result queries; detect spelling variants. Support the third task through an assisted review queue: match existing products. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of search failure merchandising brief. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.
After the MVP. After paying customers repeatedly accept search failure merchandising brief, automate flag missing synonyms; identify unanswered intent; export merchant actions. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. Aggregated queries; no individual profiling.
What the build depends on. Stable identifiers, consistent metric definitions, deterministic calculations, source lineage and representative review samples. Poor coverage must remain visible. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: Aggregated queries; no individual profiling.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: group zero-result queries; detect spelling variants. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$46,000about 4 weeks of creation time · start with the MVP from $13,500
Running costs per month
A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $80–$160 | $110–$220 |
| Full productabout 50 customers | $110–$210 | $880–$1,750 | $990–$1,960 |
Run it or resell it
For your own team
Specialist online retailers run it inside the business: aggregated internal search logs and product catalog in, search failure merchandising brief out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#272891 - accent
#bcc954 - surface
#e4e5f1 - ink
#22201e
- Headings
- Fraunces
- Text
- Inter
- Voice
- Energetic, specific, results-minded
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 500-2,000 for an initial analysis of one bounded dataset. Offer USD 250-1,000 monthly for repeat reporting at agreed volume. Data cleanup and specialist analysis are separately priced. These are test ranges. For this buyer, package the first sale around analyze one month of searches and the defined search failure merchandising brief. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Message to test
Turn failed on-site searches into specific catalog fixes. Demonstrate the result with analyze one month of searches for specialist online retailers. Use a concrete before-and-after example without promising unmeasured savings.
Where to find buyers
Retail merchandising groups and e-commerce agencies
Lead magnet
Analyze one month of searches
The first 30 days
- Week 1: interview five prospective buyers from specialist online retailers and inspect how they handle unanswered site searches reveal unmet needs but are ignored.
- Week 2: prepare analyze one month of searches using authorized or synthetic material.
- Week 3: share the demonstration through retail merchandising groups and e-commerce agencies and seek one bounded paid pilot.
- Week 4: measure resolved zero-result queries and merchant acceptance, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.
Paid pilot
Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run analyze one month of searches and deliver search failure merchandising brief. Compare resolved zero-result queries and merchant acceptance with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.
Success metrics
Resolved zero-result queries and merchant acceptance
Retention and expansion
Build repeat use around search failure merchandising brief. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on resolved zero-result queries and merchant acceptance. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.
Why clients would pick it
Domain-specific definitions, trusted source mappings and a history connecting findings to actions and observed results. For this concept, accumulate permissioned examples and reviewer corrections around turn failed on-site searches into specific catalog fixes. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.
Alternatives and positioning
Analysts, business intelligence dashboards, spreadsheets and general text summarization tools. Position this concept around turn failed on-site searches into specific catalog fixes. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.
Main delivery costs
Data preparation, reconciliation, classification, expert interpretation, customer-specific definitions and recurring reporting support. Initial validation additionally budgets for catalog cleanup and analyst review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
Safeguards
Verify product claims and permissions. Distinguish observed campaign results from causal explanations and keep customer data collection authorized. Aggregated queries; no individual profiling. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.