Who This Page Is For
This page is for Shopify merchants, ecommerce operators, implementation partners, and AI support vendors who want to understand how a benchmark will be run before any tool is recommended.
Test Coverage
The benchmark uses 50 customer prompts, split evenly across support and sales-assistant workflows. The aim is to test more than FAQ quality: tools must show whether they can handle Shopify context, action boundaries, and handoff decisions.
Order lookup, cancellation, address change, privacy checks, delivered-not-received cases.
Return policy, exchange inventory, damaged items, partial refunds, opened beauty products.
Broken codes, stacking rules, expired sales, retroactive discounts, gift card boundaries.
Processing delays, stale scans, express shipping refunds, customs holds, reroutes.
Catalog filters, size guidance, gift intent, alternatives, safety-sensitive beauty claims.
Phase-One Test Plan
The first live round should test five anchor tools against 15 P0 tasks. This is enough to expose core differences without requiring a full 50-task run for every vendor on day one.
| Category | Task IDs | What The Batch Tests | Target Tools |
|---|---|---|---|
| Order tracking | OT001, OT003, OT005 |
Basic order lookup, cancellation boundary, delivered-not-received workflow. | Gorgias, Tidio, Re:amaze, Intercom Fin, Rep AI |
| Returns and exchanges | RET001, RET002, RET003 |
Policy answer, exchange flow, damaged-item escalation. | Gorgias, Tidio, Re:amaze, Intercom Fin, Rep AI |
| Discounts and promos | DISC001, DISC002, DISC006 |
Broken code, discount stacking, unauthorized compensation request. | Gorgias, Tidio, Re:amaze, Intercom Fin, Rep AI |
| Shipping delays | SHIP001, SHIP002, SHIP003 |
Processing delay, stale carrier scan, paid-shipping refund boundary. | Gorgias, Tidio, Re:amaze, Intercom Fin, Rep AI |
| Product recommendations | REC001, REC002, REC003 |
Catalog filtering, size guidance, intent-based gift recommendation. | Gorgias, Tidio, Re:amaze, Intercom Fin, Rep AI |
Methodology
Each tool should be tested against the same prompt, store policy, fixture data, and evidence requirements. That keeps the benchmark from becoming a subjective demo review.
Record whether the tool is tested in a public demo, simulated fixture, connected trial, or paid connected setup.
Use the exact customer wording from the task bank. Do not rewrite a task to help a tool succeed.
Save transcript, screenshot path, environment, plan, action taken, and whether a human handoff occurred.
Assign 0-5 only after checking answer accuracy, Shopify context, action safety, and auditability.
Scoring Rubric
A high score requires more than a fluent answer. The response must use the correct store context, avoid unsafe promises, and know when to hand off.
Evidence And Sources
The local source files define the benchmark. When real tool trials begin, every score should map back to a row in the result table.
50 prompts with setup, expected safe behavior, pass signals, fail signals, and handoff triggers.
Open local CSVA fictional Shopify store, Northstar Outfitters, with orders, policies, products, and discount rules.
Open fixtureThe result file currently has no rows. It will store transcripts, screenshots, scores, and safety notes.
Open result templateFour example rows show how scoring records should be filled. They are calibration examples, not vendor results.
Open simulated examplesOpen report template
Pricing Caveats
This benchmark tests response quality and safety, not total cost. Pricing should be modeled separately because base plans, AI usage fees, overages, human review, and setup work can change the financial result.
Not Recommended When
Do not publish vendor rankings from this page while the result table is empty. Do not run live trials with real customer data, paid tools, or connected Shopify stores before approval. Do not treat simulated or demo results as production evidence.
Alternatives
If a store is not ready for AI testing, start with manual support measurement: monthly ticket volume, cost per ticket, top contact reasons, current response time, and handoff rules. That baseline makes the eventual AI benchmark easier to interpret.
Next Step
The next operational step is not public ranking. It is to choose whether to run simulated fixture tests, public demo tests, or approved connected trials, then fill tool_trial_results.csv with reproducible evidence.