ShopperProof
Run audit

Customer call guide

Run better AI shopper review calls.

Use this script to help a Shopify merchant or agency client decide whether the findings are commercially meaningful, what to fix, and how to rerun the same scenario after changes.

Open audit workspaceRun a free scan first
1 scenario
Approved in the customer's own words
3 fixes
Specific enough to ship or assign
1 rerun metric
Clear enough to prove improvement

Live flow

A five-part call script

Keep the call focused on buyer behavior, evidence quality, and fix decisions.

5 min

1. Set the commercial goal

If an AI assistant sent you a qualified shopper this month, what buying question would matter most?

  • Pick one live store and one product category.
  • Name the customer type, purchase goal, constraints, and competitors.
  • Agree on what success means: recommendation, citation, add-to-cart confidence, or checkout handoff.
7 min

2. Compose the scenario in the customer's words

Describe the shopper as if they were asking ChatGPT, Gemini, Perplexity, or Claude for help.

  • Keep the wording close to the customer's language.
  • Include constraints that change the purchase decision, such as size, ingredients, shipping, returns, warranty, or subscription terms.
  • Avoid broad prompts like 'find a good product' when a real buyer would be more specific.
10 min

3. Review the agent outcome

Where did the agent find enough proof, and where did it hesitate or choose a competitor?

  • Check whether the store was recommended, cited, or ignored.
  • Separate site usability friction from missing product or policy evidence.
  • Ask whether the finding feels commercially meaningful or like a low-value metric.
10 min

4. Turn findings into fix decisions

Which fix would you actually ship before asking an AI shopper to try again?

  • Prefer specific fixes: product attributes, comparison copy, policy clarity, schema, FAQs, or checkout handoff details.
  • Capture owner, priority, and expected proof for each fix.
  • Reject vague fixes such as 'improve SEO' or 'make the page better.'
8 min

5. Rerun and compare

If these fixes were live, what score or behavior would prove the store became more agent-ready?

  • Rerun the same prompt after changes or proposed fixes.
  • Compare win rate, fit score, evidence quality, citation rate, and remaining blockers.
  • Decide whether the report is strong enough to forward internally.

Decision checkpoint

Would this finding change what the merchant fixes next week?

If the answer is no, tighten the scenario or skip the finding. If the answer is yes, capture the fix owner and rerun metric before the call ends.

yes

The report earns its place when it produces a change the team is willing to ship and measure.

Objections

Questions to ask when the customer pushes back

The goal is not to defend every score. The goal is to find the evidence gaps worth fixing.

The score feels subjective.

Ask which individual evidence gap they disagree with, then separate scoring debate from whether the missing fact would help a buyer decide.

We already have SEO and schema tools.

Position the audit as outcome testing: SEO and schema improve inputs, while this checks whether agents can complete the buying task.

Agents are not a meaningful traffic source yet.

Use the report as readiness insurance: the same fixes often improve product clarity, customer support, paid landing pages, and marketplace feeds.

We cannot change the theme quickly.

Prioritize merchant-controlled content first: product copy, FAQ blocks, policy pages, metafields, structured data, and comparison content.

Follow-up email

Send a decision recap

After the call, send a short summary that makes the next rerun obvious.

  • The scenario we tested
  • The buyer question the store failed or passed
  • The evidence agents used
  • The top three fixes
  • The metric to rerun after fixes
  • The owner and target date for each fix