Spark playbook scenarios · Spark: AI Store Assistant

Compare conversion rates across traffic channels

A merchant has visits but insufficient orders and cannot identify which part of the buying journey needs attention. In this case, the immediate task is to compare conversion rates across traffic channels. The workflow should produce a conversion diagnosis and experiment backlog, using the merchant's actual records and constraints.

Topic

Conversion Analysis and Optimization

Target keyword

shopify conversion rate by channel

Copyable AI prompt

A prompt you can drop directly into Spark

First replace every [bracketed] placeholder with real store scope and constraints, then add any actual records or data you have, and paste it into your preferred AI assistant or Spark agent. Spark will follow this page's workflow to produce structured output and verification steps.

ai-prompt.txt

Act as a Shopify operations specialist.
Help me compare conversion rates across traffic channels.
My store context is [describe the business, products and target audience].

My scope is [specify the affected products, pages, customers or reporting period]. Use these inputs: [provide matched-period sessions, funnel events, orders, devices, channels, products and recent changes]. Align metric definitions, compare segments, locate the largest evidenced losses and propose prioritized experiments with success and guardrail metrics.
Return a conversion diagnosis and experiment backlog for the specified task.
Show the relevant evidence or before-and-after examples and explain how I can verify the result.
If essential data is missing, ask for it and mark unsupported conclusions as unknown.
Do not invent metrics, product claims or customer facts.
For any publication, message, account or store change, use only connected tools within my explicitly authorized scope and report success only after verification; otherwise provide the draft or proposed steps.

Use in Spark

Scenario & problem

When does this scenario usually come up?

Start with the business context, the trigger, and the expected output before moving into the execution steps.

How to solve

A recommended resolution workflow

Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.

Step 1

Start with matched-period sessions, funnel events, orders, devices, channels, products and recent changes.

Step 2

Align metric definitions, compare segments, locate the largest evidenced losses and propose prioritized experiments with success and guardrail metrics.

Step 3

Deliver a conversion diagnosis and experiment backlog; verify the result against the supplied records and identify unresolved inputs.

FAQ

Common questions when running this scenario

Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.

Does higher traffic necessarily produce more orders?

No. Traffic quality, product fit and checkout conditions affect the share of visitors who buy.

Why compare similar periods and segments?

Changes in campaign mix, seasonality or device distribution can move the overall rate even when an individual page has not worsened.

What information is needed to investigate this use case?

Start with matched-period sessions, funnel events, orders, devices, channels, products and recent changes. These inputs establish the relevant scope and help separate an actual issue from missing information or an unsuitable comparison. Without them, the result should remain a proposed approach rather than a confirmed diagnosis.

Related scenarios

Continue with adjacent or same-topic scenarios

Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.

Conversion Analysis and Optimizationshopify conversion funnel analysis