Spark playbook scenarios · Spark: AI Store Assistant

Identify patterns in customer purchase behavior

A merchant treats customers similarly despite meaningful differences in purchase recency, frequency or value. In this case, the immediate task is to identify patterns in customer purchase behavior. The workflow should produce a customer segment table and interpretation guide, using the merchant's actual records and constraints.

Topic

Customer Analytics and Segmentation

Target keyword

shopify customer purchase behavior

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 identify patterns in customer purchase behavior.
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 stable customer IDs, purchase history, refunds, segment definitions and analysis date]. Define mutually understandable segments, calculate membership from supplied history and explain edge cases and refresh rules.
Return a customer segment table and interpretation guide 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 stable customer IDs, purchase history, refunds, segment definitions and analysis date.

Step 2

Define mutually understandable segments, calculate membership from supplied history and explain edge cases and refresh rules.

Step 3

Deliver a customer segment table and interpretation guide; 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.

Why does the analysis date matter for customer segments?

Recency and inactivity depend on the date of measurement. The same customer can change segments over time.

Are customer segments permanent labels?

No. They are views of behavior under chosen rules and should be refreshed as new data arrives.

What information is needed to investigate this use case?

Start with stable customer IDs, purchase history, refunds, segment definitions and analysis date. 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.

Customer Analytics and Segmentationshopify returning customer segmentation