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Spark playbook scenarios · Spark: AI Store Assistant

Report customer acquisition and growth over time

Every month someone asks how many customers were won and lost, and the answer depends on who prepares it. In this case, the immediate task is to report customer acquisition and growth over time. The workflow should produce a customer segment table and interpretation guide, with period boundaries drawn from the merchant's first-purchase dates.

Topic
Customer Analytics and Segmentation
Target keyword
shopify customer growth report
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 report customer acquisition and growth over time.
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: [the complete order history with customer identifiers and order dates, each customer's first-purchase date, the reporting periods and analysis date, and the merchant's stated repurchase cycle]. Assign each customer to the period holding their first purchase and to retained or lapsed in later periods using the merchant's stated repurchase cycle, keeping one acquisition record per customer across the whole series so nobody is won twice.
Return a customer segment table and interpretation guide with period totals checked and inconsistencies flagged 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
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.

01

Step 1

Assemble the complete order history with customer identifiers and order dates, take each customer's first-purchase date as the moment of acquisition, and fix the reporting periods and the analysis date before any counting begins.

02

Step 2

Assign each customer to the period holding their first purchase and to retained or lapsed in later periods using the merchant's stated repurchase cycle, keeping one acquisition record per customer across the whole series so nobody is won twice.

03

Step 3

Deliver a customer segment table and an interpretation guide with one row per period, reconcile the sum of acquisitions across all periods against the count of distinct customers in the order history, and flag any period whose boundaries were set differently from the rest.

FAQ

Common questions when running this scenario

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

Which date puts a customer into a period?

The first-purchase date in the order history, taken once and never revised, because using the date of the latest order rewrites earlier periods every time the report is rebuilt; later purchases only decide whether the customer shows as retained or lapsed in the periods that follow.

How is a lapsed customer defined?

By the merchant's stated repurchase cycle rather than a calendar rule: a customer counts as lapsed once the gap since their last order exceeds that cycle, and anyone acquired too recently to have completed a cycle is shown as not yet due instead of lapsed.

How do you know the series is consistent?

Add the acquisitions across every period and compare the total with the number of distinct customers in the order history; they must match, and any gap points to a customer counted twice, a period boundary that moved, or an order stripped of its identifier.

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