01
Step 1
Start with customer purchase history, expected buying cycle, prior communications and offer limits.
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Spark playbook scenarios · Spark: AI Store Assistant
Build a targeted customer winback workflow
Previously active customers stop returning and the merchant needs to identify appropriate reactivation opportunities. In this case, the immediate task is to build a targeted customer winback workflow. The workflow should produce an at-risk customer definition and winback plan, using the merchant's actual records and constraints.
Topic
Customer Churn and Winback
Target keyword
shopify customer winback automation
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 build a targeted customer winback workflow.
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 customer purchase history, expected buying cycle, prior communications and offer limits]. Distinguish likely inactivity from normal purchase timing, segment reasons where supported and draft a bounded winback experiment.
Return an at-risk customer definition and winback plan 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.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.
Previously active customers stop returning and the merchant needs to identify appropriate reactivation opportunities. In this case, the immediate task is to build a targeted customer winback workflow. The workflow should produce an at-risk customer definition and winback plan, using the merchant's actual records and constraints.
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.
02
Step 2
Distinguish likely inactivity from normal purchase timing, segment reasons where supported and draft a bounded winback experiment.
03
Step 3
Deliver an at-risk customer definition and winback plan; 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 a long gap always indicate churn?
No. Some categories have long natural purchase cycles. Compare the gap with relevant customer and product context.
Should every inactive customer receive a discount?
No. The reason for inactivity may involve experience, relevance or timing rather than price.
What information is needed to investigate this use case?
Start with customer purchase history, expected buying cycle, prior communications and offer limits. 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.
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Next step
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