Step 1
Start with customer behavior, product purchase cycle, program constraints and available benefits.
A merchant wants repeat business but lacks a clear retention proposition or eligibility rules. In this case, the immediate task is to design a loyalty proposition with clear eligibility. The workflow should produce a retention plan and evaluation framework, using the merchant's actual records and constraints.
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.
Act as a Shopify operations specialist.
Help me design a loyalty proposition with clear eligibility.
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 behavior, product purchase cycle, program constraints and available benefits].
Identify relevant retention opportunities and propose a program or experiment with clear eligibility, costs and success measures.
Return a retention plan and evaluation framework 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.Start with the business context, the trigger, and the expected output before moving into the execution steps.
A merchant wants repeat business but lacks a clear retention proposition or eligibility rules. In this case, the immediate task is to design a loyalty proposition with clear eligibility. The workflow should produce a retention plan and evaluation framework, using the merchant's actual records and constraints.
Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Start with customer behavior, product purchase cycle, program constraints and available benefits.
Identify relevant retention opportunities and propose a program or experiment with clear eligibility, costs and success measures.
Deliver a retention plan and evaluation framework; verify the result against the supplied records and identify unresolved inputs.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
No. Product experience, need and customer fit still influence whether someone returns.
Repeat revenue can rise while the program becomes uneconomic if benefits cost more than the value they create.
Start with customer behavior, product purchase cycle, program constraints and available benefits. 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.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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Ciwi Spark builds Shopify-native AI tools and agents around the workflows merchants actually run: research, ads, translation, and customer support. Stop rewriting the same prompts by hand.