Whether customers come back depends on how fast the products run out, but the benefits under discussion were drawn up for a different buying rhythm. 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 aligned with repurchase timing and benefit cost.
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: [observed repurchase intervals by product category, fundable benefits with their unit costs, membership fields on customer records, and the merchant's benefit budget ceiling]. Write eligibility as conditions evaluable from those fields alone, with one qualifying rule per benefit, an explicit handling rule for customers who sit between tiers, and a cost ceiling per member per period so total benefit spend stays inside the merchant's stated budget.
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.Split the work into input, judgment, and deliverable phases. Verify each step's output before moving to the next.
Collect the repurchase interval observed per product category from paid orders, the benefits the merchant can actually fund with their unit cost, and the membership fields already stored on customer records such as account creation date, order count and lifetime spend.
Write eligibility as conditions evaluable from those fields alone, with one qualifying rule per benefit, an explicit handling rule for customers who sit between tiers, and a cost ceiling per member per period so total benefit spend stays inside the merchant's stated budget.
Deliver the retention plan with the eligibility table and an evaluation framework comparing participating members against a matched non-member group over the same period, then run the rules against the customer export and list every customer the records cannot classify.
Cover decision rules, frequent misunderstandings, and required inputs so the team aligns on prerequisites before execution.
A precedence rule written before launch decides, typically the highest tier whose conditions are all met, so the same record always yields the same answer; the lower tier is not granted as well, and the rule is recorded beside the eligibility table.
Benefit timing is set against the interval at which the product actually runs out, so a reminder or reward arrives while the need is live; categories with too few repeat orders inherit the default cycle and are marked unconfirmed rather than given their own schedule.
Yes, but each change carries an effective date and applies from the next qualification cycle so members are not moved mid-period; members who joined under an earlier rule keep it until that period ends, and the change is logged with its reason.
Start with scenarios under the same topic, then expand into cross-topic matches suggested by similarity.
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