Coming soon
Loyalty signup offer test
Compares four checkout offers for joining the loyalty programme, scored on how many first-time buyers actually join.
Authored by Sid Chaudhary
Founder & CEO at Intempt
Agentic workflow
Coming soon1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.
- 1
Set up the loyalty offer test
Coming soonBlu builds a step
About this recipe
When to use it
A loyalty signup ask at checkout only works if the entry offer is compelling enough to act on in that moment. This runs a random split to find which offer actually drives signups.
What it delivers
Runs a client-side experiment with a random traffic split, testing which entry offer drives more loyalty program signups at checkout.
How you prompt this
Describe the outcome in your own words and Blu Agent maps it to this recipe, or run it by name. It drafts a plan first and nothing runs until you approve it.
Tell Blu Agent what you want
“Compares four checkout offers for joining the loyalty programme, scored on how many first-time buyers actually join.”
recipe.md preview
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Inputs & outputs
What the recipe needs before it runs, and what it leaves in your workspace.
Inputs
- Nothing declared beyond an Intempt workspace. Blu asks for anything else it needs as it goes.
Outputs
How the recipe runs
Blu follows the steps in order and shows you each one before anything goes live.
StepWhat happensBuilds
- 01Set up the loyalty offer testCreate a CLIENT EXPERIMENT on /experiences titled "Loyalty Programme Entry Offer". ═══ PATH 1: Top-level configuration ═══ Experience type: client_experiment Variants: - Control (25%): no loyalty prompt at checkout - Variant B (25%): 10% discount on next order for joining - Variant C (25%): 2× points on this order for joining - Variant D (25%): free shipping on next 3 orders for joining Targeting: - Pages: page URL contains "/checkout" - Devices: any - Audience: first-time buyers only: segment definition: Lifetime value = 0 OR orders_count = 0 (this is their first checkout) - Display frequency: once per order Primary metric: goal_completed_in_experience where experience_id = <this> (goal fires on user_tags_added with "loyalty_member" tag within session) Secondary metrics: - 90-day repeat purchase rate per joined cohort (see cohort tagging below: this is the most important downstream signal) - Placed order.total_price on the current order (does the offer increase basket size?) - Per-variant loyalty join rate (loyalty_member tag added / Checkout created within experiment) Guardrail: checkout completion rate must not drop >3% (offer banner shouldn't slow conversion) Schedule: 30 days for the experiment itself; 90 additional days for cohort-tracking the repeat-purchase rate. ═══ Cohort tagging for 90-day repeat purchase measurement ═══ The "90-day repeat purchase rate per variant" is the most informative signal but requires cohort tagging at signup. The recipe expects the loyalty-join firing logic to capture which variant the user joined under: When the user checks the loyalty checkbox in any variant, fire BOTH events together: 1. user_tags_added with tag = "loyalty_member" 2. user_tags_added with tag = "loyalty_offer:b" (or "c" or "d" matching the variant they joined under) The variant-specific tag persists on the user record. Then 90 days later, segment cohorts: - Cohort B = users with both "loyalty_member" AND "loyalty_offer:b" tags - Cohort C = users with both "loyalty_member" AND "loyalty_offer:c" tags - Cohort D = users with both "loyalty_member" AND "loyalty_offer:d" tags - Cohort Control = first-time-buyers in Variant Control who completed checkout (didn't see a prompt) 90-day repeat-purchase rate per cohort = count(users in cohort with Placed order.timestamp > join_timestamp AND Placed order.timestamp < join_timestamp + 90 days) / cohort size. ═══ PATH 2: Variant HTML content (Visual Editor) ═══ Variant: Control (no DOM changes: no prompt) Existing checkout flow. Measures intrinsic loyalty join rate via other channels. Variant: B (10% off next order) HTML target selector: .checkout-summary (insert before submit button) Variant DOM: <div class="loyalty-offer-banner" data-variant="b" data-offer-code="loyalty_offer_b"> <h3>Join our rewards program</h3> <p>Get <strong>10% off your next order</strong></p> <label class="loyalty-checkbox-label"> <input type="checkbox" id="loyalty-join-checkbox" data-variant="b" data-offer-code="loyalty_offer_b" /> <span>Yes, sign me up for [Brand] rewards</span> </label> </div> Variant: C (2× points this order) HTML target selector: .checkout-summary Variant DOM: <div class="loyalty-offer-banner" data-variant="c" data-offer-code="loyalty_offer_c"> <h3>Join our rewards program</h3> <p>Earn <strong>double points on this order</strong></p> <label class="loyalty-checkbox-label"> <input type="checkbox" id="loyalty-join-checkbox" data-variant="c" data-offer-code="loyalty_offer_c" /> <span>Yes, sign me up for [Brand] rewards</span> </label> </div> Variant: D (free shipping next 3 orders) HTML target selector: .checkout-summary Variant DOM: <div class="loyalty-offer-banner" data-variant="d" data-offer-code="loyalty_offer_d"> <h3>Join our rewards program</h3> <p>Get <strong>free shipping on your next 3 orders</strong></p> <label class="loyalty-checkbox-label"> <input type="checkbox" id="loyalty-join-checkbox" data-variant="d" data-offer-code="loyalty_offer_d" /> <span>Yes, sign me up for [Brand] rewards</span> </label> </div> When the user submits checkout with the loyalty-join-checkbox checked, fire user_tags_added twice: once with "loyalty_member" and once with the variant-specific tag from the data-offer-code attribute. The goal_completed_in_experience event fires automatically based on the loyalty_member tag addition. Taxonomy notes: - user_tags_added is a canonical event that fires when tags are added to a user record. - Segment definitions for the 90-day cohort comparison use the tags-on-user-object pattern (Users.tags array contains "loyalty_offer:b" etc.). - The 90-day window is the standard repeat-purchase measurement window for ecom; some categories (apparel) may need 60 days, others (consumables) may need 30. - Cohort Control (first-time buyers who saw no prompt and didn't join) is essential for measuring CAUSAL repeat-purchase lift: without it you only know which offer-cohort returns most often, not whether the program itself moves the needle vs. doing nothing. - The Placed order.total_price secondary metric measures whether the offer affects current-order basket size. Some offers (free shipping next-order) shouldn't move current AOV; some (2× points) might.
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- 2
Connect your data
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- 3
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- 4
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