Experiment specification

Experiment: show delivery fees in the basket

Owner/build: Ravi · Analysis: Ines · Approval: Chloe Decision: Should Basketful show delivery charges before checkout?

Hypothesis and scope

Showing the fee in the basket will reduce fee surprises, but could discourage ordering. Unexpected delivery fees accounted for 12% of support contacts last quarter; this is motivation, not a baseline customer complaint rate.

Test fee visibility only. Do not add the proposed free-delivery promotional banner: it introduces a second change and would prevent us attributing results to earlier disclosure.

Experience

• Control: Current experience; delivery fee first appears at the final checkout step.
• Treatment: Show a clearly labelled delivery line beside the basket total: £3.99 delivery, or Free delivery when eligible. Update the fee and total as the basket changes.
• Use the existing pricing service and eligibility rules. Confirm behaviour at exactly £60 before launch; “over £60” must not silently become “£60 or more.”
• Leave checkout, pricing and other messaging unchanged.

Eligibility and assignment

Include customers viewing a non-empty, orderable basket. Exclude staff, bots and test accounts using rules fixed before launch.

Randomise 50/50 by customer, not session, at their first eligible basket view. Persist assignment across visits and devices where identity is available. For signed-out customers, use a stable first-party identifier; Ravi must document identity merging and cross-device limitations before launch.

Analyse everyone assigned, whether or not they start checkout or successfully see the fee: intention to treat.

Metrics

Primary: Customer order conversion — percentage of assigned customers placing at least one order within seven days of assignment. Report treatment minus control in percentage points, with a two-sided 95% confidence interval.

Checkout completion is diagnostic only: treatment may change who starts checkout, so orders ÷ checkout starts could improve while overall ordering falls.

Secondary/diagnostic: - Checkout-start rate and checkout completion. - Delivery-fee-related support contacts per assigned customer within seven days, using consistent tagging. - Orders and revenue per assigned customer; average order value and free-delivery eligibility as diagnostics.

Guardrails: Contribution margin per assigned customer, if reliably available; otherwise net revenue per assigned customer as an explicitly limited proxy. Also monitor basket/checkout errors. Chloe and Ines must approve numerical economic and reliability tolerances before launch.

Sample size and schedule

Chloe’s smallest worthwhile order-conversion change is 0.5 percentage points: 8.0% to 8.5%, not a 0.5% relative lift.

At 5% two-sided significance and 80% power, detecting that increase requires approximately 48,000 customers per arm, or 96,000 total.

Ines must first verify that the 8% baseline matches the eligibility rules and seven-day outcome window; recalculate and freeze the sample target if necessary.

Do not count returning customers again. Expected recruitment is approximately:

• Week 1: 40,000 unique customers.
• Each subsequent week: 20,000 additional customers.
• Four weeks: approximately 100,000 unique customers.

Enroll for at least four complete weeks and until the frozen sample target is reached, ending at a full-week boundary. Then wait seven days for the final cohort’s outcomes: roughly five weeks to readout.

Monitoring and decision

Ravi validates assignment persistence, fee accuracy, exposure/order linkage and sample-ratio balance before ramp-up.

Daily checks are for instrumentation and predefined safety breaches—not efficacy stopping. No “stop when significant,” sample extensions based on results, or repeated significance decisions.

Ines delivers one final primary analysis. Chloe’s default rollout rule is: - Estimated conversion gain at least +0.5 points; - 95% confidence interval excludes zero; - Guardrails pass.

Otherwise, do not roll out on this result alone. A non-significant result does not prove no harm. Secondary trust improvements inform follow-up, not a post-hoc replacement for the primary success rule.

To: Chloe (Head of Growth), Ravi (Growth Engineer), Ines (Data Analyst) From: PM, Checkout Subject: Experiment Spec: Early Delivery Fee Display in Basket

Team, here is the polished spec for our upcoming checkout experiment. I’ve incorporated Chloe’s threshold, fixed a few methodological traps in the draft, and scoped this for a clean, reliable read.

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1. Objective & Hypothesis

• Current State: Delivery fee (£3.99, free over £60) is only revealed on the final checkout step. 12% of last quarter’s support contacts were fee-related surprises.
• Hypothesis: Showing the delivery fee (and progress to free delivery) directly in the basket increases customer trust and sets clear expectations upfront.
• Expected Impact: While Chloe rightly worries this might cause some upfront drop-off among sub-£60 baskets, we believe the resulting higher-intent checkout starts will lift overall conversion and reduce cart abandonment due to "sticker shock."

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2. Variant Definition

• Control (A): Existing basket experience. Delivery fee hidden until final checkout step.
• Variant (B):
• Displays the delivery fee clearly in the basket summary.
• Note on scope: Per Chloe’s suggestion, we are bundling the "free delivery over £60" progress banner into Variant B. Since the fee and the threshold are conceptually tied, showing one without the context of the other would skew the test.

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3. Traffic Allocation & Unit of Randomization

• Unit of Randomization: User-level (logged-in state or persistent cookie). Correction from draft: Session-level splitting would cause a jarring experience if a user refreshes or returns and sees fluctuating fee visibility.
• Split: 50/50 (Control / Variant).
• Audience: All customers viewing the basket (~40,000 unique users/week, split roughly 50/50 between new and returning).

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4. Metrics

• Primary Metric (Guardrail for Business Value):
• Order Conversion Rate: Completed Orders ÷ Total Basket Views.
• (Correction from draft: The draft proposed "Checkout Completion Rate (Orders ÷ Checkout Starts)." That misses top-of-funnel drop-off. If Variant B scares people away before checkout, measuring only checkout-starts will falsely make the variant look better. We must measure from the basket view).
• Secondary Metrics:
• Checkout Start Rate: Checkout Starts ÷ Basket Views.
• Checkout Completion Rate: Orders ÷ Checkout Starts (to isolate downstream behavior).
• Average Order Value (AOV): To monitor if the £60 threshold banner successfully drives basket-building behavior (baseline: £47).
• Support Metric:
• Volume of delivery-fee-related support tickets (Ines, let’s pull this baseline post-launch).

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5. Success Criteria & Statistical Guardrails

• Minimum Detectable Effect (MDE): Per Chloe, we care about a ≥ +0.5 percentage point absolute change in the Primary Metric (Order Conversion Rate from basket view, baseline ~8%).
• Duration & Peeking:
• Correction from draft: Do not stop the test the moment p < 0.05. Early peeking yields false positives.
• We will run the test for a fixed duration of 2 full weeks to capture day-of-week and new vs. returning user cycles (~80,000 total unique users), yielding adequate statistical power for our MDE.

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Next Steps:

• Ravi: Let’s sync on tracking implementation for the basket-level fee and banner. Target build completion: [Insert Date].
• Ines: Please set up the tracking dashboard, ensuring we segment by New vs. Returning users and Basket Value (<£60 vs. >£60).
• Chloe: Good to approve?

Experiment Spec: Delivery Fee Transparency in Basket

To: Chloe (Approval), Ravi (Build), Ines (Analysis) From: Checkout PM

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1. Objective & Hypothesis

• Context: 12% of support contacts complain about surprise delivery fees at final checkout.
• Hypothesis: Displaying the delivery fee (£3.99, or "Free" if >£60) upfront in the basket will set accurate expectations, reduce support contacts, and build customer trust without reducing net orders.
• Risk (Chloe’s concern): Upfront cost shock could cause drop-off before checkout begins.

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2. Corrections to the Draft Plan

1. Primary Metric Changed: Draft used Checkout Completion Rate (Orders ÷ Starts). Correction: Use Basket-to-Order Conversion Rate (Orders ÷ Basket Viewers). Measuring only checkout starts introduces severe selection bias—if fee visibility deters low-intent users at the basket, checkout completion would artificially increase while overall orders plummet. 2. Unit of Randomization Changed: Draft used session-level. Correction: Randomize by User ID (falling back to persistent anonymous visitor ID). 50% of weekly viewers are repeat shoppers; session-level randomization would expose returning users to conflicting experiences. 3. No Peeking / Fixed Duration: Draft suggested stopping once "significant." Correction: Continuous monitoring with early stopping inflates false-positive rates to >30%. We will fix sample size upfront and run for complete weekly cycles. 4. Scope Control (Chloe’s Banner): Cut the promotional free-delivery banner from this test. Adding a promotional banner introduces a confounding variable, making it impossible to isolate whether fee transparency helped or hurt. We will test the banner in a follow-up experiment.

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3. Variants (For Ravi)

• Allocation: 50/50 split at User/Persistent Device ID level. Triggered on basket view.
• Control (A): Current basket. No delivery fee shown (fee appears on final checkout step as today).
• Variant (B): Add a delivery fee line item to the Basket Order Summary:
• Basket < £60: `Delivery: £3.99`
• Basket ≥ £60: `Delivery: FREE`
• Include delivery fee in the displayed Basket Total.
• Note: Do not add promotional progress bars or marketing banners.

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4. Metrics Framework (For Ines)

• Primary Metric: Basket-to-Order Conversion Rate (Total Unique Ordering Users ÷ Total Unique Basket Viewers).
• Guardrail Metrics:
• Average Order Value (AOV baseline: £47).
• Basket-to-Checkout Start Rate (tracks where abandonment occurs).
• Total Net Revenue (Orders × AOV).
• Secondary / Diagnostic Metrics:
• Delivery-fee-related customer support ticket rate per 1,000 orders.
• Checkout Completion Rate (Checkout Starts → Orders).

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5. Sample Size & Run Time (For Ines & Chloe)

• Baseline Conversion: 8.0% of basket viewers place an order.
• Minimum Detectable Effect (MDE): Absolute ±0.5 percentage points (7.5% or 8.5%, a 6.25% relative change), per Chloe's threshold.
• Statistical Parameters: $\alpha = 0.05$ (two-tailed), Power ($1 - \beta$) = 80%.
• Required Sample: ~47,000 unique users per variant (~94,000 total unique basket viewers).
• Duration: 3 full weeks (21 days).
• With 40,000 viewers/week (~20k new, ~20k repeat), 3 weeks yields ~80,000–90,000 unique basket viewers across three full grocery-shopping cycles.
• Ines will run the read-out strictly after the 21-day window closes.

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6. Decision & Rollout Criteria (For Chloe)

• Ship Variant: Primary conversion does not drop by more than Chloe’s threshold of -0.5pp (non-inferiority) AND delivery-related support contacts decrease significantly.
• Iterate / Re-test: Basket-to-order conversion drops by >0.5pp. In this scenario, early fee disclosure hurts conversion, justifying Chloe’s concern; we then test the £60 threshold banner/progress bar to offset drop-off.
• Kill Immediately: Overall revenue drops >5% statistically significantly at the end of Week 1 (safety check only; not an early win stop).