Experiment specification

Experiment spec: delivery fee in basket

Owner: PM, Checkout | Readers: Ravi (build), Ines (analysis), Chloe (approval)

Hypothesis

Showing the delivery fee in the basket reduces surprise at checkout (12% of last quarter's support contacts) without reducing orders. Chloe's concern is that an early fee deters people before they start checkout, so the test measures that directly.

Changes from the team draft

Design

• Control: fee first shown at final checkout step (today).
• Variant: basket shows the delivery line: £3.99, or "Free" at £60+. Nothing else changes. The basket and checkout amounts must always match.
• Unit: customer ID, with a 50/50 split. Assignment is sticky for the whole test. Logged-out users are bucketed by a persistent device ID (Ravi to confirm how many there are).
• Enrolment: at first basket view during the test. Log an exposure event at that moment.

Metrics

Primary: share of enrolled customers who place at least one order within 7 days of first basket view. Baseline is about 8%.

Guardrails: - Checkout start rate (baseline about 12.9%, i.e. 8% ÷ 62%). - Revenue per enrolled customer (baseline about £3.76, i.e. 8% × £47). - Delivery-fee support contacts per 1,000 customers. This is the benefit metric, but volumes are low, so read it directionally.

Diagnostics (not decision metrics): - Checkout completion (orders ÷ checkout starts). Read it only alongside the primary metric, since it's conditional on starts. - New vs returning customers, and baskets under vs over £60. Both are exploratory.

Sample size and duration

• Smallest effect worth detecting: 0.5 percentage points on orders (8.0% → 8.5%), per Chloe.
• Requirement: 80% power, 5% two-sided significance. This needs about 46,000 customers per arm, roughly 92,000 in total.
• Time to reach it: Week 1 brings about 40,000 unique customers. After that, only about 20,000 new customers enrol per week, because returning customers are already assigned. That reaches about 100,000 by the end of week 4.
• Duration: enrol for 4 full weeks, plus 7 days of follow-up for the last cohort. Readout is at about 5 weeks, with no early stopping. Ines can monitor data quality but not results.
• Limit: the test can't reliably detect effects smaller than 0.5pp. A "no significant difference" result means "no effect larger than about 0.5pp", not "no effect".

Data quality checks (before readout)

• Sample ratio check: arms should be within normal variation of 50/50.
• Exposure is logged for every enrolled customer.
• Fee shown in basket equals fee charged, in all cases.

Decision rule (Chloe to confirm)

• Ship if the 95% confidence interval for the change in orders rules out a drop of 0.5pp or more, no guardrail is materially worse, and delivery-fee contacts do not rise.
• Don't ship if orders fall by 0.5pp or more, or revenue per customer drops meaningfully.
• Otherwise, treat as inconclusive and discuss. Do not extend the test to "find" significance.

Next

If the variant ships or is neutral, run a follow-up test of the free-delivery-over-£60 banner, where the fee is already visible. Its effect is likely concentrated in baskets just under £60.

Open items

• Ravi: logged-out identity handling; a flag to switch the variant off quickly.
• Chloe: confirm the decision rule and that the 0.5pp threshold means absolute percentage points on orders per basket viewer.
• Ines: confirm the 7-day order attribution and the support-contact tagging for fee-related contacts.

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?