Experiment specification

Experiment spec: delivery date and fee on the basket page

Hypothesis

Showing the delivery date and fee on the basket page, rather than at the final checkout step, raises the share of basket sessions that end in an order. We expect fewer last-step abandons because the fee is no longer a surprise. The counter-risk is more exits at the basket, so the primary metric must count both.

Randomisation

• Unit: logged-in user ID, not session or device. 31% of buyers use both app and web, and buyers average 3.8 sessions a month. Session-level assignment would show one person both experiences and make observations non-independent.
• Split: 50/50 by salted hash of user ID, the same arm on app and web. Users are assigned on first basket view.
• Independence: use a different salt from the Payments squad's "Pay later" test, so every arm is about 50% Pay-later (see Calendar).
• Checks: run an A/A check in the week before launch. Run a sample-ratio-mismatch (SRM) test throughout, and pause if p < 0.001.

Metrics

Primary: order conversion = basket-page sessions that end in an order ÷ basket-page sessions, with standard errors clustered by user.

Do not use orders ÷ checkout sessions. If the fee scares people off at the basket, they never become checkout sessions, the denominator shrinks, and the treatment looks better than it is.

Secondary: - Basket→checkout-start rate (expected to fall, and we need to see by how much). - Checkout-start→order rate (the squad's mechanism). - Revenue per basket session. - Orders per user over the window. - Platform split (exploratory only).

Guardrails: - Average order value (AOV, baseline $64): fail if the lower CI bound is below −2% (−$1.28). - Revenue per basket session: fail if significantly negative. - Fee-related support contacts per 1,000 orders. - Basket-page error rate and latency. - 14-day repeat rate among exposed users. The 30-day repeat rate (38%) can't be read in a 3-week test.

Baseline from the traffic file

• Weekly sessions are about 260k and daily volumes are stable, so I use p = 6.35%.
• Platform split: app 57–58% of sessions at about 7.1% conversion; web 42% at about 5.3%.
• Weekends run at about 65% of weekday volume, so the test must cover whole weeks.

Sample size

• MDE: +3% relative, which is +0.19pp (6.35% → 6.54%). That is roughly 490 orders a week, or about $31k a week at $64 AOV, which is worth shipping for.
• Settings: two-sided α = 0.05, power 80%, so (1.96 + 0.84)² = 7.84.
• Per arm, ignoring clustering: n = 2 × 7.84 × p(1−p) / δ²
• p(1−p) = 0.0635 × 0.9365 = 0.0595
• δ = 0.00191, so δ² = 3.63×10⁻⁶
• n = 0.932 / 3.63×10⁻⁶ ≈ 257k sessions
• Clustering: users contribute about 2.7 sessions in 3 weeks. I assume an intra-user correlation of 0.2, giving a design effect of 1 + 1.7 × 0.2 ≈ 1.3. That puts the requirement at 257k × 1.3 ≈ 334k sessions per arm.
• Duration: each arm gets about 130k sessions a week (260k ÷ 2), so 334k ÷ 130k = 2.6 weeks. Run 3 full weeks (21 days), which gives about 390k per arm.
• For comparison: a 2% MDE needs 578k × 1.3 = 751k per arm, or 5.8 weeks, which the calendar can't fit. A 5% MDE needs 92k × 1.3 = 120k per arm, under 1 week.
• Subgroups: web alone (42% of traffic) can only detect about 4.6% relative (3% × √(1/0.42)). Platform cuts are exploratory.

Data issues to resolve before launch

1. Buyer count doesn't match the traffic file. The data team's 412k buyers in 30 days is inconsistent with about 70k orders a month in the file (16.4k a week × 30/7). Their 3.8 sessions per buyer implies about 1.57M sessions, against about 1.11M in the file. I sized from the traffic file. The data team needs to reconcile the two, since the user-level assumptions (the design effect) depend on it.
2. Check the definition of `checkout_sessions`. I assumed it equals basket-page sessions. If it only counts sessions that reached checkout, the true basket-level baseline is lower and the test is longer. For example, at 4% conversion, n scales by (0.96/0.04) ÷ (0.9365/0.0635) ≈ 1.6×, which is about 4.5 weeks.
3. Re-run the sums on the real basket-session baseline before launch, and fix the duration then (3 or 4 weeks).

Calendar

• Black Friday: promotions start 20 Nov and code freeze starts 16 Nov. Promo traffic isn't representative, and anything not shipped by 15 Nov waits until after the freeze.
• Waiting for Pay later to end (9 Nov) doesn't work. That leaves 10–15 Nov, which is 6 days and not enough.
• Pay later (until 9 Nov) sits on the checkout page and affects the same outcome, so it can't be ignored. I'll run the test concurrently with an independent split. Each arm then gets about the same Pay-later mix, so the contrast stays unbiased. I'll pre-register a treatment × Pay-later interaction check, and report the effect by Pay-later arm. The Payments squad must freeze their allocation for the duration.
• Search ranking (until 30 Oct) only touches search results pages. It's balanced by randomisation and needs no action, but I'll note it.
• Schedule:
• Build, QA and A/A: 5–11 Oct.
• Run 12 Oct – 1 Nov (3 weeks).
• Readout 2–4 Nov, decision before 9 Nov.
• Ship ramp before the 16 Nov freeze.
• Buffer: a 4-week run (to 8 Nov) still ends before Pay later does.

Decision rule

This is fixed in advance, with one analysis at the end of the run and no peeking.

• Ship if the primary lift is positive with the 95% CI excluding 0, and no guardrail fails, and revenue per basket session isn't significantly negative.
• Do not ship if the CI includes 0. We'd conclude "no detectable effect at +3%". Any wish to ship for fee-transparency reasons is a separate product decision, not an experiment win.
• Kill if the primary is significantly negative, or if AOV or revenue per basket session breaches its guardrail. Basket exits are the main thing we expect to see here.
• Extend: only if an outage or traffic shortfall leaves fewer than 334k sessions per arm, up to 28 days. Never extend because the result is "almost significant".
• Interaction: if the effect differs materially by Pay-later arm, ship only to the configuration that will be live after 9 Nov, and confirm with the Payments squad which one that is.

Waymark checkout experiment spec

Hypothesis

Showing the delivery date and fee on the basket page will increase the share of eligible shoppers who place an order by setting expectations earlier and reducing last-step abandonment. It may also cause some shoppers to leave sooner; the primary metric must capture both effects.

Design and randomisation

Randomise eligible shoppers 50:50 when they first view a basket with delivery information available:

• Control: Current experience; date and fee appear at the final checkout step.
• Treatment: Date and fee appear on the basket page and remain visible through checkout.

Assign by stable customer ID across app and web, so shoppers who use both platforms see the same variant. Use a persistent anonymous ID for guests and merge it with the account on login. Stratify assignment by platform at first exposure.

Metrics

• Primary: Order conversion among eligible basket visitors: shoppers placing an order divided by shoppers randomised. This captures both earlier exits and improved final-step completion.
• Secondary: Checkout-start rate; completion among checkout starters; step-by-step abandonment; time to order; AOV; results by app/web.
• Guardrails: Basket-to-order conversion must not fall by more than 0.3 percentage points; monitor cancellations, fee-related support contacts, payment failures and site/app errors.

Sample size and duration

The traffic file covers 56 days. Summing its daily rows gives approximately 2.09m checkout sessions and 132k orders:

• Baseline checkout-session conversion: 132k ÷ 2.09m ≈ 6.3%
• Average traffic: 2.09m ÷ 56 ≈ 37.3k sessions/day, or 18.65k/arm/day at 50:50.

Power for a 5% relative lift (6.3% to 6.615%), with 80% power and two-sided 5% significance:

\[ n_{\text{per arm}}\approx \frac{2(1.96+0.84)^2(0.063)(0.937)}{(0.00315)^2} \approx 93{,}000 \]

Because shoppers can generate repeated sessions, conservatively inflate by the supplied 3.8 checkout sessions per buyer per month (worst-case design effect, assuming fully correlated sessions):

\[ 93{,}000 \times 3.8 \approx 353{,}000 \text{ sessions/arm} \]

\[ 353{,}000 \div 18{,}650 \approx 18.9 \text{ days} \]

Plan for at least 19 days, including full weeks and weekends, then allow time for order outcomes to mature. This is a provisional calculation: the file reports checkout sessions, not eligible basket visitors, and therefore cannot establish the primary metric’s baseline or power. Before launch, confirm basket-visitor volume and conversion, and validate the repeat-session assumption against deduplicated users. The supplied 412k monthly buyers and session-frequency figures should also be reconciled with observed traffic.

Calendar

Do not run concurrently with Payments’ checkout experiment, which ends 9 November. Starting 10 November would require about 19 days, crossing both the 16 November code freeze and 20 November Black Friday promotions. Defer until those changes and promotions are over and the freeze is lifted; the calendar does not specify those end dates. Confirm a stable window of at least three weeks before scheduling.

Decision rule

Ship only if the primary conversion lift is statistically significant at two-sided p < .05, the 95% confidence interval excludes zero, and the basket-to-order guardrail is not breached. Otherwise, do not ship; investigate platform and funnel-step results before proposing a follow-up. Don’t stop early for a positive result; stop only for a pre-agreed severe guardrail issue or data-quality failure.

Experiment: delivery transparency on the basket

Status: Proposed; launch requires a basket-level sizing check and agreement with Payments. The supplied traffic supports a planning estimate, not a defensible final user-level sample size.

Hypothesis and treatment

Showing the next-day delivery date and fee on the basket increases completed orders by removing a late surprise. It may instead move abandonment earlier, so success must be measured from basket exposure, not checkout entry.

• Control: Date and fee first appear at the final checkout step.
• Treatment: Show the same date and fee on the basket, before the checkout button. Retain the final-step confirmation.
• No changes to fees, delivery availability, payment options or other copy. Recalculate both displays consistently if the basket or address changes.

Eligibility and randomisation

Randomise eligible signed-in customers 50:50 at their first basket view, before rendering either experience. Eligibility requires an address sufficient to quote delivery accurately.

Use a persistent customer-ID assignment shared across app and web. Session/device randomisation would contaminate exposure: 31% of buyers use both platforms. Anonymous customers are excluded unless we can reliably preserve and reconcile assignment.

Use an independent experiment hash, balanced within existing Payments assignment and first-exposure platform. Persist assignment throughout the experiment.

Analyse intention-to-treat: include everyone assigned, including customers who never enter checkout. Each customer contributes once to the primary outcome.

Metrics

Primary: Percentage of assigned customers placing at least one order within 24 hours of first eligible basket exposure, on either platform.

Secondary: - Checkout entry within 24 hours. - Basket-to-order conversion and final-step abandonment, with explicit funnel denominators. - Orders and revenue per assigned customer within 24 hours. - Order value, descriptive only: treatment can change who buys. - Repeat ordering within 30 days of assignment, reported after maturation. The supplied 38% is buyer-conditional, not this metric’s baseline.

Guardrails, precommitted proposed tolerances: - Revenue per assigned customer: no more than 1% relative deterioration. - Cancellation/refund incidence per assigned customer: no more than 0.2 percentage-point deterioration. - Checkout technical-error incidence per assigned customer: no more than 0.1 percentage-point deterioration. - Basket p95 latency: no more than 100 ms deterioration. - Any incorrect fee/date presentation is a stop-and-fix issue.

Instrument assignment, basket exposure, checkout steps, displayed quote and orders. Check sample-ratio mismatch, cross-platform consistency and event completeness before interpreting results.

Sample size and duration: sums

Across the supplied 56 days:

Average checkout traffic:

\[ 2,083,390/56=37,203\text{ sessions/day}. \]

Planning assumptions: two-sided α=5%, 80% power, 5% relative lift as the minimum detectable effect—not an observed or commercially validated threshold.

Using checkout conversion only as a provisional proxy:

\[ p=132,196/2,083,390=0.063452,\quad \Delta=0.05p=0.003173. \]

Approximate equal-arm sample:

\[ n=\frac{2(1.96+0.84)^2p(1-p)}{\Delta^2} \approx92,600\text{ per arm}. \]

Thus approximately 185,200 independent observations. Naively:

\[ 185,200/37,203=4.98\text{ days}. \]

Five days is not a valid duration. These are checkout sessions, not unique eligible basket visitors; checkout entry is affected by treatment. The 3.8 sessions/month figure applies only to buyers, so dividing total sessions by 3.8 would not repair the denominator. Nor do 412,000 monthly buyers establish eligible traffic.

Pre-launch requirement: Measure deduplicated eligible basket visitors, their 24-hour conversion, and expected new enrolments over the planned window. Recalculate using that conversion rate; separately check guardrail power. Lock the largest required sample before launch.

Proposed maximum enrolment is 28 days, representing:

\[ 28\times37,203\approx1,041,695 \]

checkout sessions as a volume benchmark—not a count of independent participants. Launch only if forecast eligible customers support the locked sample within that window.

Calendar

Propose 12 October–8 November 2026, four complete weeks, with primary outcomes matured by 9 November.

This overlaps Pay Later. Obtain Payments agreement for independent allocation, retain its assignment in analysis, and estimate the treatment-by-payment interaction. Otherwise, do not use this window.

Search ends 30 October; log its assignment and account for calendar-date changes.

The uncontaminated pre-promotion window is only 10–19 November: ten days. The 16 November freeze requires any running experiment and kill switch to be preapproved; it is not automatically a shutdown. Use that alternative only if revised sizing supports it. Otherwise postpone until a confirmed post-promotion window.

Decision rule

At the locked endpoint, ship if the primary lift’s two-sided 95% confidence interval excludes zero positively and all guardrails pass their precommitted non-inferiority checks. Review platform and Payments interactions before broad rollout.

Reject on clear conversion harm or a guardrail breach. Otherwise call it inconclusive—not “no effect.” No efficacy peeking or opportunistic extension; safety monitoring remains continuous.