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 spec: earlier delivery-date and fee disclosure

Status: Conditional launch approval. The supplied traffic supports a planning calculation, but not a defensible final sample size for the correct primary metric.

Hypothesis and experience

Showing the next-day delivery date and actual delivery fee on the basket page increases completed purchases by reducing late surprises. Earlier abandonment may increase; the test must measure the net effect from basket entry, not just conversion among people who proceed to checkout.

• Control: Current disclosure at the final checkout step.
• Treatment: Disclose on the basket page; retain an accurate fee/date in the final order summary. No changes to prices, delivery eligibility or payment options.
• Eligibility: identifiable, signed-in customers opening a non-empty basket for which we can calculate the date and fee. Do not introduce a login requirement. Results initially apply only to this population.

Randomisation and analysis

Assign eligible customers 50:50 by persistent account-ID hash, immediately before their first eligible basket renders. Keep assignment sticky across sessions, app and web.

Cross-platform consistency matters: \(412,000 × 31\% = 127,720\) recent buyers used both platforms. Device- or session-level assignment would expose many people to both variants.

Analyse intention-to-treat: everyone assigned remains in their arm, including failed renders and immediate exits. Each customer contributes one primary observation. Pre-specify adjustment for entry platform and enrolment date; report app/web effects as secondary, not independently powered launch decisions.

Validate allocation, identity stitching, exposure events and order attribution before starting.

Metrics

Primary: Percentage of assigned customers placing a paid order within 24 hours of first eligible basket entry, on either platform. This captures both earlier exits and downstream gains without a treatment-selected checkout denominator.

Secondary: - Basket-to-checkout progression and final-step abandonment, as funnel diagnostics. - Orders and net revenue per assigned customer within 24 hours. - Order value among purchasers, labelled a conditional diagnostic. - Thirty-day repeat purchase; the supplied 38% is context, not necessarily this cohort’s baseline. This needs a separate mature readout.

Guardrails — proposed tolerances, requiring sign-off: - Net revenue/customer: no worse than −1% relative. - Payment-error and cancellation/refund incidence per assigned customer: no worse than +0.1 percentage points, using fixed, pre-registered observation windows. - Zero tolerance for confirmed incorrect fee/date disclosure or unintended charges. - Monitor basket latency, crashes and sample-ratio mismatch; material regressions trigger investigation or rollback.

The $64 average order value alone cannot establish revenue variance or power these guardrails.

Sample size and duration: sums

Across the supplied 56 days:

Thus:

\[ p_{\text{checkout}}=132,196/2,083,390=6.345\% \]

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

Propose 80% power, two-sided 5% significance, 3% relative minimum detectable uplift, subject to commercial sign-off.

Illustrative checkout-session calculation only:

\[ \Delta=0.06345×0.03=0.0019035 \]

That is 0.190 percentage points.

\[ n_{\text{arm}}\approx \frac{2(1.96+0.84)^2p(1-p)}{\Delta^2} \approx257,200 \]

\[ N\approx514,400;\quad514,400/37,203=13.83\text{ days} \]

Two complete weeks would provide approximately:

\[ 2,083,390/8×2=520,848\text{ checkout sessions} \]

This does not prove the proposed experiment is powered. The intervention precedes checkout, and checkout sessions are neither eligible basket visitors nor independent customers. The 412,000 figure counts buyers only; 3.8 sessions/month describes buyers, not all eligible visitors. Dividing session traffic by 3.8 would be invalid.

Launch-blocking data request: Extract deduplicated eligible basket entrants, their 24-hour purchase rate and cumulative unique entrants over candidate windows. Recalculate:

\[ n{\text{arm}}= \frac{15.68pb(1-pb)}{(0.03pb)^2} \]

Choose a fixed enrolment duration covering at least two complete weeks and enough unique customers, then add 24 hours for primary-outcome maturation.

Calendar

There is no unconditionally clean two-week pre-promotion slot after Payments ends:

• Payments runs through 9 November.
• 10–15 November offers only six days before freeze.
• Even if running tests may continue during freeze, 10–19 November offers only ten days before promotions.

Conditional slot: 31 October–13 November, outcomes mature 14 November, readout 15 November. Search has ended. This requires Payments to hold its configuration through follow-up and agree independent, orthogonal assignment, balanced payment strata and interaction analysis. It also requires the recalculated customer sample to fit.

Otherwise, negotiate isolation earlier or defer until after promotions and freeze; their end dates are not supplied.

Decision rule

No efficacy peeking or early “winner” calls. At the fixed endpoint, ship only if the primary 95% confidence interval excludes zero positively, the estimated uplift meets the agreed 3% business threshold, and guardrail confidence bounds exclude their harm margins. Require valid instrumentation and no material platform/payment interaction harm.

Otherwise retain control: distinguish evidence of harm from an inconclusive result. Missing power, immature guardrails or calendar approval means no launch, not an assumed pass.