Experiment specification

Experiment spec: delivery date and fee on the basket page

Owner: Checkout PM · Status: Draft. Blocked on one data question (see 0).

0. Data question to resolve before launch

The two sources disagree badly:

• CSV: about 16,525 orders a week, or about 70,800 per 30 days.
• Data team: 412,000 people ordered at least once in 30 days, so at least 412,000 orders.
• Sessions: 412,000 × 3.8 = about 1.57M checkout sessions a month. The CSV shows about 1.12M.

The CSV may cover one region or a subset, or the definitions may differ. I've sized the test on the CSV because it is the conservative figure. If real traffic is about 6× higher, the test still runs the full 4 weeks to cover the weekly cycle. It would simply detect a smaller effect.

1. Hypothesis

Showing the delivery date and fee on the basket page removes the "surprise fee" at the final step. That reduces last-step abandonment and increases orders per shopper.

The counter-risk is that some shoppers leave at the basket instead. The test measures the net effect of both.

2. What's randomised and how

• Unit: account ID, 50/50. It is not the session. Buyers average 3.8 checkout sessions a month, and 31% use both app and web. Session-level randomisation would show the same person both versions and contaminate the result.
• Assignment point: first basket-page view in the test window, with the assignment held for the whole test.
• Logged-out web baskets: use the device cookie, then map to the account at login. Accounts that end up in both arms are excluded, and the rate is reported.
• Platforms: both, with app and web as pre-registered segments.
• Run first: a 2-day A/A check for sample-ratio mismatch.

Why the metric can't be "orders ÷ checkout sessions": the treatment changes who starts checkout. People who leave at the basket drop out of the denominator, which would inflate the treatment's rate. The denominator must be fixed at the point of assignment.

3. Metrics

Primary: orders per assigned account during the test window.

Secondary (diagnostic): - Basket → checkout start rate. Expected to fall; this is the risk the squad named. - Final-step abandonment rate. Expected to fall. - Basket → order conversion, split by app and web. - Share of baskets topped up to reach a free-delivery threshold, if one exists.

Guardrails (any breach blocks shipping): - Average order value (baseline $64): no drop of more than 2%. - Delivery-fee revenue per order: no significant drop. Must be explained if it moves. - Basket-page load time (p75): no increase of more than 100 ms. Basket-page error rate: no increase. - Customer contacts about fees or delivery date: no significant increase. - Sample-ratio mismatch: p > 0.001.

Note: the 30-day repeat rate (38%) can't be read inside the window. It becomes a follow-up read 30 days after the test ends.

4. Sample size and duration

Baseline from 8 weeks of CSV data (10 Aug – 4 Oct):

• Weekly volume is very stable: about 260,424 sessions and 16,525 orders a week.
• Weekdays run about 40% above weekends, so the test must run whole weeks.

Minimum detectable effect: a 3% relative lift, from 6.345% to 6.535% (+0.190 pp). At current volume that is about 496 orders a week, or about $31.7k a week at $64 average order value.

Calculation (α = 0.05 two-sided, 80% power):

• n per arm = (1.96 + 0.84)² × [p₁(1−p₁) + p₂(1−p₂)] / d²
• = 7.84 × (0.05942 + 0.06108) / 0.0019035²
• = 0.9447 / 3.623×10⁻⁶
• ≈ 260,750 sessions per arm, or 521,500 in total

Adjusting for repeat users: randomising by account means sessions from the same user are correlated. I've applied a design effect of 1.5 because the true clustering factor is unknown:

• 521,500 × 1.5 = 782,000 sessions
• ÷ 260,424 a week = 3.0 weeks

Planned duration: 4 full weeks. The extra week covers uncertainty in the clustering factor. It also leaves enough power for a directional read on app and web separately (each about 0.5–0.6 of total traffic).

The analysis will use account-level orders with variance computed directly from the data, so the 1.5 design effect is not baked into the result.

5. When it can run

Waiting until Pay later ends (10 Nov) leaves only 6 days before the freeze, which is too short. So we run alongside it.

Dates: - Build and A/A check: 5–11 Oct. - Test: Mon 12 Oct – Sun 8 Nov. That is exactly 4 weeks. It ends the day before Pay later finishes, so Pay later's 50/50 split is constant for our whole test. - Analysis and ship decision: 9–13 Nov, before the freeze.

6. Decision rule (pre-registered)

1. Ship if the primary lift is positive and significant (p < 0.05), no guardrail is breached, and sample-ratio mismatch passes. 2. Don't ship if: - the primary is significantly negative, or - any guardrail is breached.

Either way, investigate whether the basket-stage drop outweighed the last-step gain. 3. Flat result (confidence interval within ±3%, no guardrail breach): ship only if final-step abandonment fell significantly, on the grounds of transparency. Otherwise keep control. 4. Pay later interaction: if the effect differs significantly between Pay later arms, report the effect within each arm. The ship decision then follows Pay later's own launch decision. 5. No early stopping on the primary metric. A guardrail breach above a pre-set threshold may stop the test early.

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.