Experiment specification

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.

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: Upfront Delivery Fee & Date on Basket

Squad: Checkout Author: PM, Checkout Date: October 5, 2026

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1. Hypothesis & Context

• Hypothesis: Displaying the delivery date and delivery fee upfront on the basket page (instead of at the final checkout step) will reduce unexpected cost friction at checkout, decreasing last-step abandonment and increasing overall basket-to-order conversion.
• Risk: Showing fees earlier may induce immediate drop-off on the basket page, reducing the number of checkout sessions started.
• Core Question: Does the increase in final checkout completion compensate for any drop in basket-to-checkout progression?

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2. Randomisation & Allocation

• Unit of Randomisation: User ID (with persistent Device/Visitor ID fallback for non-logged-in users).
• Why: Data shows 31% of monthly buyers shop on both the app and the website, and users average 3.8 checkout sessions/month. Randomising by session or device would cause severe cross-platform contamination and inconsistent user experiences.
• Split: 50% Control, 50% Treatment, stratified by platform (iOS, Android, Web).
• Variants:
• Control (A): Current flow. Delivery date and fee revealed on final checkout step.
• Treatment (B): Delivery date and calculated fee dynamically displayed on the basket page prior to entering checkout.

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3. Metrics Framework

* Primary Metric: * Basket-to-Order Conversion Rate (Orders / Unique Basket Sessions): Note: We must not use Checkout-to-Order CVR alone as the primary metric, because filtering out users on the basket page could inflate downstream checkout conversion while reducing total orders. * Secondary Metrics (Funnel Diagnostics): * Basket-to-Checkout Initiation Rate: Measures upfront drop-off risk (Basket views $\to$ Checkout step 1). * Final-Step Checkout Abandonment Rate: Anticipated to drop as fee surprises are eliminated. * Average Order Value (AOV): Baseline is $64; monitors whether upfront fees alter basket composition. * Guardrail Metrics: * Total Orders & Revenue per User: Must not see a statistically significant decrease. * Basket Page P95 Latency: Fee lookup API must not add $>150\text{ ms}$ to basket load time. * CS Contact Rate: Delivery fee disputes/inquiries per 1,000 orders.

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4. Traffic Sums, Sample Size & Duration

#### A. Baseline Traffic & Conversion (from 8-week data: Aug 10 – Oct 4, 2026) * Total 8-week volume: * App checkout sessions: $1,202,306$ | App orders: $85,466$ ($7.11\%$ CVR) * Web checkout sessions: $874,188$ | Web orders: $46,756$ ($5.35\%$ CVR) * Combined totals: $2,076,494$ checkout sessions | $132,222$ orders * Weekly averages: * Sessions: $\approx 259,560$ sessions/week ($\approx 37,080$ sessions/day) * Orders: $\approx 16,528$ orders/week ($\approx 2,360$ orders/day) * Baseline Checkout Conversion Rate ($p$): $$p = \frac{132,222}{2,076,494} = 6.37\%$$

#### B. Sample Size Calculation Using standard two-tailed hypothesis testing parameters: * Significance level ($\alpha$): $0.05$ ($Z{\alpha/2} = 1.96$) * Statistical power ($1 - \beta$): $80\%$ ($Z{\beta} = 0.84$) * Minimum Detectable Effect (MDE): $2.5\%$ relative lift ($\Delta = 6.37\% \times 0.025 = 0.159\%$ absolute $\to p_2 = 6.53\%$)

$$\text{Sample size per variant } n = \frac{(Z{\alpha/2} + Z\beta)^2 \cdot 2 \cdot p(1 - p)}{\Delta^2}$$ $$n = \frac{(1.96 + 0.84)^2 \cdot 2 \cdot (0.0637 \cdot 0.9363)}{(0.00159)^2} = \frac{7.84 \cdot 2 \cdot 0.05964}{0.000002528} \approx 369,980 \text{ sessions/variant}$$

• Total required sessions (Control + Treatment): $\approx 740,000$ checkout/basket sessions.

#### C. Duration * Total traffic per week $\approx 259,560$ sessions. * Required weeks: $740,000 / 259,560 \approx 2.85$ weeks. * To eliminate day-of-week seasonality (e.g., weekend drop to $\approx 28\text{k}$ sessions/day vs. weekday $\approx 41\text{k}$), we set the duration to exactly 3 full calendar weeks (21 days), providing $\approx 778,000$ sessions and achieving an MDE of $\approx 2.4\%$.

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5. Calendar & Schedule

``` [Oct 5] Spec Approval & QA [Oct 12] Launch Experiment (Day 1) ├── Oct 12 – Nov 1: 3-Week Test Window └── Concurrently: Orthogonal to Payments "Pay later" [Nov 1] Conclude Test & Analysis [Nov 2 – Nov 6] Squad Review & Rollout Decision [Nov 9 – Nov 13] 100% Rollout (if successful) [Nov 16] CODE FREEZE for Black Friday ```

• Constraints & Conflicts:
• Code Freeze: Begins 16 November.
• Payments Squad ("Pay later" on checkout): Live for 50% of users until 9 November. We cannot wait until Nov 9, as that leaves only 7 days before code freeze.
• Search Ranking Test: Ends 30 October on search result pages only (no direct conflict).
• Interference Mitigation:
• Randomise our test on an orthogonal hash layer to Payments. Because assignment is independent, exposure to "Pay later" will balance evenly (50/50) across both our Control and Treatment groups.
• Check for an interaction effect via a $2 \times 2$ factorial ANOVA post-test.

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6. Decision Rules