Experiment specification

Experiment Spec: Upfront Delivery Fee & Date on Basket Page

1. Hypothesis

Showing the next-day delivery date and delivery fee on the basket page (rather than waiting until the final checkout step) will reduce sticker-shock abandonment at the end of the funnel and increase overall order conversion by at least 2% relative, without degrading average order value (AOV) or causing excessive early-funnel drop-off.

• Control: Delivery date and fee are revealed on the final checkout step.
• Treatment: Delivery date and fee are displayed directly on the basket page before the user clicks "Proceed to Checkout".

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2. Randomisation & Unit of Analysis

• Randomisation Unit: `user_id` (logged-in account).
• Why: Data shows 31% of buyers use both the app and web in a 30-day period, and the average buyer starts 3.8 checkout sessions/month. Device- or session-level randomisation would expose the same user to both variants, causing UX confusion and contamination. Unauthenticated users will be randomised by a persistent device cookie and stitched upon login at the start of checkout.
• Trigger Point: First view of the Basket Page during the experiment window.
• Split: 50% Control / 50% Treatment, stratified by platform (App vs. Web) since baseline conversion differs significantly between the two.

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3. Metrics

Note on Denominator: Because Treatment alters the basket page—before a `checkoutsession` starts—using `checkoutsessions` as the primary denominator would introduce post-treatment selection bias. The primary denominator must be users reaching the basket.

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4. Traffic, Sample Size & Duration (Show Your Sums)

A. Baseline Traffic from CSV (56 days: 10 Aug – 4 Oct 2026)

• App Totals: 1,196,659 checkout sessions; 85,127 orders $\rightarrow$ 7.11% session CVR (21,369 sessions/day).
• Web Totals: 875,705 checkout sessions; 46,966 orders $\rightarrow$ 5.36% session CVR (15,638 sessions/day).
• Combined Totals: 2,072,364 checkout sessions; 132,093 orders.
• Daily Averages: 37,006 checkout sessions/day and 2,359 orders/day.
• Weekday vs. Weekend split: Weekdays average ~40,800 sessions/day; weekends average ~28,100 sessions/day. (Must run in full 7-day increments).
• Session-to-Order Baseline CVR: $132,093 / 2,072,364 = \mathbf{6.374\%}$.

B. Converting Sessions to Unique Users

* Each buyer averages 3.8 checkout sessions per 30 days (~0.95 sessions/week). * In a 14-day window: $37,006 \text{ sessions/day} \times 14 = 518,084 \text{ checkout sessions}$. * Accounting for repeat sessions (~1.6 sessions per user over two weeks), a 14-day window yields approximately 323,800 unique users entering checkout: $$518,084 / 1.6 \approx 323,800 \text{ unique users (161,900 per arm)}$$ * User-level Baseline CVR ($p1$) over 14 days: $$\text{Total 14-day orders} = 2,359 \times 14 = 33,026$$ $$p1 \approx 33,026 / 323,800 \approx \mathbf{10.20\%}$$ (Even if analysed at the session level where $p_1 = 6.374\%$, we calculate both below to ensure statistical power).

C. Sample Size Calculation

Using standard two-tailed parameters: $\alpha = 0.05$ ($Z{\alpha/2} = 1.96$), Power $1 - \beta = 0.80$ ($Z\beta = 0.84$), so $(Z{\alpha/2} + Z\beta)^2 = (1.96 + 0.84)^2 = \mathbf{7.84}$. Target Minimum Detectable Effect (MDE): 2.5% relative lift.

1. At the Session Level ($p_1 = 0.06374$, $\Delta = 0.06374 \times 0.025 = 0.00159$): $$n = \frac{2 \times 7.84 \times 0.06374 \times (1 - 0.06374)}{(0.00159)^2} = \frac{0.9358}{0.00000253} \approx \mathbf{370,000 \text{ sessions/arm}}$$ * With cluster inflation (design effect $\approx 1.3$ for 1.6 sessions/user): $370,000 \times 1.3 = 481,000 \text{ sessions/arm}$ ($962,000$ total). * Days required: $962,000 / 37,006 \text{ sessions/day} = \mathbf{26.0 \text{ days}}$.

2. For a 14-Day Run ($518,084$ total sessions $\rightarrow 259,042$ sessions/arm, or $161,900$ users/arm): * At user level ($p1 = 0.102$, $n = 161,900$/arm): $$\text{MDE}{\text{abs}} = \sqrt{\frac{2 \times 7.84 \times 0.102 \times 0.898}{161,900}} = \sqrt{\frac{1.436}{161,900}} = 0.00298 \text{ (or } \mathbf{2.92\% \text{ relative lift}}\text{)}$$

Duration Decision: Run for 14 full days (2 weeks) to capture two full weekly cycles and detect a $\ge 2.9\%$ relative lift in user conversion ($\sim 68$ extra orders/day, or ~$4,350/day incremental revenue).

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5. Calendar & Scheduling

• Constraints:
• Search squad's ranking test ends 30 Oct (low interaction risk, upstream of basket).
• Payments squad's "Pay later" test runs on the checkout page at 50/50 until 9 Nov (high interaction risk: changes payment friction on the exact step our test targets).
• Code freeze starts 16 Nov; Black Friday promos start 20 Nov.
• Schedule:
• Waiting until 9 Nov leaves only 7 days before the 16 Nov code freeze—insufficient for a 14-day run.
• Therefore, we will run orthogonal (cross-factored) to the Payments "Pay later" test for 14 days: Monday 26 October – Sunday 8 November 2026.
• Implementation: Use an independent hash seed so our 50/50 split is evenly distributed across Control and Treatment of the "Pay later" experiment (25% in each of the 4 cells). This leaves a full week (9–15 Nov) to analyse results and ship or roll back before the 16 Nov code freeze.

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6. Decision Rule

1. Ship: Primary metric (User CVR from Basket) is statistically significantly positive ($p < 0.05$, two-tailed), AOV guardrail does not drop by more than 1% ($p < 0.05$), and interaction check with the "Pay later" test shows no severe negative synergy.
2. Kill / Roll back: Primary metric is significantly negative ($p < 0.05$) due to early basket abandonment, OR Revenue per User drops significantly.
3. Inconclusive (Flat): If User CVR is flat ($p \ge 0.05$) and guardrails are unharmed, ship Treatment anyway—upfront fee transparency is a better customer experience and aligns with consumer protection best practices.

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.

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