Experiment specification

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 Specification: Early Delivery Fee & Date Preview

Author: PM, Checkout Squad Date: October 2026

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1. Hypothesis

Showing the delivery date and delivery fee on the basket page (pre-checkout) will reduce final-step checkout abandonment by setting upfront expectations. * Core Hypothesis: The reduction in late-stage drop-off outweighs any early-stage basket abandonment caused by friction, resulting in a net positive impact on overall checkout conversion.

2. Randomisation Strategy & Unit

• Unit of Randomization: User ID (sticky across app and web platforms, given 31% multi-platform usage).
• Allocation: 50% Control (status quo: fee shown on final checkout step), 50% Treatment (fee and date visible on the basket page).

3. Metrics

• Primary Metric: Checkout Conversion Rate (Completed Orders / Unique Checkout Sessions).
• Secondary Metrics:
• Basket-to-Checkout Drop-off Rate (users leaving before entering checkout).
• Average Order Value (AOV).
• Guardrail Metrics:
• Total Orders / Gross Merchandise Value (GMV).
• 30-day Repeat Purchase Rate.

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4. Traffic Analysis & Sample Size Sizing (The Sums)

Real Traffic Baseline (from past 8 weeks data): * Summing the 8-week dataset (56 days): * Total App Checkout Sessions = 1,304,784 (~23,300/day) * Total Web Checkout Sessions = 959,183 (~17,128/day) * Total Combined Sessions (8 weeks) = 2,263,967 * Daily Average Traffic = 40,428 checkout sessions/day * Total Orders = 155,752 * Baseline Conversion Rate ($p$) = $155,752 / 2,263,967 \approx 6.88\%$

Sample Size Calculations: To detect a relative change of 2% in conversion rate (e.g., from 6.88% to 7.02%) with $\alpha = 0.05$ (two-tailed) and power $(1-\beta) = 0.80$: * Using standard sample size formulas for proportions, we require approximately 375,000 users per variant (~750,000 total sessions). * At our baseline traffic of ~40,428 sessions/day (split 50/50 = ~20,214 sessions per variant daily): * Required Duration = $750,000 / 40,428 \approx 18.5$ days. * We will round up to 3 full weeks (21 days) to cleanly account for day-of-week seasonality (grocery ordering behaves very differently on Saturdays vs. Mondays).

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5. Calendar & Timing

• Overlaps & Conflicts:
• Search squad: Runs exclusively on search results pages. Zero overlap with checkout/basket flows. Safe to run concurrently.
• Payments squad ("Pay later"): Runs on the checkout page (affecting 50% of users) until 9 November. Because the checkout page is downstream of our intervention (basket page), running both creates a multi-variate interaction risk if not accounted for.
• Holiday/Promo Constraints:
• Black Friday promotions start November 20.
• Code freeze begins November 16.
• Proposed Window:
• Start: Monday, October 12, 2026
• End: Monday, November 2, 2026 (Exactly 21 days).
• Note: This completely clears the Payments squad end-date (Nov 9) by launching earlier, and sits safely clear of the November 16 code freeze.

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6. Decision Rule

We will evaluate the experiment after exactly 21 days of data collection.

1. Ship (Rollout 100%): If the Primary Metric (Checkout Conversion Rate) shows a statistically significant positive lift ($p < 0.05$), and no guardrail metrics (GMV, 30-day repeat rate) drop significantly.
2. Iterate / Inconclusive: If the primary metric shows no statistically significant change ($p \ge 0.05$), but secondary metrics show positive intent shifts, review basket abandonment drop-offs and redesign the UI placement.
3. Rollback: If the Primary Metric drops significantly ($p < 0.05$) due to early basket abandonment outweighing final-step savings, or if guardrail metrics degrade, immediately halt the experiment and revert to control.

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