Experiment specification

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: 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.