Analyse experiment results

Subject: Hold on Monday’s launch — Home Page ("Your usual basket") test readout

Hi Zoe, team,

I dug into the raw data to sanity-check the numbers before Monday. My recommendation is that we do not ship on Monday.

While the top-line numbers initially look like a ~5.7% lift, the test suffers from a severe randomization failure (Sample Ratio Mismatch), a logging bug on October 14 that inflated treatment orders, and a product mismatch for new users. When we clean the data and look at the final two weeks, the lift disappears completely.

Here is the full breakdown and my working.

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1. What the raw data shows (and how we got to ~6%)

If we sum `daily_results.csv` across all 28 days: * Control: 196,353 user-days | 16,580 orders (0.08444 orders/user-day) * Treatment: 226,672 user-days | 20,231 orders (0.08925 orders/user-day) * Apparent Lift: +5.70% ($Z = 5.56, p < 0.0001$) * Guardrail (AOV): Control averaged ~$48.25 vs. Treatment ~$48.00 (-0.52%, well within our -2.0% guardrail).

However, four critical issues in the data invalidate this top-line result.

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2. What kills the top-line conclusion (Showing the working)

#### A. Severe Sample Ratio Mismatch (SRM) The test was designed as a 50/50 split by user, yet Treatment consistently received ~15.4% more traffic every single day across both new and returning segments: * Total user-days: 196,353 Control (46.42%) vs. 226,672 Treatment (53.58%). * Chi-square test against 50/50: $\chi^2 = 2,173, p \ll 0.0001$. Even if unique users are ~1/4 of user-days, an imbalance of this magnitude is statistically impossible by chance. * Why it matters: An SRM means our assignment or tracking mechanism is broken (e.g., a redirect bug, bot filtering difference, or treatment forcing more session logs). Until we know why 15.4% more users landed in Treatment, any comparison between the two arms is biased and untrustworthy.

#### B. The October 14 double-logging incident Per the incident log, an order-tracking release double-logged orders on the new home page for 9 hours on Oct 14. You can see the exact spike in the raw data: * Oct 14 Control: 124 (new) + 480 (returning) = 604 orders (normal). * Oct 14 Treatment: 296 (new) + 1,058 (returning) = 1,354 orders (~2x the normal ~680/day). * Removing Oct 14 (27-day totals): * Control: 189,327 user-days | 15,976 orders (0.08438 orders/user-day) * Treatment: 218,565 user-days | 18,877 orders (0.08637 orders/user-day) * Adjusted Lift: Drops by more than half to +2.35% ($Z = 2.26, p = 0.024$).

#### C. User-level vs. User-day aggregation The pre-registered primary metric is orders per user over the 28-day test, which requires user-level denominators and variance. However, `daily_results.csv` reports daily active users and orders. Summing daily users gives user-days, not unique users. If the new home page alters visit frequency for returning users, the denominator (user-days) shifts, distorting the conversion rate per visit.

#### D. Segment & Novelty Breakdown: Why did New users spike in Week 1? If we break that remaining +2.35% lift down by user type and week (excluding Oct 14), we uncover a major anomaly:

Look closely at what this table tells us: 1. Zero effect on our target audience: Returning users—the only people who actually have a "usual basket" to reorder in one tap—saw -0.03% lift over the test. The feature did not move their order rate at all. 2. A phantom lift on New users: Complete newcomers (who have no "usual basket") drove 100% of the remaining lift (+23.2% in Week 1), which then decayed rapidly to +4.2% by Week 3 and Week 4. 3. Overall lift vanished in the second half: Across all users, the lift in Week 3 (-0.29%) and Week 4 (+1.20%, $Z=0.58, p=0.56$) is completely flat and statistically indistinguishable from zero.

(Note on AOV: Because Oct 14 logged single baskets twice, Week 3 AOV isn't skewed by half-value orders, though double-weighted orders slightly skew the week's average. Regardless, AOV isn't why we're holding).

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3. What this test does and doesn't tell us

• What it shows: After removing the Oct 14 bug, there is 0% lift on returning users, and overall lift decays to noise (+0.45% across Weeks 3–4 combined). Furthermore, the test is compromised by a 15.4% SRM.
• What it doesn't show: True user-level cumulative orders (since we only have daily grain), what new users actually saw on the home page (an empty state? a fallback? a bug?), and why the traffic split failed.

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4. What we should do next

1. Hold Monday's launch. Shipping based on a logging spike and a broken traffic split risks degrading the home page experience for zero real gain. 2. Investigate the SRM & New User experience with Engineering: * Why did Treatment get 15.4% more users every day? (Check bucketing logic, caching, and trigger timing). * Why did New users see a +23% spike in Week 1 that decayed? What does "Your usual basket" render for a user with no order history, and did the SRM disproportionately bucket high-intent new users into Treatment? 3. Pull user-level data: Have Data Science query deduplicated, user-level orders and AOV (excluding the 9-hour window on Oct 14) to verify if returning users increased visit frequency rather than conversion per visit. 4. Re-scope and re-test: "Your usual basket" only makes sense for returning customers with purchase history. Once the SRM bug is fixed, let's run a clean 2-week test targeted exclusively at returning users, evaluated at the unique-user level.

Happy to walk through the numbers live before you message leadership!

Readout: don’t ship the new home page on Monday. The logged data reproduce Zoe’s roughly 6% lift, but they do not yet establish a valid, durable improvement in the pre-registered metric. We have a treatment-specific measurement incident, an unexplained arm imbalance, and a lift that weakens substantially over time.

1. What the headline shows—and how I calculated it

I summed orders and reported daily users within each arm, then calculated:

\[ \text{Lift}=\frac{\text{treatment orders}/\text{treatment users}} {\text{control orders}/\text{control users}}-1 \]

Thus, the logged lift is 5.75%, consistent with “about 6%.”

However, summed daily users are not necessarily distinct randomized users. If users recur across days, this is orders per user-day, not the pre-registered orders per user over the test. If these are active or exposed users, the denominator may itself be affected by treatment. We need the denominator definition and assignment-level data before accepting the primary result.

Nor can these aggregates validate Zoe’s significance claim. For illustration, treating orders as independent Poisson counts gives:

\[ SE(\log RR)=\sqrt{1/20{,}234+1/16{,}560}=0.01048 \]

That produces a nominal 95% lift interval of approximately +3.6% to +8.0%. But it ignores repeated-user dependence, potentially treatment-dependent denominators, and corrupted order counts. It is not a valid substitute for inference at the randomized-user level.

2. The tracking incident materially inflates the result

On 14 October, treatment logged 1,354 orders, versus roughly 700 on surrounding days. Control logged 604. This aligns with the incident report, although we cannot identify the exact duplicate count from these aggregates.

As a sensitivity check, removing that entire day from both arms, including its denominators, gives:

Lift becomes 2.41%, less than half the headline. This does not prove there is no effect; equally, dropping a day is not a repaired primary analysis. We should deduplicate against authoritative order IDs, not halve the day’s orders or discard inconvenient observations.

3. The remaining signal is concentrated early and among new users

Using the same rate calculation within each week and segment:

Across all non-incident days, new-user lift is approximately +11.5%, while returning-user lift is −0.1%.

This is not evidence of a broad reorder benefit for everyone. The pattern is consistent with novelty, changing cohorts, or another time-dependent effect—not proof of any one explanation. These are exploratory comparisons, not statistically established segment differences or grounds to ship only to new users. Calendar-week results also do not measure persistence since each user’s first exposure.

4. Allocation and AOV require qualification

Treatment accounts for 53.6% of reported user-days, despite the planned 50/50 user split; the imbalance appears throughout the test. Repeated daily users prevent a valid assignment-level sample-ratio test here. It could reflect assignment problems, logging, filtering, or treatment-driven activity. We must reconcile it against unique randomized users, rather than assume either harmless imbalance or broken randomization.

Weekly AOV is down approximately 0.2–0.6%, not literally flat. Those point estimates are within the −2% guardrail, but weekly averages provide neither uncertainty nor assurance that duplication did not distort order weighting.

Decision and next steps

The pre-registered shipping rule should stand—but we have not established that it was met with valid data.

1. Reconcile assignment, eligibility, exposure, and user counts; investigate the imbalance.
2. Rebuild orders and revenue from authoritative transactions; verify the incident’s scope in both arms.
3. Recompute 28-day orders per randomized user, including zero-order users, with user-level uncertainty; recompute AOV and its uncertainty.
4. Examine pre-treatment segments and exposure-age cohorts to investigate persistence without treating exploratory findings as confirmatory.
5. If the original test cannot support a trustworthy decision, run a repaired, newly pre-registered test. Do not silently extend this fixed-horizon test until significance returns.