Google Meet Enterprise Clients Complaining About Frequent Disconnections

Before I pick a method, I need to define what "impact" means, because there are three distinct layers and each needs a different estimator.

• Immediate (day of outage): call volume, active meeting count, completed-meeting rate — measured in minutes to hours.
• Short-term (week after): user-level retention — did affected users come back and host/join meetings in the 7 days post-outage, or did they reroute to Zoom / Teams?
• Long-term (30 days): enterprise contract-level churn or non-renewal — the number that actually hits revenue.

I'll commit upfront: primary causal estimator is difference-in-differences, comparing affected APAC regions against unaffected control regions (e.g., EMEA/Americas) over a pre-period of at least 4 weeks and a post-period of 30 days. That requires the parallel-trends assumption: pre-outage, treated and control regions trend together in call volume and retention. I'd verify that with a pre-period trend plot and an event-study specification before trusting the headline estimate.

Panel regression at region-day level, outcome = daily call volume (or, in a separate regression, user-level 30-day retention aggregated):

y_{r,t} = α_r + λ_t + β · (Treated_r · Post_t) + ε_{r,t}

where α_r is region fixed effects, λ_t is day fixed effects, and β is the DID estimate — the treatment effect on APAC after the outage date, net of the control regions' change and net of any global trend. Standard errors clustered by region.

Concretely, with these synthetic numbers:

• Day-of call volume: β = −8% — APAC dropped 8% relative to what the parallel trend from EMEA/Americas would have predicted. That's the immediate usage hit.
• Week-after retention: a separate DID on user-level 7-day return showing, say, a 2–3pp drop in the treated population.
• 30-day churn excess: β = +1.2pp on contract non-renewal probability over the next quarter, again relative to the control regions' trend. At ~$20k ACV and, say, 5,000 affected enterprise contracts, that's roughly 0.012 × 5,000 × $20k ≈ $1.2M in revenue at risk — I'd report it with a 95% CI from clustered standard errors, not a point number.

The key diagnostic is the event-study plot: plot the treatment-control gap week by week around the outage. If the gap is zero in the pre-period and jumps at the outage date, parallel trends is credible and the DID estimate is the causal effect. If the gap is already drifting pre-outage, I've got a confounder and I need to back off the causal claim.

Binary target at the contract level: renewed vs. non-renewed at contract end. Features across five buckets:

• Outage exposure: count of outage minutes the account experienced, disconnect rate pre- and post-outage, region.
• Product quality: baseline call quality scores, disconnect rate, % meetings with issues.
• Usage: meeting frequency, unique active users, feature adoption (recording, breakout rooms).
• Engagement trend: 4-week usage delta — declining usage is a leading indicator.
• Account metadata and support: contract size, tenure, open support tickets, escalations.

Commit: logistic-regression, single model, no ensembling. I'd use gradient boosting only if it bought me more than 2 AUC points over logistic — and even then, the account management team needs to answer "why is account X at risk?" in a customer conversation. Logistic coefficients map directly to that: "disconnect rate contributes +0.4 to the log-odds of churn, which dominates the other factors" is a sentence an account manager can use. A gradient-boosted SHAP explanation is a sentence a data scientist can use. I'm optimizing for the AM conversation, not AUC.

Evaluate with auc-roc for ranking quality and — more importantly — calibration on the predicted probability, because the AM team will make actual outreach decisions based on the probability, not the rank.

Then the headline DID estimate is biased and I need to say so explicitly rather than report the number. Three fallbacks, in order:

1. Synthetic control. Build a weighted combination of unaffected regions that matches APAC's pre-period trend exactly, then compare post-outage. This relaxes the strict parallel-trends assumption at the cost of needing a rich donor pool.
2. Interrupted time series on APAC alone. Fit a model on 8+ weeks of pre-outage data, project the counterfactual, measure the deviation. Weaker because there's no control for global shocks (e.g., Google-wide incidents affecting everyone).
3. Dose-response within APAC. Clients with longer outage exposure (in minutes) should show larger retention and churn hits. A monotone dose-response is robust to many confounders — if heavily-affected accounts drop more than lightly-affected ones, that's strong evidence of causality even without a control region.

I'd run all three and present the range. If they agree, I'm confident. If they disagree, I report the disagreement and wider confidence-intervals, because multiple methods pointing different directions is information, not noise.

Same toolkit, flipped direction. The fix is a second "event"; I want to show disconnect rate and retention recover.

• Primary: DID. If the fix rolls out regionally (APAC first, other regions later), I use the not-yet-treated regions as controls. Disconnect rate in APAC post-fix minus disconnect rate in control regions post-fix, minus the pre-fix gap.
• Secondary: dose-response. Clients with the worst pre-fix disconnect rates should improve the most. A monotone relationship between pre-severity and post-improvement is strong evidence the fix is acting on the right thing, not a global recovery.
• Tertiary: interrupted time series at the deploy date. Look for a sharp level break at the rollout timestamp. If the break is gradual rather than sharp, the fix might not be the cause.

Declare the fix effective only if at least two of the three agree in sign and magnitude, and report CIs with caveats that confounders can't be ruled out without randomization.

DID as the primary estimator comparing APAC to unaffected regions over a 4-week pre-period and 30-day post-period, with parallel trends verified on a pre-outage event-study plot. Report impact at three layers: day-of call volume (≈ −8%), week-after retention (2–3pp drop), and 30-day enterprise contract churn excess (≈ +1.2pp, translating to roughly $1.2M ACV at risk on 5k affected contracts). Back the DID with synthetic control and a within-APAC dose-response check, report the range when they disagree. Churn model is a plain logistic-regression on outage exposure, product quality, usage, trend, and account metadata — interpretability for the AM team matters more than a 1–2 point AUC gain from gradient boosting. Evaluate the engineering fix with the same DID plus a dose-response on pre-fix severity plus interrupted time series, and declare it effective only when at least two methods agree.