Advanced Statistical Analysis & Causal Inference

G3 Pipeline Impact Analysis

Implemented advanced statistical matching and causal inference techniques including propensity score matching (100% success rate), difference-in-differences analysis, cluster-based control groups, and bootstrap confidence intervals.

$706K Annual Revenue

Challenge

Measure the causal impact of G3 (security specialist) engagements on customer security service adoption and revenue growth. Challenge: Isolate true program impact from selection bias (engaged customers may already be high-value accounts).

Solution

Implemented advanced statistical matching and causal inference techniques including propensity score matching (100% success rate), difference-in-differences analysis, cluster-based control groups, and bootstrap confidence intervals.

Impact Metrics

$706K
Annual Revenue
(6:1 ROI)
219.8%
ARR Lift
($219,942 per account)
100%
PSM Success
Perfect matching
19%
Revenue Increase
(p < 0.05)

Results

  • $706K Annual Revenue with 6:1 ROI validation
  • 219.8% ARR Lift ($219,942 additional ARR per engaged account)
  • 19% Security Revenue Increase with statistical significance (p < 0.05)
  • 1,220 New Customer Adoptions exceeding annual target by 4 months
  • 68.7% Win Rate for direct engagements

Business Impact

This analysis directly influenced 2026 goal setting (750 G3 engagements target with 70% win rate), resource allocation (focus on high-performing engagement types), program expansion ($17M+ investment justified), and field strategy (engagement playbooks optimized by cluster and type).

Statistical Methodologies

A proven approach combining statistical rigor, automation, and AWS best practices.

1

Propensity Score Matching

Matched treatment accounts to similar control accounts on 11 observable characteristics, achieving perfect covariate balance (all p-values > 0.05)

2

Difference-in-Differences

Controlled for time trends and unobserved factors affecting both groups equally, isolating true treatment effect

3

Cluster-Based Control Groups

K-means clustering with 11 features to identify heterogeneous treatment effects across account types

4

Bootstrap Confidence Intervals

10,000 iterations to quantify uncertainty in estimates for robust business decision-making

Scale & Scope

638,178
Data Volume
customer-month observations across 53,367 unique customers
30,567
Opportunities
opportunity records across 2,719 accounts
25
Engagement Types
different engagement types analyzed

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