Make the behavior visible.
04 · Product Measurement & Experimentation
Measure the change. Isolate what caused it. Decide what to do next.
A product-focused thread across experimentation, causal inference, behavioral analytics, and self-serve measurement—from diagnosing customer-journey drop-offs to giving merchandising leaders a go/no-go signal.
- Observed endcap lift
- 10%
- True incremental lift
- 4%
- Impact lens
- 4 KPI families
The product loop
Analytics earns its place when it changes the next decision.
01Instrument →02Compare →Choose an honest baseline.
03Measure →Separate signal from movement.
04Decide Turn evidence into action.
Causal measurement · 84.51°
The headline moved. The counterfactual changed the story.
For a Quaker endcap in-store test, Bayesian Structural Time Series compared the observed outcome with a synthetic control. A 10% sales uplift contained only 4% true incremental sales—turning an attractive headline into a more useful merchandising decision.
Product outcomeEvidence for a go / no-go decision
Product analytics · Practo + 84.51°
Three ways measurement became part of the product workflow.
01
Self-serve visibility
Built a real-time R/Shiny application consolidating key Search platform metrics, replacing a costly third-party SQL tool and enabling teams to answer recurring questions directly.
Search metrics→Self-serve view
02
Journey experimentation
Designed and ran A/B tests to evaluate conversion drop-offs across customer journeys, pairing behavioral evidence with product-feedback and interaction text analysis.
ABConversion
03
Multi-KPI impact
Measured the effect of merchandising changes across Sales, Visits, Units, and Households—keeping a single favorable metric from defining the whole decision.
SalesVisitsUnitsHouseholds
Product-minded data science
Start with the behavior and the decision—not the technique.
- Question
What user or business behavior should change?
- Baseline
What would have happened without the intervention?
- Measure
Which primary and guardrail metrics tell the full story?
- Decision
What will the team do differently if the evidence changes?
- Learning
How does the result improve the next test or product iteration?
What this case demonstrates
A broader profile than pricing or model building alone.
The common thread is product judgment: instrumenting behavior, designing comparisons, measuring incrementality, interpreting multiple KPIs, and translating evidence into a decision that product, commercial, and operating teams can use.