Behavioral Experimentation

Description

Behavioral Experimentation is how we prove which changes actually improve long-term customer value — not just short-term clicks or conversion spikes.

Most teams can run A/B tests. The hard part is running the right tests, on the right customers, with outcomes measured in retention, contribution margin, and lifetime value. That's where we come in.

Unlike experimentation programs built around velocity, we are typically brought in when decisions are expensive to get wrong. That includes discounting and incentives, loyalty mechanics, winback policy, onboarding changes, pricing tests, and messaging that shapes customer behavior, and profitability, over time. Using a research-based behavioral science lens, we design tests around the mechanisms that actually drive decisions—so you learn not just what worked, but why it worked, for whom, and how long it lasts. Our focus is not testing for activity; it's testing for incremental, durable value.

How we work:

Hypothesis → decision:

we start from the business decision at stake and define what "success" means financially.

Clean design:

value-stratified randomization (e.g., by predicted CLV deciles), holdouts, and guardrails to avoid contaminated conclusions.

Long-term readout:

results interpreted through retention and value over time — not just immediate uplift.

Operational handoff:

recommendations translated into practical rules (who gets what, when, and why), designed for your existing CRM/CDP workflows.

Behavioral Experimentation is used selectively –when decisions are high-stakes and being wrong is expensive. Done well, it turns predictive modeling into a reliable customer value operating rhythm: proving what drives incremental, durable profit and helping organizations move from "personalize everything" to personalize profitably.

Value

De-risked high-stakes decisions: You avoid scaling changes that look good in short-term KPIs but lose money long-term.

  1. Causal proof (incrementality), not correlation:

    You learn what actually caused uplift—so you can defend decisions to leadership and finance.

  1. Value-segment precision: Learn how different customer segments respond — and what that means for your product and experience design.

  1. Decision-ready policy: Turn results into targeting rules and playbooks your CRM team can run repeatedly.

Behavioral Experimentation helps your organization answer questions such as:

1. What works (in the real world)? Which change actually causes improvement vs. just correlates with it?

2. For whom does it work? Which customer segments (e.g., predicted-value deciles) respond — and which don't?

3. Is it profitable and incremental? Does it create incremental profit/retention after costs, or just subsidize inevitable behavior

4. Will it last — and should we scale it? Is the effect durable over time, and what's the clear rule for rollout (what/when/who)?