Experimentation & A/B Testing

The problem behind the problem.

Many organizations run tests that cannot answer their own questions. Sample sizes are too small to detect realistic effects. Tests are stopped the moment results look favorable, which inflates false positives. Several metrics are checked until one shows significance. Variants change many things at once, so even a clear winner explains little. Over time, leaders notice that winning tests do not add up to better performance, and confidence in experimentation erodes.

Outside digital products, experimentation is often considered impractical. Yet branch processes, outreach scripts, onboarding programs, clinical reminders and training initiatives can all be tested through randomized pilots, stepped rollouts or carefully matched comparisons. Organizations that only test button colors miss the larger opportunity: learning, with evidence, which operational and service changes actually shift customer, patient and employee behavior. A well-run experimentation program treats every significant change as a chance to learn, and builds a record of what works that compounds over time.

How we deliver experimentation & a/b testing.

1. 01Observe

   Ground tests in real behavior

   We review your current testing practice, past results and decision process, and gather qualitative insight into the behavior each test is meant to change. Good experiments start from an understanding of why people act as they do, which leads to bolder, better-targeted variants than incremental guesswork.

2. 02Signal

   Choose metrics that matter

   We select a single primary metric linked to a predictive KPI, establish baselines and variance from historical data, and define guardrail metrics that would reveal harm, such as cancellations, complaints, error rates or downstream disengagement. Segment-level baselines help anticipate where effects may differ across behavioral types.

3. 03Design

   Write the hypothesis and the plan

   Each experiment receives a written plan before launch: the behavioral hypothesis, the mechanism expected to drive change, randomization unit, minimum detectable effect, power calculation, test duration, stopping rules and analysis method. Internal pre-registration of this plan prevents after-the-fact metric shopping and makes results credible to skeptical stakeholders.

4. 04Test

   Run and analyze with rigor

   We support implementation, monitor sample ratio and data quality during the test, and analyze results with appropriate methods, including confidence intervals, variance reduction where suitable and corrections for multiple comparisons. Results are reported in plain language with effect sizes and uncertainty, not just a pass or fail verdict.

5. 05Scale

   Build an experimentation capability

   We help establish a test backlog prioritized by expected value, templates for plans and readouts, a knowledge base of past results, and review practices that keep quality high. Training for product, marketing and operations teams turns experimentation from a specialist activity into a routine way of making decisions.

Deliverables.

What changes.

Who engages us.

We work with product and growth leaders, marketing executives, heads of analytics and data science, operations leaders and program directors in health systems and associations. Clients range from software companies with mature A/B testing platforms that need sharper methodology, to lenders, retailers and nonprofits running their first randomized pilots. Common triggers include a string of inconclusive tests, skepticism from leadership about results, or a major initiative that deserves evidence before full rollout.

Industries

• Technology & SaaS
• Banking & Financial Services
• Digital Health

Frequently asked questions.