A/B Testing, with the whole app in view.
A/B testing infrastructure solves the risk of deploying unvalidated product changes, damaging revenue metrics, and making product decisions based on internal opinions rather than user evidence. Many mobile product teams launch tests with insufficient sample sizes, stop experiments early the moment results appear positive, or fail to account for variance, leading to false-positive conclusions that degrade product metrics over time.
What we can take care of
- Experimentation platform integration and evaluation (Statsig, LaunchDarkly, Optimizely, Firebase A/B Testing)
- Feature flag architecture enabling progressive rollouts, instant kill-switches, and targeted user gating
- Pre-experiment power analysis calculating required sample sizes and experiment run durations
- Bayesian and Frequentist statistical analysis models adapted to mobile conversion distributions
- Guardrail metric definitions ensuring feature changes do not degrade app stability, crashes, or retention
- Documentation standards and experiment repository management to preserve organizational testing learnings
- Executive and product team training on hypothesis formulation and statistical literacy
How we approach the work
We establish robust experimentation programs that enable mobile engineering and product teams to test rapidly without breaking production. We implement flexible feature flag and experimentation platforms, configure randomization engines to prevent user allocation bias, and define explicit primary, secondary, and guardrail metrics for every test. Experiments are monitored under rigorous statistical protocols, preventing early stopping and ensuring findings represent genuine user behavior.
What progress looks like
Experiment velocity (number of statistically valid tests concluded per quarter), win rate of experimental hypotheses, guardrail metric protection rate, and business metric lift delivered by validated rollouts.
Before we begin
Share your app links, the challenge and the outcome you want. Your concierge will confirm inputs, deliverables, timing and costs for your scope before work begins.
What is the biggest mistake mobile teams make with A/B testing?
The most common mistake is stopping experiments prematurely as soon as an early p-value looks favorable. In mobile environments, novelty effects and small early sample sizes frequently generate misleading positive spikes. Running tests through predetermined sample sizes and full weekly business cycles is essential to avoid false-positive rollouts.