A/B Testing
An A/B test run badly is an opinion with extra steps. We run experiments that produce answers you can bet budget on.
A/B testing at RedSage follows the scientific method with engineering rigor: hypotheses from evidence, sample sizes calculated before launch, tests run to significance without peeking, and results reported with their confidence - including the inconclusive ones.
We handle the full stack: test design, implementation, QA across devices, statistical analysis, and the documentation that turns one test's outcome into institutional knowledge.
The Challenge
Most in-house testing programs lie to their owners: tests called at 60% confidence because the boss asked, variants shipped after three days of a winning streak, overlapping tests contaminating each other, and a graveyard of 'wins' that never showed up in revenue.
The tooling is rarely the problem. The discipline is - and discipline is exactly what a deadline-driven team cannot give itself.
Our Approach
Every test starts with a written hypothesis and a power calculation: what change, why it should win, and how many conversions we need before the result means anything. Tests run their full course; early trends are noted, never acted on.
Results feed a documented library: what was tested, what happened, what it means, what follows. Wins ship with their measured lift; losses generate the next hypothesis. Over quarters, the library becomes the most valuable conversion asset you own.
Capabilities
Hypothesis design from research evidence.
Power calculation and sample-size planning.
Test implementation and cross-device QA.
Statistical analysis with honest confidence.
Experiment documentation and knowledge library.
Execution Process
Hypothesize
Evidence-based prediction, written down.
Power
Sample size and duration calculated.
Run
Full duration, no peeking, clean QA.
Decide
Ship, iterate, or kill - documented either way.
Business Outcomes
Results you can bet budget on
No false wins inflating expectations
Institutional knowledge from every test
Faster decisions with less politics
A compounding experimentation capability
Deliverables
Technologies
Relevant Industries
Related Services
Frequently Asked Questions
However long the power calculation says - usually two to six weeks depending on traffic and the size of the effect we are detecting. We give you the number before launch, not an excuse after.
Then we learned the change does not matter much - which is information. It gets documented, the variant is retired, and the next hypothesis moves up. Inconclusive is a result, not a failure.
Not reliably with classic A/B methods - the math does not bend. For low traffic we use research-driven changes, sequential testing, or bandit approaches, and we tell you which compromise we are making.
A/B Testing
Experiments run like experiments - powered, significant, and honest about what won and what did not.
Test with discipline