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RankBrain

Data Analytics & CRO

Experiments With Results You Can Trust.

We build and run the experimentation infrastructure behind A/B and multivariate testing, from tooling and test design to statistical analysis, so product and marketing teams can make decisions with confidence.

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Overview

What Experimentation covers.

Experimentation is the discipline of running controlled tests correctly. A test can look like a win and still be noise if it stops too early, splits traffic unevenly, measures the wrong metric or ignores a tracking bug. A sound program defines how tests are designed, how long they run, how results are read and how learning is stored and shared.

Rankbrain sets up or improves the testing tool and its integration with your analytics, agrees primary and guardrail metrics, and calculates sample size and duration before each test launches. We run QA on every variant, monitor for problems during the test and analyze results with appropriate statistical methods. Every outcome, including losses, goes into a searchable learning library.

Problems we solve

What this fixes.

  1. 01

    Tests stopped too soon

    Results are called as soon as a variant looks ahead, which often produces false winners that fail to hold up after launch.

  2. 02

    No reliable testing setup

    Testing tools are poorly integrated with analytics, cause page flicker or split traffic incorrectly, so results are hard to trust.

  3. 03

    Learning gets lost

    Past tests live in scattered slides or people’s memories, so teams repeat ideas or forget why something was changed.

Scope of work

What’s included.

A/B and multivariate testing programs with proper test design, statistical rigor and tooling.

  • 01

    Testing tool setup and integration

    Selecting or configuring an A/B testing platform and connecting it to GA4 so experiment data is captured accurately.

  • 02

    Test design

    Clear hypotheses, primary and guardrail metrics, audience targeting and the choice between A/B, split URL and multivariate tests.

  • 03

    Sample size and duration planning

    Calculating required traffic and run time from baseline conversion rates and the smallest effect worth detecting.

  • 04

    QA and monitoring

    Cross-browser and device checks before launch, plus monitoring for sample ratio mismatch and tracking issues during tests.

  • 05

    Analysis and learning library

    Statistically sound result analysis, segment review and a central library documenting every experiment and outcome.

  • 06

    Experiment backlog and governance

    A managed backlog, test calendar and simple rules that prevent overlapping tests from interfering with each other.

Benefits

What it can do for your business.

Ideal for

  • Teams already running ad hoc A/B tests
  • Product teams testing features and onboarding flows
  • High-traffic e-commerce and SaaS websites
  • Companies building an in-house testing culture

  • More reliable decisions

    Proper design and analysis help reduce false winners, so changes you roll out are more likely to hold up.

  • Higher testing throughput

    Templates, QA routines and a managed backlog can make it faster to move from idea to live experiment.

  • Knowledge that lasts

    A learning library preserves insight from every test and helps new team members build on earlier work.

How we deliver it

A clear, practical process.

  1. 1

    Assess

    Review current tooling, analytics integration, past tests and traffic levels to see what testing is realistic.

  2. 2

    Set up

    Configure the testing tool, connect it to analytics and create design templates, QA checklists and governance rules.

  3. 3

    Run

    Plan sample sizes, launch tests after QA and monitor data quality until each test reaches its planned duration.

  4. 4

    Analyze and record

    Analyze results, recommend whether to ship, iterate or discard, and add the outcome to the learning library.

Related services

Works well alongside.

Back to Data Analytics & CRO →

Questions

Experimentation questions, answered.

How is experimentation different from your CRO service?

CRO is the research-led process of finding and prioritizing conversion improvements. Experimentation focuses on the testing program itself: tooling, test design, statistics and the learning library that make results trustworthy.

How long should an A/B test run?

It depends on traffic, baseline conversion rate and the size of effect you want to detect. We calculate this before launch, and tests typically run at least one to two full weeks to cover weekly patterns.

Which testing tools do you work with?

We work with established A/B testing platforms and can configure the one you already use. If you do not have one, we recommend options that suit your traffic, tech stack and budget.

What happens when a test loses?

A losing or flat test still answers a question. We document why it likely did not work and use that insight to refine the next hypothesis.

Ready to talk about Experimentation?

Tell us about your business and goals, and we’ll recommend a clear, practical plan.

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