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Experimentation and testing

Stop debating the idea. Test it.

repeatGROWTH helps teams turn growth ideas, UX changes, merchandising decisions, and funnel improvements into measurable experiments.

We research the opportunity, design and build the variation, run the test, analyze the result, and hand your team the evidence needed to make a better production decision.

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Build the test. Measure the result. Keep the learning.

What experimentation can answer

If the business is debating a change, testing can often settle the question.

Experimentation is useful anywhere a measurable customer behavior may change.

  1. Messaging and offers

    Headlines, positioning, promotions, pricing presentation, trust, and value communication.

  2. UX and design

    Layout, hierarchy, navigation, calls to action, interactions, and experience changes.

  3. Merchandising and discovery

    Product placement, recommendations, collections, cross-sells, and pathways into higher-value content or products.

  4. Funnels and forms

    Step order, field strategy, commitment, qualification, checkout, lead flows, and other multi-step journeys.

  5. New concepts

    Meaningful ideas can be validated before committing full production resources to them.

What makes a testing program work

Running tests is easy. Building a testing program you can trust is harder.

The value of experimentation comes from making better decisions, not from launching as many tests as possible.

Every test starts with a reason

A variation runs because the data points to a real problem worth solving.

Instead of testing on a hunch

Results are called on evidence

A test runs long enough to collect a reliable sample and account for normal buying behavior.

Instead of calling tests early

Losses and ties are kept

Every result is documented, so the company keeps the learning it paid to create.

Instead of only winners surviving

The whole picture gets checked

We watch the other business numbers alongside the headline metric before calling a result.

Instead of missing what else moved

Validation comes before production

Ideas get tested first, so engineering time goes to concepts that already proved themselves.

Instead of building first and learning later

How we work

We handle the experiment from question to evidence.

  1. Step 1 of 6

    Start with a reason

    We use analytics, usability research, customer behavior, and business context to identify questions worth testing.

  2. Step 2 of 6

    Define the hypothesis

    We document what we believe will change, why we believe it, and which metric should move if the idea is correct.

  3. Step 3 of 6

    Design and build the variation

    We create the experience, build the test code, and QA it across relevant devices and browsers.

  4. Step 4 of 6

    Launch and monitor

    We run the experiment, monitor data quality, and watch for technical or behavioral issues that could invalidate the result.

  5. Step 5 of 6

    Analyze the outcome

    We evaluate the primary metric, the other business numbers, sample behavior, and practical impact before calling a result.

  6. Step 6 of 6

    Hand off the evidence

    When a test wins, your team gets the tested code and findings for production. When it loses or is inconclusive, the learning is documented rather than discarded.

Better hypotheses

The quality of the experiment starts before the test is built.

Testing random ideas faster does not create a better experimentation program.

We combine quantitative data with qualitative research so the test is grounded in an observed customer problem rather than preference.

Quantitative Tells us where to look.
Qualitative Helps us understand why.
Experimentation Tells us whether the solution works.

Recent experiments

Small changes can create meaningful revenue impact.

Product page

Improved related-product merchandising by introducing a higher-value complementary product.

Relative performance

Variation performed +43% against control.

+43%
Revenue per visitor
Article page

Improved the path from editorial content into relevant product pages.

Relative performance

Variation performed +21% against control.

+21%
Conversion rate
Home page

Redesigned homepage routing to move shoppers into higher-intent collections and products.

Relative performance

Variation performed +30% against control.

+30%
Conversion rate

None of the 3 hurt anything else. Return rate, average order value and add to cart held steady or improved.

See the test archive

Beyond the headline metric

A winner should make the business better, not just the chart greener.

The right metric depends on the experiment, but every test needs a clear primary outcome and the context needed to read it responsibly.

Conversion rate

Whether more visitors completed the intended action.

Revenue per visitor

Whether the experience created more value from the traffic it received.

Average order value

Whether conversion improved at the expense of basket size.

Funnel progression

Whether users moved farther through an important journey.

Lead quality or downstream value

Whether more conversions actually turned into better business outcomes.

Side effects

The measures that tell us whether a local improvement created a problem somewhere else.

The learning system

Winners are useful. Losses are useful too.

A testing program becomes more valuable when every result adds to what the company knows about its customers.

Winners

We document what changed, the result, the supporting evidence, and what should move into production.

Losses

We capture what did not work so the same idea does not return 6 months later disguised as a new recommendation.

Inconclusive tests

We document what the test did and did not answer, and whether the idea deserves another approach.

The archive

Results stay available as a growing record of customer behavior, hypotheses, outcomes, and learnings.

We publish the losers because experimentation is about learning, not building a highlight reel.

Built to work with your team

We move fast in the test environment. Your team owns production.

Less engineering dependency

Your developers do not need to production-build every idea before the business can learn from it.

Direct access

You work with the people researching, designing, building, and analyzing the experiments.

Faster decisions

The question gets turned into a test without disappearing into a long chain of handoffs.

Cleaner implementation

When a variation wins, your development team can implement the proven concept properly in the production codebase.

Who this is for

Built for teams with enough traffic, and enough questions, to benefit from a repeatable testing program.

Common use cases Ecommerce, lead generation, publishing, marketplaces, pricing presentation, and funnels.
Works especially well when The team has plenty of ideas but needs a disciplined way to prioritize, test, document, and act on them.
Best fit

Businesses with meaningful traffic, measurable digital outcomes, and regular decisions that can be validated through experimentation.

Traffic matters, but testability also depends on conversion volume, page-level traffic, baseline performance, and the size of the effect we are trying to detect.

Pricing

Experimentation programs are scoped around testing volume and complexity.

Pricing depends on the number of experiments, research needs, variation complexity, traffic distribution, and how much of the customer journey the program covers.

We do not sell a fixed number of tests without first understanding whether those tests can run cleanly and produce useful answers. After the evaluation, we build the scope around the questions worth testing and the pace the data can support.

Longer programs let each experiment improve the quality of the next hypothesis, building a compounding body of customer knowledge.

Start with the questions

Before we build a testing roadmap, we find out what is actually worth testing.

We review your analytics, examine the key journeys and templates, and conduct usability research to identify where the strongest opportunities may exist.

Then we determine which opportunities are measurable, which have enough traffic to test, and what we would prioritize first.

You keep the evaluation deck and usability recordings whether we work together or not.

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Questions

What teams ask before starting a testing program.

What types of experiments do you run?

We run A/B and split tests across ecommerce, lead generation, UX, messaging, merchandising, forms, funnels, and other measurable digital experiences.

Do you build the test variations?

Yes. We design the variation, build the test code, QA it, launch it, and analyze the result.

Does our development team need to build the test?

Usually not. We build the experiment so the team can learn before spending production engineering time on the idea. When a test wins, we hand off the tested code and findings for implementation.

When do you call a result?

We agree on how the result will be judged before the test starts, then look at the evidence alongside sample behavior, duration, side effects, and practical business impact. A temporarily favorable line is not a settled result.

How many tests can run at once?

That depends on traffic, funnel overlap, and whether one experiment could influence another. We prioritize clean results over running as many tests as possible.

How long does a test run?

Long enough to collect a useful sample and account for the normal behavior of the business. The right duration depends on traffic, baseline conversion, and the expected effect size.

What happens when a test loses?

We document the result and what it taught us. Losing tests are part of the knowledge base and keep the same unsupported idea from cycling back into the roadmap later.

Can you help us build an experimentation program from scratch?

Yes. The engagement can include opportunity research, testing process, hypothesis standards, prioritization, QA, measurement, analysis, and a structured experiment archive.

Turn your next debate into an experiment.

We will review the data, identify what is worth testing, and show you where a disciplined experimentation program can create better business decisions.

Request an evaluation

Research. Test design. Development. QA. Analysis. Documented learning.