Data-driven research
Turn customer behavior into better decisions.
repeatGROWTH combines analytics with qualitative user research, including moderated usability tests with 10 to 12 participants to help teams understand where customers are struggling, why it is happening, and which opportunities are worth acting on.
We go beyond reporting what the numbers did. We connect the data to real user behavior so your team can make stronger product, UX, merchandising, and experimentation decisions.
The customer journey
We look for the points where behavior and intent stop lining up.
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Funnel behavior
Where users enter, progress, hesitate, abandon, or fail to complete the intended action.
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Page and template performance
Which page types perform well, which underperform, and where the differences may be coming from.
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Navigation and discovery
How users find products, content, services, or next steps, and where discovery breaks down.
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Conversion behavior
The signals around add-to-cart, form completion, checkout, lead submission, revenue per visitor, and other key outcomes.
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Mobile and desktop behavior
Where the experience changes by device and whether the differences create extra friction.
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Customer understanding
What users notice, what they miss, what confuses them, and what they need before they are willing to move forward.
Why research first
Research is cheaper than rebuilding the wrong thing.
Large redesigns and testing roadmaps often start with opinions about what looks dated or what competitors are doing. We prefer to start with evidence.
Research before a redesign
Evidence shows which parts of the experience actually create friction, and which behaviors should be protected rather than replaced.
Instead of rebuilding on opinionResearch before testing
A hypothesis grounded in an observed customer problem produces experiments and learnings the business can use.
Instead of cycling through random ideasThe process
Research should end with a decision, not another dashboard.
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Step 1 of 5
Start with the business question
We define what the team needs to understand: where conversion is being lost, why a funnel is underperforming, why mobile behaves differently, or where the strongest opportunity may be.
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Step 2 of 5
Find the signals in the data
We analyze the quantitative data to identify patterns, drop-offs, segment differences, and areas that deserve a closer look.
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Step 3 of 5
Investigate the behavior
We use usability research and behavioral evidence to understand what may be causing the patterns we found.
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Step 4 of 5
Connect the findings
We bring the quantitative and qualitative evidence together into a clear point of view on what is happening and why.
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Step 5 of 5
Turn the research into action
We prioritize the opportunities and recommend the next step: fix it, investigate further, redesign it, or test it.
The gap in analytics
Analytics tells you what happened. It rarely tells you why.
A funnel can show where users drop off. A dashboard can show which page underperforms. A heatmap can show where people click.
But none of those explain whether users were confused, missed an important message, could not find the right product, or simply lacked the confidence to continue.
That is why our research combines quantitative data with qualitative evidence.
How we learn
We use multiple sources of evidence because no single tool tells the whole story.
Analytics
Traffic, funnels, page performance, conversion behavior and segment differences, plus heatmaps where they help show where attention goes.
Usability testing
We watch 10 to 12 real users complete tasks across desktop and mobile, moderated, to uncover confusion, hesitation, missed content and friction.
Session behavior
When available, recordings and interaction data show how visitors move through the experience at scale.
Surveys and customer feedback
Direct feedback when it helps explain motivation, objections, expectations, or unmet needs.
Journey analysis
We connect findings across the full customer journey rather than evaluating pages in isolation.
The output
Findings your team can actually use.
Depending on the scope, research deliverables may include:
Funnel and conversion analysis
Page and template performance findings
Mobile and desktop usability findings
Session-behavior observations
Customer journey maps
Screenshots and video examples
Friction points and unanswered questions
Prioritized opportunities
Recommended hypotheses for experimentation
A presentation and walkthrough with your team
We explain what the data means for the customer experience and what your team should investigate next.
Who this is for
Useful when the numbers tell you something changed but not what to do about it.
This work is especially valuable when:
Conversion is moving and the reason is unclear
A funnel has a visible drop-off but the cause is not obvious
Teams disagree about why users are struggling
You are considering a redesign and want evidence first
Mobile and desktop performance differ significantly
You want stronger hypotheses before launching A/B tests
You have plenty of analytics but not enough customer understanding
You need an independent view of a key journey
Pricing
Research scope depends on the question we are trying to answer.
A focused investigation into 1 funnel may require a relatively small engagement. A broader research program across multiple journeys, devices, and customer segments requires more depth.
After an initial evaluation, we define the research scope around the business question and provide clear pricing before the work begins.
Start with the data
Every engagement starts with an evaluation.
Before we scope the work, we review your analytics, run mobile and desktop usability tests, and look closely at your key funnels and templates.
We use that research to identify the strongest opportunities, confirm what the data can support, and build a proposal around what we actually found.
You keep the evaluation deck and usability recordings whether we work together or not.
Request an evaluationQuestions
What teams ask about research.
What is data-driven research?
It is the process of combining quantitative data with qualitative user research to understand customer behavior and make better decisions about UX, conversion, product, and experimentation.
What is the difference between data and insight?
Data tells you what happened. Insight connects that behavior to a useful explanation or decision. A conversion drop is data. Understanding which users are dropping, where it happens, and what appears to be causing it is insight.
What data do you need access to?
It depends on the project, but read-only analytics access is usually the starting point. We may also use platform data, search data, session recordings, surveys, or other sources when they help answer the question.
Do you conduct usability testing?
Yes. Mobile and desktop usability testing are often a key part of the work because they explain behavior that analytics alone cannot.
Do you provide recommendations?
Yes. We prioritize the findings and explain what we believe should happen next. Depending on the evidence, that may be a direct fix, further research, UX work, or an A/B test.
Can this research support a redesign?
Yes. Research can be used before a redesign to identify which parts of the existing experience are actually creating problems and which behaviors should be protected or improved.
Can this lead into experimentation?
Yes. Research is often the foundation for a stronger experimentation program because it generates hypotheses based on observed customer behavior rather than opinion.
Start with the evidence.
We will look at the numbers, study how customers use the experience, and help your team understand where the strongest opportunities may be.
Request an evaluationQuantitative analysis. Qualitative research. Clear next steps.