behavioural research
Behavioural Research Built for CRO Leaders Who Need Evidence, Not Guesses
Most A/B test programmes produce far fewer winners than teams expect. OpenScouter gives CRO leaders the behavioural evidence to understand why variants fail, fix the right things, and build a test backlog grounded in observed user behaviour rather than stakeholder opinion.
Why CRO Programmes Stall Without Behavioural Evidence
CRO leaders are accountable for conversion rate, revenue per visitor, and test velocity. But the evidence most teams rely on, analytics dashboards, heatmaps, and session recordings, tells you where users drop off. It does not tell you why. Without the why, every hypothesis is a guess dressed up as a strategy.
The stakeholder problem compounds this. Product managers want feature parity. Brand teams protect visual identity. Developers push back on test complexity. To win those arguments and get the right tests into the roadmap, CRO leaders need behavioural evidence that is specific, observable, and hard to dismiss. A clip of a user saying out loud that a checkout form feels confusing, combined with rage-click data and a visible expression of frustration, is a different category of evidence from a bounce rate.
The result of evidence gaps is a test backlog built on assumption. Teams run tests that were always unlikely to win, burn statistical power on low-signal variants, and struggle to explain programme ROI to the board. The fix is not more tests. It is better inputs before the tests begin.
Our approach
Three Behavioural Streams, One Correlated Report
OpenScouter captures interaction signals, voice think-aloud, and facial expression in parallel during remote sessions. An AI pipeline correlates the three streams to surface moments where behaviour, language, and affect diverge from what a clean funnel would predict. Every report is human-confirmed before delivery. You receive specific, timestamped evidence tied to journey steps, not a summary of general impressions.
A Higher-Signal Panel for Conversion Friction
Neurodivergent participants, people with ADHD, autism, dyslexia, and related cognitive differences, surface usability friction that neurotypical users tolerate or work around silently. For CRO leaders, this matters because tolerated friction still suppresses conversion. If a checkout flow, a pricing page, or a form is genuinely confusing, neurodivergent testers will make that visible in ways that standard usability panels rarely do.
Evidence Structured for Roadmap Decisions
Reports are formatted so findings map directly to journey steps: landing page, product detail, basket, checkout, confirmation. Each finding includes the behavioural signal, the user quote, and a recommended hypothesis for testing. CRO leaders can take findings straight into a prioritisation session without additional synthesis work.
What you receive
- Timestamped behavioural clips indexed by funnel stage, from landing page through to checkout confirmation
- Correlated findings report combining interaction signals, voice quotes, and facial expression data for each identified friction point
- Prioritised hypothesis list formatted for direct input into your A/B testing backlog
- Journey-level friction map showing where cognitive load, hesitation, and abandonment signals cluster
- Human-confirmed written report suitable for sharing with product, brand, and senior stakeholders as evidence for roadmap decisions
Microsoft, Google and Booking.com report that roughly 10 to 20 percent of A/B tests produce positive, statistically significant results
Harvard Business Review reported in 2017 that Microsoft, Google, and Booking.com, three organisations with mature, well-resourced experimentation programmes, see only a fraction of their A/B tests produce positive, statistically significant results. For CRO leaders, this finding reframes the core problem. The constraint is not test execution or statistical rigour. It is hypothesis quality. When the inputs to the test backlog are weak, most tests will fail regardless of how well they are run. Behavioural research addresses the input problem directly: it gives CRO teams observed evidence about why users behave as they do at specific journey steps, so that the hypotheses entering the backlog are grounded in something more than analytics inference or internal opinion.
Frequently asked
- How does OpenScouter fit alongside tools like Hotjar or Contentsquare?
- OpenScouter is a complement to quantitative tools, not a replacement. Hotjar and Contentsquare tell you where users drop off in aggregate. OpenScouter tells you why specific users behave the way they do at those moments. The two types of evidence work together: quantitative data identifies the where, behavioural research explains the why.
- How many participants do you recruit per study?
- Study size depends on the scope agreed at briefing. For focused funnel studies, sessions typically run with between six and twelve participants. Neurodivergent panels produce high-signal findings at smaller sample sizes because participants surface friction explicitly rather than silently working around it.
- How long does a study take from brief to report?
- From confirmed brief to delivered report, most studies complete within days rather than weeks. Exact timelines depend on session scheduling and the number of journey steps in scope. We confirm a delivery window at the start of each engagement.
- Can OpenScouter test specific variants or pages we are already running?
- Yes. Studies can be scoped to a specific page, a specific variant in an active test, or a defined journey segment such as the basket-to-checkout step. If you have a test that is underperforming and you want to understand why, that is a straightforward brief for an OpenScouter engagement.
- How does facial expression data work and where is it processed?
- Facial expression analysis runs locally on the participant's device using on-device computer vision. No raw facial video is transmitted or stored on OpenScouter servers. The output is an affect signal, a structured data stream indicating moments of confusion, hesitation, or disengagement, which is correlated with the interaction and voice streams in the AI pipeline.
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