A/B testing tells you which variant won. It never tells you why visitors stall.
The traditional cycle works, and it’s slow by design: significance takes traffic, deploys take engineering, and the visitors who hit the friction this week are gone before the winning variant ships. Asking is the shortcut the toolkit is missing. One contextual question at the hesitation point names the blocker directly, whether that’s cost, unclear terms, or a missing spec, which is the research step that turns your next test from a guess into a confirmation.
We report what each agent drove against the number you already track, and it composes with your existing experimentation program instead of fighting it: Pulse finds and patches the friction in-session, your testing stack validates the permanent fix. Reason distributions come out ranked, which is a prioritized backlog for your next quarter of tests.
Trusted By Enterprise Teams At
COMCAST
VISA
J&J
CROCS
SKECHERS
BENJAMIN MOORE
The timing gap
Help now, while the fix is built
You already know where visitors drop off. The question is how long it takes to do something about it, and how many conversions you lose in between.
Traditional CRO cycle
Identify: spot the drop-off in analytics. Maybe next Monday’s dashboard review.
Hypothesize: build a theory. Get alignment from stakeholders. Two weeks, optimistically.
Test: design and run an A/B test. Wait for statistical significance. Four to eight weeks.
Deploy: get engineering resources. Push to production. Add another sprint or two.
Timeline: 3–6 months per fix
With Pulse Agents
Detect: Pulse monitors 50+ behavioral signals in real time. Friction is identified the instant it happens.
Intervene: a pre-approved, contextual intervention deploys automatically. Native to the page, invisible until needed.
Timeline: during the visit
Behavioral sensing
50+ signals monitored in real time
Pulse reads the behavioral fingerprint of every session: the micro-signals your analytics platform aggregates away. Here’s a sample of what it watches.
Rage clicks
Form abandonment
Ping pong navigation
Comparison behavior
Viewport idle time
Scroll depth stalls
Exit intent patterns
Error encounters
Cart hover, no add
Reverse navigation
Hesitation on CTAs
Tab switching
Dwell time
Dead click zones
Multi-session return
How It Works
AI builds it. Humans approve it. The system deploys it.
You stay in control. Every intervention is reviewed and approved before it ever reaches a visitor. Here's the flow.
AI generates intervention candidates
Pulse analyzes your site, your traffic patterns, and your conversion flows to recommend contextual interventions for each friction point it detects.
AI-Powered
Your team reviews and approves
Nothing goes live without your sign-off. Review the copy, the targeting logic, and the trigger conditions. Edit anything. Approve when you're satisfied.
Human-controlled
System deploys in real time
Approved interventions activate instantly. When a matching behavioral signal fires, the intervention renders natively on the page. No dev tickets, no deployment cycles.
Automatic
See the friction your dashboard can't show you
Get a personalized analysis of your site’s hesitation points, and what real-time interventions would look like. Free, 15 minutes, no strings.
Get Your Free Analysis
Same technology Crocs, Skechers, and Benjamin Moore use daily.
Frequently asked questions
How is this different from A/B testing?
A/B testing tells you which variant won; it does not tell you why visitors stalled. Pulse asks at the point of hesitation, which turns your next test from a guess into a confirmation.
Does this replace our experimentation program?
No, it feeds it. Pulse patches friction in the session while your testing stack validates the permanent fix, and the ranked reason data becomes a prioritized backlog for what to test next.
How fast can we act on a finding?
Interventions deploy from an approved library without a release, so the gap between finding a problem and responding to it is hours rather than the usual multi-week cycle.
How do you show what it drove?
We count everyone who engaged and what they went on to do, and report it against the number you already track. Some of the people it helps would have left without it; those are incremental conversions from people who were about to leave and finished instead.