The Experimentation Lead agent runs the play and promotes winners into always-on personalization. You approve each test before it touches traffic, so velocity never costs you control.
0.0on G2Test velocity is the single best predictor of a testing program's long-term success, and most teams run one or two experiments a month while their queue waits on developers. Then the winner sits in a backlog, because the tool that found it cannot message the segment it won for.
The bottleneck is not ideas, it is test velocity. A visual editor and CUPED variance reduction let one marketer conclude experiments in days, and the Experimentation Lead agent drafts the next play from what the last one learned.
A test aimed at trial users from paid social does not belong in front of all your traffic. You choose the segments worth testing, and the agent finds the winner inside each one.
A clear winner does not go back to a backlog. It becomes a personalization rule for the segment it won for, in one click, with version history and one-click rollback as the guardrails.
| Tool | Entry price | Fit |
|---|---|---|
| VWO | ~$314/mo (Growth) | The operator default; testing plus heatmaps and recordings |
| Optimizely | ~$63,700/yr | Enterprise; roughly 17× VWO at entry |
| Statsig | Free tier | Server-side, engineering-led |
| AB Tasty / Convert | Mid-market | Testing plus personalization |
Only 12% of test ideas produce a statistically significant positive result, across an Optimizely analysis of more than 127,000 experiments. The other 88% are changes teams would have shipped anyway without knowing they did nothing. That is the argument for velocity: you cannot pick the 12% in advance, you can only run enough tests to find them.
Most teams run one or two experiments a month, under 30 a year. A common velocity target is four or more per month. On traffic: CUPED uses pre-experiment behavioral data to reduce variance and cuts time to significance by 20-40%. A working baseline is 1,000 unique visitors per variant per week; below that, personalization rules beat testing because significance arrives after the result is stale.
Count that baseline after consent, not before it. A visitor who declines tracking still shops, but nothing they do lands on a profile, so your testable traffic is smaller than your raw traffic and every variant takes longer to call. Consent decides what gets tracked and which identifiers a test can use, and it holds across experiments and personalization from the one record.
Sources: Optimizely analysis of 127,000+ experiments · Invesp on testing velocity · vendor pricing pages 2026.
Connected outcomes across the platform.
“We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.”
Jim Stromberg
CEO, StockInvest
“Intempt helped us turn real browsing and purchase signals into personalized experiences that drive repeat buying. We finally have one system that sees the whole customer journey.”
Eric Gardner
COO, FieldsUSA
“With Intempt, we built a signal-led pipeline driven by real behaviors. Follow-ups are triggered by intent signals instead of timelines, so we only focus on users who are truly engaging.”
Tadas Kertenis
Co-founder, Hoperfy
Connect your site. The Experimentation Lead agent proposes the first test in the same session, and the winner becomes a permanent rule without waiting on a deploy. No consultant, no dev queue.
