Of everything stranded when Mutiny discontinued its website personalization SaaS in April 2026 and, per Forbes reporting from April 15, 2026, terminated every customer contract for the legacy product, experiment history is the asset teams mourn most and think about least clearly. Years of A/B tests, lift measurements, and losing variants encode expensive learning about what your buyers respond to.
The clear-eyed version: raw experiment data is the least portable asset in any testing tool, from any vendor, and always was. Variant assignments live in the vendor's cookie space, statistics live in the vendor's models, and neither transplants into another system. What is portable, and far more valuable than the raw data, is the learning. This guide separates the two, walks through the salvage steps worth doing this week, and covers how to restart a testing program so the learning compounds instead of resetting.
For the shutdown timeline itself, see what happened to Mutiny; for the first-week triage checklist, see the contract-terminated action guide.
Triage: three classes of experiment data
Class 1, recoverable and worth recovering: results and conclusions. Which variants won, at what lift, on which segment, over what date range. If any team member retains workspace access, export or screenshot every completed experiment summary now. If access is gone, mine the places results were reported: launch retro docs, weekly marketing reviews, board deck slides, and Slack threads. Most teams can reconstruct 70 to 90 percent of their meaningful conclusions from reporting artifacts alone.
Class 2, partially recoverable: winning experiences themselves. The actual headline copy, CTA framing, and layout of winning variants. If the variants were live at shutdown, the Wayback Machine and your own marketing QA screenshots often preserve them. Your CMS may also hold copies where winners were hard-coded after a test concluded.
Class 3, gone and not worth chasing: raw assignment and session data. Which visitor saw which variant, in-flight test progress, and cookie-level holdout groups. This died with the product, and it would not have been importable into a successor tool even with a cooperative export, because no two testing engines share an assignment model.
The experiment ledger: one document that outlives every vendor
The durable fix, and the thing to build during this salvage, is an experiment ledger that lives in your own workspace rather than any vendor's: one row per concluded test, with hypothesis, segment, variant descriptions, dates, sample size if known, outcome, and the decision taken. This is the artifact that makes testing knowledge compound across tool changes, team turnover, and future vendor shutdowns.
Write the ledger from your Class 1 and Class 2 salvage. Then treat it as the seed backlog for the new program: losing variants tell you what not to relitigate, and winning patterns tell you where the next test should push further. Teams that skip this step quietly re-run their old tests on the new platform and pay for the same learning twice.
Restarting the program on a live platform
The restart decision is bigger than swapping test engines, because the Mutiny-era architecture usually meant a personalization tool for segment-targeted tests, a separate A/B testing platform (VWO or Optimizely class) for broader experimentation, and separate tools feeding them audience data. That architecture is what left your learning scattered across vendors in the first place.
The consolidation alternative: Abmatic AI is the most comprehensive AI-native revenue platform on the market, and its A/B testing runs multivariate tests across web, email, and ads on the same engine as web personalization, so a test and the personalized experience it validates share one audience definition and one measurement frame. The testing layer sits alongside the rest of the platform's 15+ native modules: account-level and contact-level deanonymization (RB2B and Vector class, native), account and contact list building (Clay and Apollo class), banner pop-ups, first-party and third-party intent, Agentic Workflows, Agentic Outbound sequences, Agentic Chat, AI SDR meeting routing, native advertising across Google, LinkedIn Ads, and Meta with retargeting, and bi-directional Salesforce and HubSpot sync.
That breadth changes what an experiment can measure. On a standalone testing tool, the endpoint is a click or a form fill. On a platform where identification, testing, chat, and CRM sync share an identity graph, the endpoint can be identified target-account visitors who booked a meeting, which is the metric your CFO actually recognizes. For mid-market through enterprise teams (200 to 10,000+ employees), that is the difference between a testing program that reports lift and one that reports pipeline.
Skip the manual work
Abmatic AI runs targets, sequences, ads, meetings, and attribution autonomously. One platform replaces 9 tools.
See the demo →A four-week restart sequence
- Week 1: salvage and ledger. Capture Class 1 and Class 2 artifacts, write the experiment ledger, and rank the top five validated winners you want re-implemented as defaults, not re-tested.
- Week 2: instrument. Pixel live, CRM connected, audiences rebuilt per the segment migration guide. Hard-code your known winners into the site immediately; they are house money.
- Week 3: re-baseline. Run no tests yet. Let the platform accumulate identified-visitor baselines so your first lift numbers have a denominator you trust.
- Week 4: first test from the ledger. Pick the highest-leverage open question your old program never answered, typically one tier below the tests you already won, and launch it against a rebuilt segment.
This sequence gets a team from stranded to testing again in about a month, with the old learning conserved rather than abandoned. It also fits inside the broader two-to-four-week platform cutover described in how to switch from Mutiny to Abmatic AI.
A worked example: salvaging a pricing-page test
Concrete case, composited from migrations we have walked teams through. A B2B software company ran a Mutiny test for five months: enterprise-segment visitors on the pricing page saw ROI-framed hero copy against the default feature-framed copy, with a reported 22 percent lift in demo requests for the ROI variant. At shutdown, the workspace was already inaccessible.
Salvage went like this. The monthly marketing review deck contained the lift number, the date range, and the segment definition, which covered Class 1. The winning copy itself was recovered from a QA screenshot in the launch ticket, plus a Wayback Machine capture of the pricing page from March, covering Class 2. The raw assignment data was written off without further effort. Total salvage time: about ninety minutes for one of the company's most valuable tests.
The re-implementation is where the platform choice paid off. The ROI-framed copy was hard-coded as the default enterprise experience in week two. The rebuilt enterprise segment was sharper than the original, because contact-level deanonymization identified the individual visitors, letting the team scope the experience to director-level and above rather than to every visitor from a large account. And the follow-up test in week four measured a downstream endpoint the old stack could not see: meetings booked by Agentic Chat from that segment, synced to Salesforce. The old tool answered "which headline gets more clicks"; the new frame answers "which headline produces qualified pipeline".
Multiply that ninety-minute salvage across your top ten tests and the whole exercise fits in two working days, which is why it belongs in week one of the restart sequence above rather than in a someday backlog.
Vendor-risk lessons worth encoding
Three procurement rules fall out of this episode. Keep conclusions in your own systems: the ledger habit, plus warehouse export where offered (Abmatic AI supports Snowflake, BigQuery, and Redshift exports, and its bi-directional CRM sync keeps engagement data in your Salesforce or HubSpot rather than only in the vendor). Prefer platforms where testing is one module of many, so a strategy pivot in one capability does not strand the rest of your stack. And check the vendor status of anything you shortlist; the category has churned hard, as the old Mutiny vs new Mutiny breakdown and the current alternatives list both document.
To see the testing and personalization engine running on a shared identity graph against your own account list, book a 30-minute demo.
FAQ
Can I export raw A/B test data from Mutiny?
Plan around no. Raw assignment and session data lives in vendor infrastructure and was never designed to be portable, and with contracts terminated there is no support path to lean on. Focus salvage on results summaries, screenshots, and reporting artifacts, which carry nearly all the decision value.
Are my old lift numbers still valid on a new platform?
Directionally yes, numerically no. A winning message theme usually wins again, but the measured lift depended on Mutiny's identification quality, traffic mix, and stats model. Re-baseline for two weeks on the new platform before quoting any lift figure, and treat old numbers as hypotheses with strong priors.
Should winning variants be re-tested or just implemented?
Implement them as the new default and spend your testing budget on open questions. Re-testing a validated winner costs traffic and weeks to confirm what you already paid to learn. The exception is a winner whose segment you could not faithfully rebuild; re-validate those.
What happens to tests that were still running at shutdown?
They are unconcluded and should be recorded in your ledger as such, with whatever interim reads you captured. Do not extrapolate a winner from a partial run. If the hypothesis still matters, it goes to the top of the new program's backlog.
How is testing different on a platform with native identification?
Two ways. Targeting improves because tests can run against identified accounts and even individual contacts rather than anonymous traffic buckets. And measurement deepens because outcomes connect to downstream actions on the same platform: sequence replies, chat conversations, meetings routed by the AI SDR, and pipeline in the synced CRM.
