Switching A/B testing providers without losing a single week of learning
Nobody switches A/B testing platforms for fun. You switch because the renewal quote made your CFO laugh, because your marketers still can't launch a test without filing a request, or because the "AI features" bolted onto your current tool turned out to be a chatbot with a dashboard behind it.
And then you hesitate, because switching feels like it means losing things. Your experiment history. Your live tests. Your targeting rules. Your team's muscle memory.
Here's the truth that makes this whole post shorter than you expected: an A/B testing platform holds far less than you think it does. Your real assets are your hypotheses, your learnings, and your traffic. Two of those live in your head and your docs, and the third comes with you wherever you go. The platform is plumbing.
This guide covers what actually needs to move, what to deliberately leave behind, and what the switch looks like at two very different scales: the team with a handful of experiments, and the enterprise program with hundreds of tests, flags, and stakeholders.
First, what are you actually migrating?
Strip an A/B testing program down and there are only five things in it:
Live experiments
Tests currently running and collecting data.
The queue
Ideas and hypotheses waiting their turn.
The archive
Completed experiments: what won, what lost, and why you think so.
Configuration
Audiences, targeting rules, goals, and integrations.
The habit
The team's cadence of launching, reading, and acting on tests. This is the one nobody lists and the one that actually dies in most migrations.
Notice what isn't on the list: the raw data. Your old platform's click logs and session-level exports are nearly worthless in a new tool, because no two platforms count, bucket, or model visitors identically. Trying to replay old data in a new system produces numbers nobody trusts. Migrate conclusions, not clicks.
The light switch: migrating with a few experiments
If you're running one to ten experiments, your migration is a week, and most of that week is deciding, not doing. This is the profile of most marketing-led teams on a legacy tool: paying enterprise prices, using a fraction of the platform, launching a couple of tests a quarter because each one costs so much effort.
Here's the play.
Step 1: Let live tests finish or call them honestly
Don't port a half-finished experiment. A test that moves platforms mid-flight is two half-tests, and neither reaches significance. For each live experiment, either let it run to conclusion on the old platform before the contract ends, or accept the result as directional and plan to re-run it properly on the new one.
Example. Say you're A/B testing two checkout headlines, "Complete your secure checkout" against "Pay in seconds," and it's at 80% confidence when the renewal deadline hits. Don't squint at 80% and ship it. Write it down as a promising direction, and make it your first prompt in RunPivot: "Test 'Pay in seconds' against our current checkout headline on mobile." The experiment is rebuilt in minutes, and this time it runs to a real answer.
Step 2: Move the queue as sentences
Your backlog of test ideas is probably scattered across the old tool, a spreadsheet, and three people's heads. Consolidate it as plain English, one sentence per idea, because on an AI-native platform the sentence is the setup. "Test a shorter hero headline on the pricing page." "Try moving social proof above the fold for paid traffic." Ten ideas, ten sentences, ten launch-ready experiments. What used to be a migration task is now just the queue.
Step 3: Write the archive down once, properly
For each completed experiment, record four lines: the hypothesis, the variants, the result with its confidence level, and what you learned. An afternoon of work, and it's the most valuable afternoon of the whole migration. Programs that document learnings compound; programs that don't keep re-running the same tests every time someone new joins.
Step 4: Remove the old snippet, add the new one
For a small program, the technical migration is genuinely this: one script comes out, one goes in. No SDK project, no dev queue. Run one A/A test on the new platform if you want belt and braces, confirm the numbers look sane, and start launching from your queue.
Your first week on RunPivot, if you're this team: re-run your most promising unfinished test, launch the top three sentences from your queue, and let RunPivot watch all four while you get back to your actual job. That's more concurrent experimentation than most small programs managed in a quarter on the old tool, and it's exactly the point of switching.
The enterprise-grade transfer: migrating a serious program
Now the other end of the spectrum: hundreds of historical experiments, dozens live at any time, server-side tests, feature flags tangled into the codebase, multiple brands or markets, and a governance process with real teeth. This migration is a program, not a weekend, and pretending otherwise is how enterprise switches go bad.
The good news: the same principle holds. You're migrating conclusions and capability, not data. It just takes more discipline at scale.
Phase 1: Audit before you move (two weeks)
Inventory every live experiment, every flag, and every audience definition, and sort them into three buckets:
Conclude. Tests near significance. Let them finish on the old platform.
Rebuild. Tests and always-on personalisation rules that earn their place. These get recreated on the new platform, and here's the uncomfortable discovery every enterprise audit makes: a meaningful share of "live experiments" are zombie tests that concluded months ago and were never rolled out or switched off. The audit alone usually pays for the migration.
Retire. Everything nobody can explain the purpose of. If no one owns it, it doesn't move.
Example. A retailer migrating 60 live experiments typically finds something like 15 worth concluding, 20 worth rebuilding, and 25 that are zombies, including a "temporary" holiday banner test from two seasons ago still splitting traffic. Retiring the zombies isn't a compromise of the migration. It's one of its returns.
Phase 2: Parallel running (four to six weeks)
Enterprises don't cut over, they overlap. Run both platforms simultaneously on separate experiments, never splitting the same page's traffic across two tools, which contaminates both. Pick one high-traffic, low-politics surface and rebuild a known experiment on the new platform.
Example. Take a test your old platform already answered, say a product page CTA test where "Add to bag" beat "Buy now" with 96% confidence. Re-run it on RunPivot. You're not A/B testing the button again, you're A/B testing the platform: does the new tool reach the same conclusion on the same traffic? When it does, you've built the trust that makes the rest of the cutover an operational task instead of a political one.
This is also where the agentic difference shows up at scale. On a legacy platform, 20 concurrent experiments means a team of analysts watching dashboards. On RunPivot, the platform watches every one continuously, calls winners at 95% confidence, and rolls them out automatically or holds them for approval, depending on the guardrails you set per experiment. Enterprise governance doesn't fight the automation, it configures it.
Phase 3: The knowledge transfer (parallel with phase 2)
At enterprise scale the archive isn't an afternoon, it's a project, and it's still non-negotiable. Export the record of every completed experiment: hypothesis, variants, result, confidence, decision taken. Structure it once, load it into your program's single source of truth, and every future "what should we test next" conversation starts from evidence instead of memory.
This is also the moment to fix reporting. If your old program's results lived in a dashboard leadership never opened, don't rebuild that. Every RunPivot experiment ends with lift, confidence, and revenue impact in a report written for the room, which quietly solves the oldest enterprise experimentation problem: proving the program deserves its budget.
Phase 4: Cutover and decommission (one to two weeks)
Migrate the remaining rebuilt experiments, remove the legacy snippet and SDKs, confirm nothing in the codebase still calls the old flags, and end the contract. Total elapsed time for a serious program: eight to ten weeks, run mostly in parallel with business as usual. The teams that struggle are the ones that try to move everything as-is. The teams that succeed treat the migration as the audit their program was overdue for anyway.
The experiments to run in your first month, at either scale
Whatever size you are, the fastest way to make a new platform pay is to stop migrating and start A/B testing. Four experiments that earn their keep in month one:
The unfinished business test
Re-run your best directional result from the old platform to a proper conclusion. You likely have a winner sitting at 85% confidence that nobody ever shipped.
The mobile checkout test
Checkout is where abandonment lives and mobile is where checkout struggles most. "Test a shorter headline and fewer visible form fields on mobile checkout" is one sentence and routinely one of the highest-value experiments a business runs.
The pricing page framing test
Test what you anchor first: monthly versus annual emphasis, or which plan is visually recommended. Small framing shifts here move revenue, not just conversion rate.
The one you never had capacity for
Every team has a test they've wanted to run for a year but could never justify the setup cost. On an agentic platform the setup cost is a sentence. Run it in week one, if only to feel the difference.
The switching checklist
Inventory live experiments: conclude, rebuild, or retire each one.
Consolidate the idea queue as one-sentence hypotheses.
Document the archive: hypothesis, variants, result, learning, for every completed test worth remembering.
Rebuild only the experiments and audiences that earned their place.
Validate the new platform by re-running one known result.
Set your guardrails: confidence thresholds, traffic caps, auto-rollout or approval per experiment.
Remove the old snippet, SDKs, and dead flags. End the contract.
Launch the queue and let RunPivot take the watch.
Comparing your options first?
If you're weighing up where to land, we maintain honest, current comparisons of RunPivot against the major experimentation platforms, including what each migration path looks like. And if you'd rather just see the workflow, describe a test in plain English and watch what happens.
Switch without losing a week of learning
Bring your hypotheses, your learnings, and your traffic. Describe a test in plain English and watch RunPivot build it, run it, and ship the winner.
Timeline figures in this guide are illustrative planning guidance, not guarantees. Your migration length depends on program size, traffic, and governance requirements.