Experimentation platform
What is an experimentation platform?
An experimentation platform helps teams turn hypotheses into tests, evidence, and decisions. Some platforms optimise products after launch. Rapidly helps enterprise teams test ideas before they commit to building them.
Definition
An experimentation platform is software that helps teams design tests, collect evidence, and make decisions from the results. Post-build platforms usually compare variants in an existing product or customer experience. Pre-build platforms test the assumptions behind an idea before the organisation commits to building or funding it.
Reviewed by Leslie Barry, founder of Exponentially · Reviewed 12 July 2026
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Two-stage framework
Two types of experimentation platforms solve different decisions
The category covers two different stages of a decision. Pre-build experimentation asks whether an idea deserves investment before it exists. Post-build experimentation asks which version of an existing product or customer experience performs better. Both are legitimate, and many enterprise teams need both.
Should we build or fund this idea?
Pre-build experimentation
Tests an idea, proposition, service, workflow, or feature before delivery. The output is evidence for a decision to back, pivot, park, stop, or test again.
Which live version performs better?
Post-build experimentation
Compares variants in an existing product, feature, page, or customer experience. The output is evidence for a decision to ship, roll back, continue, or optimise.
- 01Idea and assumptions
- 02Pre-build experimentation
- 03Build decision
- 04Product or experience
- 05Post-build experimentation
- 06Optimisation decision
Compare the stages
Pre-build experimentation versus A/B testing
The distinction is not which approach is better. It is which decision the team needs to make and what already exists when the experiment begins.
| Dimension | Pre-build experimentation | Post-build A/B testing |
|---|---|---|
| Core question | Should we build or fund this idea? | Which version performs better? |
| Starting point | An idea, assumption, proposition, or proposed workflow | An existing product, feature, page, or live experience |
| Typical experiments | Pretotypes, fake doors, landing-page tests, concierge tests, Wizard of Oz tests | A/B tests, multivariate tests, feature rollouts, holdouts |
| Evidence | Behaviour that indicates demand, commitment, or use | Comparative performance across live variants |
| Infrastructure required | A believable test and a way to observe customer behaviour | A built experience, instrumentation, traffic, assignment, and statistical analysis |
| Primary users | Product, innovation, transformation, R&D, and business teams | Product, growth, engineering, and data teams |
| Decision | Back, pivot, park, stop, or test again | Ship, roll back, continue, or optimise |
| Example platforms | Rapidly | Statsig, Amplitude, GrowthBook, Optimizely, VWO |
Many enterprise teams need both. Rapidly supports the upstream investment decision. A/B testing platforms support downstream optimisation once something has been built and exposed to users. For a leading post-build example, see Microsoft’s experimentation platform.
Enterprise workflow
From idea to evidence-backed decision
A useful enterprise experimentation workflow begins with the decision, not the tool. Rapidly structures the work and keeps the evidence trail, while teams continue to use the channels and execution tools that fit each experiment.
- 01
Capture the idea
Record the idea, strategic context, intended customer, and the decision the organisation expects to make. 2. 02
Frame the problem
Turn the idea into a Lean Canvas or equivalent framing so the customer, problem, proposition, and alternatives are explicit. 3. 03
Find the riskiest assumption
Identify the belief most likely to make the idea fail, rather than testing the easiest part first. 4. 04
Set a falsifiable hypothesis
Define the behaviour you expect, the audience, the timeframe, and the evidence threshold before results arrive. 5. 05
Design the smallest useful pretotype
Choose the least expensive believable test that can expose the assumption to real customer behaviour. 6. 06
Collect and interpret evidence
Run the test through the right channel, then assess the signal, limitations, and what the evidence does not prove. 7. 07
Make the decision
Back, pivot, park, stop, or test again, with the reasoning and evidence preserved for the portfolio.
Rapidly does not pretend to execute every experiment automatically. Teams may still use Figma, ads, analytics, landing pages, customer channels, research tools, or delivery systems. Rapidly provides the common method, experiment record, evidence trail, and portfolio view around that work.
Learn more about pretotyping software and what pretotyping is.
Buyer checklist
What should an enterprise experimentation platform include?
The strongest platforms make the quality of the decision visible, not just the volume of ideas or experiments.
01
Clear assumption and hypothesis structure
Reveal what must be true and turn it into a testable claim, rather than simply storing an idea.
02
Multiple experiment methods
Support the smallest method appropriate to the risk instead of forcing every question into an A/B test.
03
Behavioural evidence
Distinguish observed customer behaviour from survey opinions, internal votes, or AI-generated scores.
04
Decision rules
Make evidence thresholds and the next decision explicit before the team sees the results.
05
Portfolio visibility
Show experiment status, evidence quality, outcomes, velocity, and what the organisation chose not to fund.
06
Reusable learning
Keep a searchable record of assumptions, tests, evidence, and decisions so teams do not repeat avoidable work.
07
Governance without friction
Support roles, review points, and standard methods without creating another heavy stage-gate process.
08
Integration with execution tools
Complement design, analytics, advertising, research, and delivery systems rather than attempting to replace them.
09
Human judgment
Help experienced practitioners interpret weak signals, improve experiment design, and decide what the evidence means.
Fit check
When do you not need an experimentation platform?
More process is not automatically better. An experimentation platform earns its place when the organisation needs repeated, comparable decisions across teams.
- A team has one low-risk idea and can run a simple test with existing tools.
- The decision has already been made and the remaining job is project delivery.
- The team only needs a one-off survey or usability check.
- The organisation cannot collect customer evidence or will not act on the results.
- The main question is which live variant performs better, so a post-build A/B testing platform is the better fit.
The need becomes stronger when multiple teams must run experiments repeatedly, preserve evidence, compare opportunities, and explain investment decisions to leadership.
Rapidly
Rapidly turns enterprise ideas into evidence before build
Rapidly is a pre-build experimentation platform for enterprise teams that need to decide which product, service, innovation, or AI ideas deserve investment. It is the software layer around a disciplined experimentation practice, not a replacement for practitioner judgment.
- Capture ideas and strategic context in one experiment record.
- Structure Lean Canvases, hypotheses, riskiest assumptions, and evidence targets.
- Guide pretotyping experiment design without claiming to automate every test.
- Track behavioural evidence, learning, and the limits of the signal.
- Record decisions to back, pivot, park, stop, or test again.
- Give leaders visibility across a portfolio of experiments.
- Use AI to accelerate first drafts and experiment design while customer behaviour remains the decision evidence.
- Work upstream of project management and A/B testing systems rather than replacing them.
4,000+
experiments run
$30M+
costs saved
50+
enterprise teams
At Tabcorp, teams ran 130+ experiments, saved $12M, reached a validated decision in as little as eight days, and worked across more than ten squads in parallel during a 24-month embed. AGL saved $7.5M+, while RACQ stopped a bad idea in three days. These are operating outcomes from Exponentially and Rapidly engagements, not software-generated predictions.
Read the Tabcorp case study, the AGL case study, or see Rapidly’s idea validation software for enterprise teams.
Comparing categories? See idea management versus evidence-based experimentation.
FAQ
Experimentation platform questions
What is an experimentation platform?
An experimentation platform is software that helps teams design tests, collect evidence, and make decisions from the results. Some platforms compare variants in live products. Others support pre-build experiments that test whether an idea, service, workflow, or feature deserves investment before a team commits to delivery.
What is the difference between an experimentation platform and A/B testing software?
A/B testing software compares versions of an existing product, feature, page, or customer experience. A pre-build experimentation platform starts earlier, with an idea and its riskiest assumptions. It helps the team collect evidence for a build or funding decision before there is a finished experience to split-test.
What does an enterprise experimentation platform do?
An enterprise platform gives multiple teams a consistent way to frame assumptions, choose experiment methods, define evidence thresholds, record results, and make decisions. It also gives leaders portfolio visibility across experiment status, evidence quality, learning, velocity, and the ideas that were stopped before they consumed delivery capacity.
Can you run experiments before a product exists?
Yes. Pretotypes such as fake doors, landing-page tests, concierge tests, and Wizard of Oz tests create a believable experience without building the complete product. The goal is to observe a precise customer behaviour that reduces uncertainty about demand, use, or commitment before the organisation invests in full delivery.
What is pre-build experimentation?
Pre-build experimentation tests the assumptions behind an idea before it becomes a funded project. A team identifies what must be true, writes a falsifiable hypothesis, chooses an evidence threshold, runs the smallest credible test, and decides whether to back, pivot, park, stop, or test the idea again.
How is an experimentation platform different from idea management software?
Idea management software is usually designed to collect, organise, score, and vote on suggestions. Experimentation software moves selected ideas into hypotheses, tests, behavioural evidence, and decisions. The categories can work together, but internal enthusiasm and voting are not substitutes for evidence from the people expected to use or buy the idea.
How is experimentation software different from project management software?
Project management software coordinates delivery after an organisation has decided to proceed. Experimentation software supports the earlier uncertainty: whether the idea deserves people, budget, and momentum. Rapidly works upstream of delivery systems, then preserves the evidence and decision that explain why a project should begin, change direction, or stop.
Do teams need both Rapidly and an A/B testing platform?
Many enterprise teams need both because they solve different decisions. Rapidly supports the upstream decision about whether an idea deserves investment. An A/B testing platform supports downstream optimisation once a product or experience exists, has instrumentation, and receives enough traffic for a reliable comparison between live variants.
What evidence should an idea experiment collect?
The evidence should match the riskiest assumption and the decision. Useful signals often involve behaviour such as attempting to access a proposed feature, booking a conversation, committing time, paying a deposit, completing a task, or returning to use a service. Survey preference alone is usually weaker than observed action.
When is an experimentation platform unnecessary?
A platform may be unnecessary for one low-risk test, a one-off survey, or a decision that has already been made. It is also the wrong fit when the only question is which live variant performs better. The need grows when teams run repeated experiments and leaders need comparable evidence across a portfolio.
Decide which ideas deserve the next dollar
Bring the portfolio, not just one polished pitch. We will talk through the decisions your teams need to make and the evidence they can collect before build.