Category guide

Web experimentation:
the complete guide

Web experimentation is the ongoing program of testing website changes, measuring outcomes, and feeding learnings forward. A/B testing is one method inside it—this page explains the full discipline first.

Start A/B testing Read the guide See how it works

Web experimentation is the ongoing practice of testing changes on a website or web app, measuring their impact on defined outcomes, and feeding those learnings back into the next cycle of ideas. It sits above any single tool feature: programs exist because shipping opinions at scale is expensive, and structured tests reduce regret.

Where A/B testing sits in the stack

Web experimentation
├── A/B testing
├── Split testing
├── Multivariate testing
├── Personalisation
├── Feature experimentation
├── UX experimentation
└── AI-powered experimentation

Teams often say “we do A/B testing” when they mean a broader A/B testing habit inside a larger experimentation program. The program owns prioritisation, documentation, and standards; individual tests are the unit of learning.

The experimentation lifecycle

Ideation: opportunities from analytics, replay, support, and qualitative research.
Hypothesis: specific change, expected direction, and primary metric.
Variant creation: design, copy, or code changes isolated for fair comparison.
Implementation: snippet, flags, or CMS integration with QA on tracking.
Traffic allocation: who enters the test and how exposure is split.
Measurement: events, conversions, and guardrail metrics during the run.
Analysis: significance, segment checks, and practical significance (is the lift worth it?).
Learning and iteration: document outcome; queue follow-ups; roll out or revert.

CRO, personalisation, and product experimentation

Term	Focus	Relationship to web experimentation
CRO (conversion rate optimisation)	Improving conversion on key journeys	Often the business goal; experimentation is how you validate CRO ideas
A/B testing	Controlled comparison of two or more variants	Core method inside a web experimentation program
Personalisation	Different experiences by segment or context	Can be tested like any other experience; adds targeting complexity
Product experimentation	Tests inside logged-in product flows	Same statistical ideas; different surfaces and identity

How AI changes the workflow

AI-assisted tools can draft variants, suggest hypotheses from behavioural data, and monitor tests continuously. That shifts team time from production and dashboard babysitting toward strategy, prioritisation, and review. Categories like AI A/B testing, automated A/B testing, and prompt-based A/B testing describe different depths of that assistance.

RunPivot is one example of a platform oriented around this workflow: describe a test, review generated variants, launch with clear goals, and read live significance without rebuilding the operational layer for every experiment. See agentic experimentation for how the product frames continuous operation.

Related guides

A/B testing guide

Definitions, statistics, examples, and common mistakes.

AI A/B testing

What AI automates and where human review still matters.

Automated experimentation

Nine stages from discovery to learning loop.

From idea to live
winner in three moves

Try a sharper headline on the pricing page

Variant A

Variant B

Step 1

Prompt it

Type what you want to test. A sharper headline, a new offer, a different layout. RunPivot turns your words into ready-to-run variants.

Live2,418 visitors

Variant A · 50%Variant B · 50%

Step 2

Test it

Your experiment goes live on your real site in minutes. RunPivot splits traffic, watches results, and shifts visitors toward what's working.

Winner called95% significance

+30% conversion lift

Step 3

Ship the winner

The moment a result is statistically real, RunPivot calls it and rolls the winner out to everyone. No second-guessing, no stale tests running forever.

Why agentic
experimentation?

You describe the test. RunPivot is AI-native and closes the loop: launch, monitor, call winners, ship results.

Illustration of a stopwatch representing moving faster

Move faster

Describe the test you want in plain language and it's live in minutes. RunPivot builds the variants and handles the setup, so you go from idea to live experiment the same day.

Test smarter

Run better experiments without the statistics degree. RunPivot picks the right approach, shifts traffic toward what's working, and tells you the moment a result is real.

Illustration of a browser with rising bars representing scale

Scale without compromise

Fast, flicker-free experiences on every page. Run more experiments at once without slowing your site down or losing control of what goes live.

Prove impact

Every experiment ties back to revenue. Clear reporting connects each winner to the numbers your leadership actually cares about, so the value of your program is never in question.

One platform.
Three ways to win.

For marketers

Prompt-built variants
Visual editor for hands-on control
AI test suggestions drawn from your visitor behaviour

Launch what you can describe. Edit what you can see. Never wait on anyone.

For your visitors

Flicker-free delivery
Variants that match your brand automatically
Pages that get better week after week

They never see the A/B testing. They just get a better site.

For the program

Smart traffic allocation
Significance monitoring around the clock
Automatic winner rollout

Your experimentation program runs while you sleep.

One script tag.
Nothing to rebuild.

RunPivot works on any website. Add one line to your site and you're A/B testing the same day, without touching your website's code or waiting on anyone.

Works withWordPressWebflowShopifySquarespaceWixNext.jsCustom builds

Your data, your rules

AU Data Residency

Your experiment data can be kept in Australia, so it stays close to home and inside your rules.

Privacy-conscious handling

RunPivot doesn't build ad profiles or sell visitor data. No third-party tracking.

Security reviews supported

We work with your team through the security checks your organisation needs before going live.

Limited device signals

We keep only the limited technical signals needed to run experiments and protect your site.

Hosted on major clouds

RunPivot runs as hosted software on trusted, major cloud providers.

“We had three test ideas waiting for months. All three were live in an afternoon, and one beat control by 30%.”

Growth lead, RunPivot customer

Integrations

Connects to your existing tools

Send experiment results and conversion data to the tools your team already uses.

GGoogle Analytics

fMeta Ads

HHubSpot

SSegment

WWebflow

SShopify

GGoogle Ads

inLinkedIn Ads

▲Vercel

MMixpanel

CClarity

SStripe

Web experimentation FAQ

What is web experimentation?

Web experimentation is the disciplined practice of testing changes on websites and web apps, measuring impact on defined metrics, and using results to decide what to ship next. A/B testing is one method inside that program.

How is web experimentation different from CRO?

CRO is the goal of improving conversion. Web experimentation is how you validate CRO ideas with controlled tests rather than shipping unmeasured changes.

What is a web experimentation platform?

Software that combines variant creation, traffic allocation, measurement, analysis, and often program management (queues, documentation, integrations) for tests on web surfaces.

Do I need an experimentation program?

If more than one team ships web changes, a program prevents duplicated tests, defines standards for significance, and captures learnings. Ad hoc testing rarely compounds.

How does AI fit into web experimentation?

AI can accelerate ideation, variant drafting, and monitoring. The program still needs prioritisation, primary metrics, and review. See the AI A/B testing guide for detail.

What is feature experimentation?

Testing changes inside authenticated product experiences, often with feature flags. Statistical ideas match marketing A/B tests, but identity and assignment differ.