A/B testing agency in Mumbai, India
Search Savvy designs, builds and analyses A/B and multivariate tests with hypotheses drawn from research, sample sizes planned in advance and results reported honestly. You keep the changes that genuinely lift leads and sales, and drop the ones that only looked good.
What businesses ask before they start experimenting.
The whole experimentation cycle, from first hypothesis to rolled-out winner.
Traffic, conversions, tracking and tool choice reviewed before you spend on tests.
Test ideas written as clear hypotheses and ranked by impact, confidence and effort.
Minimum detectable effect, sample size and run time agreed before launch.
Copy, layout, offer, form and checkout variations designed from research.
Variations built and tested across browsers and devices to avoid flicker and bugs.
The right test type chosen for the question, page and traffic you have.
Messages tested for segments such as new visitors, returning users or paid traffic.
Results analysed for significance, revenue impact and segment differences.
Winners implemented and every result documented to guide the next test.
A disciplined process that protects you from false winners.
Analytics and user research produce a clear "if we change this, then that, because" idea.
Primary metric, guardrail metrics, audience and sample size fixed in advance.
Variations coded and checked on real devices before any visitor sees them.
Traffic split checked for errors, with no peeking at results to decide early.
Results read for significance, revenue effect and segment behaviour once the test completes.
Winner rolled out or idea retired, and the learning recorded for next time.
Choosing the wrong method is the quickest way to run a test that never reaches an answer.
| Feature | A/B test | Multivariate test |
|---|---|---|
| What changes | One version against another | Several elements in many combinations |
| Traffic needed | Moderate | Much higher, as visitors split across combinations |
| Best for | Big ideas, new layouts, offers and copy | Fine-tuning elements on high-traffic pages |
| What you learn | Which version wins | Which elements and pairings matter most |
| Speed | Faster to reach a result | Slower to reach a reliable result |
| Complexity | Simple to build and read | Harder to design and interpret |
Most failed testing programmes fail on method, not on ideas.
Calling a winner after a lucky streak produces false results that disappear after rollout.
Low-volume pages cannot detect small changes, so tests run for months and prove nothing.
Button colour tweaks rarely move results. Meaningful changes come from research.
If goals double count or miss events, the test crowns the wrong version.
A sample ratio mismatch signals a technical fault that invalidates the result.
Without a test library, teams repeat old ideas and lose insight.
Your visitors now come from AI answers and automated campaigns, so your tests should too. See AI search optimization
Visitors from ChatGPT, Perplexity and Gemini arrive pre-informed, so we test pages that confirm the answer fast and show the next step.
Tests are judged on revenue or qualified leads, not clicks, so winners help ad platforms optimise for customers.
Performance Max and Advantage+ send varied traffic, so we test message match across different intents.
Related services that supply test ideas, clean data and the pages you test.
Straight answers about experiments, statistics and how our testing service works.
Still have a question? Ask usSearch Savvy's A/B and multivariate testing service includes a testing readiness assessment, hypothesis and roadmap development, sample size planning, variation design, development and quality checks, A/B, split URL and multivariate tests, analysis and reporting, rollout of winners and a documented learning library.
The traffic you need depends on your current conversion rate and the size of improvement you want to detect. Pages with a few hundred conversions a month can usually run reliable tests. Lower-traffic sites benefit more from research-led fixes and user testing than from formal split tests.
An A/B test should run until it reaches the sample size calculated before launch, and for at least one to two full business weeks so weekday and weekend behaviour are both included. Stopping early because one version looks ahead is a common mistake that produces false winners.
Statistical significance estimates how likely it is that a difference between versions is real and not random chance. A 95% confidence standard means a result this strong would appear by chance in roughly one in twenty tests where nothing had changed, so it still needs sensible interpretation.
We use platforms such as VWO, Optimizely, AB Tasty and Convert, chosen to fit your traffic, site and budget. Google Optimize was retired in 2023, so we recommend a tool that integrates with GA4 and your consent set-up, and can be owned by your team.
Properly run tests do not harm SEO. Google has advised using rel="canonical" on split URL variations, 302 redirects for temporary tests, and ending tests once a winner is found. We follow these guidelines and avoid showing search engines content that differs from what visitors see.
If traffic is too low for reliable split tests, we focus on higher-impact work: fixing tracking, usability testing, session recording review and implementing evidence-backed improvements, then measuring before and after. Formal testing can begin once volume supports it.
No. Honest testing cannot guarantee any single result, because some tests lose and that is how you learn. What we commit to is a rigorous process, accurate measurement and clear reporting, so over time you keep the changes that raise conversions and avoid the ones that hurt.
Tell us about your website, traffic and conversion goals. Our CRO team will check whether you are ready to test and reply with your first test ideas within 48 hours.
Talk to our team about experiments built on accurate tracking, real research and statistics you can trust.
