13 AB Testing Best Practices

A/B testing is a critical tool for optimizing digital experiences. It allows you to directly compare two versions of a page, ad, email, or other component by showing one variation (version A) to some visitors and a different variation (version B) to others. 

By tracking key metrics like conversions, clickthrough rate, or time on page for each variant, you can determine which version performs better. 

Running A/B tests is essential for companies today because it enables data-driven improvement of customer experiences. Rather than guessing what will optimize conversions or engagement, A/B testing eliminates conjecture by putting your ideas to the test. 

The data reveals how changes impact real customer behavior. This allows you to iterate based on evidence, increasing key performance indicators over time. 

With the fierce competition online, A/B testing is no longer optional – it’s necessary to stand out, remove friction, and deliver the experiences customers want most.

In this article, we’ll dive deep into AB testing best practices across several key sections.

Let’s get started.

A/B testing best practices during the planning stage

Clearly define goals and hypotheses upfront

Begin by clearly articulating your goals for the test. Go beyond vague notions of “increasing conversions” and define exactly which metric you want to improve. State the baseline performance and your target numeric increase. For example, “Increase landing page conversion rate from 2.5% to 3.5%.” Quantifiable goals clarify what success looks like.

With clear goals set, formulate hypothesis statements about which changes will drive improvement. Hypotheses connect design tweaks or content changes to the desired lift in your goal metric. For instance, “Removing the lead magnet popup on the blog homepage will increase time on page by 15%.” This ties the design change of removing an element to the anticipated impact on site engagement.

Craft multiple hypotheses about different variables that may influence your goals. Prioritize the hypotheses with the largest potential impact for testing. For each priority hypothesis, outline what variations you will show to test it. These become the A and B versions of your test.

Document all goals, hypotheses and planned variants before beginning test setup. This upfront investment in planning is time well spent to shape tests that produce actionable insights. Structure tests around clear hypotheses and metrics to learn what really moves the needle for your goals.

– Prioritize elements with high traffic or conversion potential

Prioritize elements with high traffic or conversion potential. Focus your testing on pages that get the most visits, like your category pages or product pages. These high-traffic pages have the most opportunity to influence key metrics.

Analyze your analytics to identify top entry and exit pages. Sort pages by traffic volume and bounce rates. Pages with high entrances and exits are priorities—test changes to grab attention or reduce departures. For example, refresh a stale homepage visitors exit quickly from.

Map the user journey and find friction. Study your conversion funnel and workflows. Where do customers commonly encounter speed bumps or extra steps? Prioritize optimizing these pain points. For instance, if checkout abandonment is high, test ways to simplify the process. Smoothing rough edges fuels conversions.

Define your primary success metric like conversion rate or time on site. Then analyze which pages have the biggest influence on this goal. Test pages correlated with your KPI first. Start with quick wins that build momentum.

Leverage internal search data. See which on-site content brings traffic but has low engagement. Refreshing these pages can better attract and retain visitors.

Maximize impact by testing pages with high visibility and potential first. Traffic volume, bounce rates, funnel friction, correlations—use data to inform what you test.

– Limit scope to isolate key variables and avoid too many changes 

Only test one or two variables at a time. Comparing multiple changes in A/B tests muddles insights about what impacted metrics. For example, test button color OR test button text, but don’t test both together.

Isolate key variables like headlines, copy, or calls-to-action. Don’t make dramatic page-wide changes that disrupt layout or information architecture. For instance, keep page sections the same and solely test introducing/removing a sidebar widget.

Don’t drastically redesign page layout or content flow. Radical changes make it impossible to connect results to specific elements. Users may bounce simply because the experience is too disrupted. Keep layout, imagery, and content structure identical between A/B versions.

Follow the principle of least change. Only modify what’s essential to test your hypothesis. If you want to test how a longer homepage headline performs, every other element should be identical between versions. Don’t simultaneously increase body text size. Introducing too many variables pollutes data.

By limiting test variables and scope, you gain insights about how isolated changes impact metrics. This disciplined approach provides clear learning to optimize digital experiences.

– Determine appropriate sample size and test duration

Use power calculators to estimate minimum sample size needed. Power calculators factor in your traffic numbers, baseline conversion rate, and desired minimum detectable effect. Plugging in values provides the minimum visitors required per variation.

Set test duration for at least 1-2 weeks. Shorter tests may not achieve statistical significance. Allot time to allow your minimum sample size to be exposed to the test variations. Don’t end tests prematurely.

Avoid small samples prone to random variance. For example, if 100 visitors per variation is needed but the test is stopped at 50, results are unreliable. Stick to power calculator guidance.

Account for seasonality in traffic. Tests may need to run longer during slow periods to reach sample sizes. Plan duration based on low-traffic projections.

Build in buffer time for analysis. End tests on Fridays to allow time to analyze results before the next week begins. Don’t stop tests abruptly without assessing data.

Determine significance thresholds like 95%+ confidence level and 0.5%+ difference between variants. Test until variations achieve your targets.

Properly setting test length and minimum sample size provides statistically significant results upon which to base optimization decisions. Take the guesswork out with power calculators.

AB testing best practices during the implementation phase

– Use proper technical set-up of identical pages except for one variable

A critical foundation of effective A/B testing is constructing technically identical test variations that isolate the variable being analyzed.

Start by duplicating the target page – for example, making a copy of your product page called product-variant. The original page and the variant should be completely identical in layout, imagery, calls-to-action, and all other elements. Then, update the isolated variable on the variant page to reflect the change being tested. If testing a different product image, swap in the new image only on the product-variant page while keeping the original unchanged.

With identical pages except for one element, you can clearly measure the impact of that variable. Ensure to implement tracking codes on both the original and variant pages linking to your desired success metric. Use a persistent URL structure and equal traffic split between versions so visitors consistently see the same page. Consistent exposure and measurement allows a fair comparison untainted by technical factors. No other aspects – site speed, server location, etc. – should differ.

Setting up clean, properly structured A/B test pages is crucial to isolate your variable and obtain reliable insights. Take care to duplicate all other elements before changing just the single factor being tested. This discipline in technical implementation lays the foundation for statistically significant results.

– Ensure full functionality on both page versions

Ensuring full functionality on both A/B test variations is crucial for accurately assessing the isolated variable’s impact. Before launching your test, thoroughly test all interactive elements like forms, buttons, dropdowns and links on each page version. Click through every flow yourself, confirming that navigation, calls-to-action and other links direct users seamlessly to the intended destinations on both original and variant pages. Verify that any media like images, videos or slideshows display properly without glitches or slowing page loads.

Have developers review code to catch errors that could cause crashes selectively on one version. Examine analytics regularly during the test for anomalies like spikes in 404s. Address any technical issues early that skew metrics away from the experience of your isolated variable. Make sure both pages are responsive across device sizes without content overlap or horizontal scrolling on mobile that would detract from mobile experience.

Consistent functionality is key so that users encounter equivalent experiences on both A/B test pages. Smooth end-to-end flows with no technical distractions or hindrances allow you to directly measure how your isolated variable impacts engagement or conversions.

– Drive sufficient traffic to both versions for statistical significance

To obtain statistically significant results from A/B testing, sufficient traffic must be driven to each variation. Manual splitting introduces errors, so leverage testing tools to automate even distribution of visitors between the A and B versions.

50/50 splits are ideal for clear data. While leveraging advanced targeting to direct more of your ideal customer traffic can help achieve sample sizes faster, maintain the even split between variations.

Continually inspect reports to ensure consistent traffic ratios throughout the test’s duration. Watch for lopsided exposure between versions that could incorrectly skew metrics. End tests on Fridays to leave time for thorough analysis before operationalizing any winning changes the next week. Don’t stop tests abruptly without reviewing the data.

Automated testing tools scale your experiments across pages while handling technical requirements like persistent URLs behind the scenes. Driving sufficient traffic in a disciplined manner is key to achieving sample sizes that generate confidence in the statistical significance of results. Careful monitoring for imbalances provides quality data.

– Monitor data in real-time to catch any issues

Effective monitoring of A/B tests requires going beyond setting up experiments and letting them run unattended.

To maintain data integrity, set up real-time dashboards in your testing tool to track key metrics as results come in. Review these dashboards and reports frequently, watching for performance discrepancies between variations or unexpected swings that may indicate technical issues.

Check that your success metric is being accurately captured on both versions. Monitor traffic splits closely to ensure sufficient sample sizes with no lopsided exposure skewing data. As results accumulate, regularly assess statistical significance to determine if metric differences are meaningful and not just normal variance.

Analyze funnel performance to find where drop-off differs between versions. Active vigilance identifies issues early so you can pause tests and address problems. Ongoing inspection provides quality control. By regularly reviewing real-time data and statistical significance, you can confidently determine which variation delivered the meaningful improvement to optimize.

– Stick to test duration and don’t stop early

When running A/B tests, it’s critical to stick to the full test duration rather than ending experiments prematurely. The temptation often arises to stop tests early when initial data directionally favors one variation.

However, this risks introducing confirmation bias and stopping before statistical confidence is achieved. Avoid impulsively closing tests, making changes, or declaring winners based on incomplete data or just because results “feel” right. Instead, diligently follow your pre-determined timeline and allow tests to run to completion per sample size calculations.

Regularly review power metrics to confirm when minimum thresholds are reached. Account for seasonal traffic fluctuations that may necessitate timeline adjustments as well. While impatience for results is understandable, remaining disciplined in your duration process ensures decisions are backed by reliable, significant data.

Letting tests fully play out provides learnings that can inform optimization even when results seem inconclusive. Sticking to timelines helps prevent bias and yield quality insights.

A/B testing best practices during the post-test analysis

– Check for factors that may have skewed results

When analyzing A/B test results, it’s crucial to thoroughly review the data for factors that may have skewed or inflated differences between the variations.

This helps validate that the performance variance was truly driven by the change you tested. Examine analytics over the full test duration as well as segmented by period, traffic source, geography, device etc. to uncover any anomalies not attributable to the isolated variable.

Review technical metrics to catch site errors selectively impacting one variation. Statistical significance calculations also assess if metric differences are unlikely due to random chance.

Additionally, compare user behavioural flows between versions to better understand the reasons behind performance variances. Discuss results with customer-facing teams to surface any user complaints potentially related to test issues. Vetting results to rule out seasonal, technical, geographical or other variability lends confidence that the outcomes accurately reflect the variable you modified. A rigorous audit provides assurance the data credibly informs optimization decisions.

– Share results internally and align on next steps

After completing an A/B test, it’s crucial to thoroughly share the detailed results and analyses with all involved stakeholders across teams like product, marketing, UX and engineering.

Discuss learnings and user behavioural insights in addition to just data. Build consensus on which variation delivered measurable improvements while acknowledging any data limitations. Solicit diverse perspectives on possible reasons driving results and considerations before optimizing.

Work cross-functionally to align on a prudent plan, like first rolling out the winning version only on the specific page tested and monitoring closely.

Brainstorm ideas for thoughtful expansion to related pages with input from partners. If gains continue, get buy-in from executive sponsors for a broader rollout.

Set timelines for follow-up testing and document all methodology learnings, next steps and owner responsibilities. Inclusive discussion and sharing test analyses not just outcomes brings everyone onto the same page regarding how best to apply findings for maximum optimization uplift.

– Document insights, recommendations, and follow-ups

Thoroughly documenting A/B testing learnings, analyses, recommendations and follow-ups is crucial for capturing insights to guide future efforts.

Write a comprehensive report that outlines the original hypothesis, methodology, detailed results analysis, statistical significance, limitations, recommendations for optimization, and change implementation risks and plan. Capture key behavioural insights and strategic takeaways gleaned about customers.

List new hypotheses generated for follow-up prioritized by potential impact. Store reports centrally to retain institutional knowledge. Set reminders to review metrics over time post-optimization.

Update test plan documentation with lessons learned for reference. Complete documentation preserves details, analysis, and strategic direction so hard-won insights remain accessible. This knowledge bank continuously informs institutional learning and future optimization efforts for maximum impact over time.

– Optimize winning version and expand test

When optimizing based on A/B test results, take a phased, measured approach to rolling out changes. Start by implementing the winning variation for all users, but only on the specific page that was tested.

Closely monitor performance daily at full scale before expanding to other pages. Once you’ve gained confidence that gains persist site-wide, thoughtfully expand the change to related pages with similar layouts, audiences and goals, customizing minimally per context.

Continue monitoring each rollout incrementally, tweaking elements based on real user data. If improvements continue site-wide, advocate for full implementation across all applicable pages.

Consider additional follow-up tests to further iterate and build on your original findings. Capture all expansion iterations, performance data and results for shared learning. Gradual optimization focused on incrementally validating improvements page-by-page allows winning experiences to be strengthened site-wide while controlling risks.