Introduction To MultiVariate Testing: A Complete Guide (2023)
Multivariate testing, also known as multivariant testing, is a method of experimenting with multiple variables on a website or mobile app to determine which variation performs the best. The goal is to identify the combination of variations that drive a desired outcome, like more conversions or higher revenue.
Multivariate testing is extremely valuable for making data-driven decisions about changes to your site or app. Testing multiple variables at once allows you to understand how different elements interact and impact each other. This provides deeper insights than changing one variable at a time. By identifying the optimal combination of variables, you can significantly improve key metrics like conversion rates.
In this article, you will learn what multivariate testing is, why it is important, how to design and run a multivariate test, and how to analyze and interpret the results. With this knowledge, you will be equipped to leverage multivariate testing to make smart decisions that improve your website’s or app’s performance.
Table of Contents
What Is Multivariate Testing
Multivariate testing, sometimes called multivariant testing, is a method of experimenting with multiple variables on a website or mobile app simultaneously. The goal is to determine which combination of variations performs the best to drive a desired outcome.
In a multivariate test, you select multiple elements on a page to vary at the same time. For example, you may test different headlines, call-to-action button colours, and image placements, and form layouts together in a single experiment. You can create different variation combinations and serve them randomly to visitors to see which performs the best.
This differs from A/B testing, where you only test two variants against each other, changing just one element at a time. With multivariate testing, you can test the interactions between different page elements and how changing multiple variables together impacts success metrics.
The key advantage of multivariate testing is that it provides a deeper understanding of the variables that influence performance. By testing multiple changes simultaneously, you can identify the optimal combination that drives the highest conversion rates, revenue, or other goals.
Importance Of Multivariate Testing
1. Helps marketers find the best combination of elements
Multivariate testing is invaluable for marketers looking to optimize ad campaigns across multiple dimensions.
Key variables that can be tested together include ad messaging, imagery, calls-to-action, audience targeting, placement, timing, and more.
By creating different combinations of these campaign elements and exposing audiences to them in a controlled experiment, marketers gain data-driven insights about what works best.
For example, testing different ad images with various types of copy and calls-to-action can uncover what imagery and text resonates most with each target customer segment.
These insights allow marketers to fine-tune campaign elements and allocate a budget toward what drives the highest conversion rates and ROI.
2. Helps in improving user experience
Multivariate testing is critical for UX/UI professionals looking to elevate user engagement, satisfaction, and conversions.
By testing different page layouts, UI flows, microcopy, visuals, and other design elements together, UX researchers gain a nuanced understanding of what combination of variables optimizes the user experience.
Testing can uncover how page layout impacts user attention, how microcopy influences behaviour, how visual cues affect perceptions, and how flows drive conversions.
With these multivariate insights, designers can craft targeted solutions. For example, shortening forms, enhancing navigational cues, simplifying instructions, and highlighting calls-to-action can together transform bounce rates.
Multivariate testing empirically models the complex relationships between design variables, equipping UX teams to build interfaces with the optimal balance of elements to delight users.
3. Informs Data-Driven Decision Making
The overarching benefit of multivariate testing is enabling data-driven decision-making. Rather than rely on hypotheses and guesses, multivariate testing allows for different ideas and combinations to be tried out and objectively evaluated.
By gathering performance data on multiple variables under live conditions, teams get clear validation about what works. These evidence-based insights justify meaningful investments of time and budget into the ideas that drive metrics like conversion rates and revenue.
Stakeholders can align around decisions backed by sound multivariate testing data.
Additionally, the insights uncovered allow for continual optimization over time as new concepts are tested against the current best performers. With multivariate testing, website and mobile app owners reap the benefits of innovation grounded in empirical facts rather than opinions.
4. Reduces Risk
Implementing changes without properly testing them first carries substantial risk.
Rolling out untested website, app or marketing changes can lead to wasted development time and resources if they fail to improve metrics.
Additionally, unvalidated changes run the risk of accidentally alienating or frustrating users by altering experiences in unvetted ways. Without seeing how changes impact performance, teams run the risk of making choices based on assumptions instead of data.
Multivariate testing minimizes risks by validating proposed changes empirically first. Testing changes simultaneously provides insights into interactions between variables that may produce unexpected results.
This allows teams to catch issues and correct course prior to rolling changes out fully. By taking the guesswork out of decision-making, multivariate testing builds internal stakeholder alignment around choices grounded in performance data.
Resources can be invested more confidently into proven solutions. In essence, multivariate testing reduces the risks associated with unvalidated changes – ensuring efforts remain focused on what will best serve users and move the business metrics forward based on evidence.
Preparing For Multivariate Testing
1. Clearly Define the Testing Goal
Defining a clear, measurable goal is crucial for focusing the multivariate test and evaluating results. Goals should directly tie to key business or user experience metrics that stakeholders want to improve.
For an e-commerce website, common goals are increasing conversion rate, average order value, or overall revenue per visitor. For user engagement, goals could be reducing bounce rate, increasing pages per session, or boosting return visits.
Avoid vague or fuzzy goals like “improve performance” and instead precisely define the specific metric you want to optimize. Having a concrete, well-defined goal metric allows you to properly assess the significance of results.
It also enables accurately calculating projected impacts to the business, like the expected revenue lift from a 2% conversion rate increase.
A focused, quantifiable goal aligns the test around outcomes that connect to broader business priorities, which aids in gaining stakeholder buy-in. Overall, dedicating time upfront to define a clear testing goal focused on moving the needle for a key metric sets the experiment up for delivering tangible business value.
2. Select the Most Impactful Variables to Test
The specific page elements and experiences selected as test variables should have a clear, logical relationship to the defined goal metric.
For an e-commerce website, key variables likely include product page content and design, as these are what visitors directly interact with when making a purchase decision.
Consider elements website visitors need to view or use in order to convert, like headlines, product descriptions, images, pricing, specifications, and calls-to-action.
Prioritize testing variables that exist within the conversion path itself, such as checkout flows. The overarching aim is to identify and test the elements on a page that are highly likely to influence visitor behaviour related to the goal metric if optimized.
Document specific hypotheses about exactly how changing each selected variable may affect the goal, whether it be enhanced trust in a product through more detailed descriptions or higher perceived value through more premium images. Limit testing variables to the highest-impact areas on a page or within a user flow to maintain focus on changes that can meaningfully move the goal metric.
3. Design High-Quality, Balanced Variations
For each variable selected for testing, design well-crafted variations that will generate statistically meaningful data. Avoid extremes such as comparing a highly polished, premium variation against a very low-quality, distracting variation.
The different variations should be realistic options for a business to consider implementing.
Test 3-5 variants for each element to achieve balance, such as testing different headlines of similar length, formatting, and clarity. When testing visuals, ensure styles and production quality are comparable across variants.
The aim is to isolate the impact of individual variations, rather than having one variation skewing results through extremely high or low quality. Testing balanced, high-quality variations enhances the statistical validity of results and provides insights that can inform impactful changes.
4. Map Logical Variable Combinations
With multiple variables, determine reasonable combinations to test together in order to assess interaction effects between elements. Seek to test combinations where variables are likely to relate and collectively influence user responses and behaviour.
For example, pair aligned content messaging with related images or videos. Limit the total number of combinations to maintain statistical power as combinations divide overall test traffic.
To maximize test efficiency, prioritize mapping combinations between variables with the highest potential individual impact. Additionally, coordinate variation details so that combinations make sense when paired together and maintain a logical user flow. Testing logical, coordinated variable combinations optimizes learning while maintaining a streamlined, engaging experience.
How To Conduct A MultiVariate Test
Step-By-Step Guide To Designing Test Versions
- Create a test variation matrix detailing every possible combination of variables and versions. Map these systematically to visualize the scope. A clear matrix prevents gaps in test design.
- Design wireframes or mockups for each variation combination. Use clear naming like “Var A – Headline 1, Image 2, CTA 3”. Visualizing combinations enables refinement. Create multiple mockups to compare versions.
- Manage variations in modular templates, like separate files for each headline or image. This makes it easy to mix and match variations within combinations. Maintain organized structures for efficiency.
- Nest variable templates within master designs. Enable quick swapping between options. Set up folders or libraries to access all variations on demand. Organization is key with multiple assets.
- Do holistic reviews of combinations against brand style guides. Check colors, fonts, tones for consistency. Refine combinations to adhere to branding. Present combinations professionally and logically.
- Create a usage matrix detailing which combinations will be shown to each visitor segment. Coordinate this allocation with dev teams. Methodically map combinations to visitors.
- Establish naming conventions for implementation like “button-version-A” and “image-option-B”. Ensure naming protocols are clear and consistent across platforms and teams.
- Centralize approved assets and materials for each combination in a single well-organized repository. Enable easy access for design, engineering, and analysis.
- Conduct comprehensive QA testing of fully assembled variations before launch. Check flows, formatting, functionality, and errors. Identify and resolve any issues.
- Verify analytics are tracking key metrics for each combination to support data analysis. Implement tagging like URLs with version identifiers.
Instructions On How To Deploy These Test Versions On The Platform
- Collaborate extensively with engineering teams throughout implementation. Align on naming conventions, tagging protocols, redirects, and requirements early on. Provide assets and documentation in an organized, clear manner. Have regular check-ins to track progress and address issues proactively.
- For websites, build out a flexible test page template that dynamically pulls in the appropriate variation assets for each visitor based on segmentation in the backend testing tool. Take advantage of features like Optimizely’s custom snippets. Use clear CSS naming conventions like “button-version-A” to cleanly switch variations.
- For mobile apps, create separate app versions for each variation combination in the code and submit these as distinct releases to app stores. Configure the testing tool to route various user segments to specific app releases. Document app release details and allocate enough testing budget.
- Set up seamless redirects to map visitors to the appropriately assigned test variation page or app without disruption. Insert identifiers like ?var=B into URLs and analytics to denote variations. Confirm redirects work across devices.
- Implement event tracking and analytics uniformly across all variations to capture interaction data. Pipe this data directly into the testing tool’s dashboards for centralized analysis. Tag events like “button-click-varC” to identify behaviours by variation.
- Perform extensive quality assurance testing on each implemented variation across all major browsers, devices, and use cases. Verify functionality, design, numbering, and flows. Identify and rapidly resolve defects pre-launch. Sign off QA verbally with teams.
- Start with an initial soft launch of lower traffic to confirm a smooth rollout before ramping up. Monitor metrics closely across variations and act fast if issues emerge. Have a rollback plan if needed.
- Maintain full documentation covering implementation details, naming conventions, redirects, tags, URLs, assets used per variation, app release info, QA outcomes, and more. Thorough documentation improves analysis.
How To Collect and Record Data
- Define core metrics upfront that align with test goals and business priorities. For example, focus on conversion rate, average order value, new signups, or engagement score. Limit metrics to critical ones to streamline analysis.
- Implement comprehensive event tracking across all variations to capture granular user behaviours. Track clicks, hovers, page scrolling, videos watched, content downloads, engagement time and more. This reveals detailed user journeys.
- Pipe all data directly into the centralized test platform dashboard for unified access and reporting. Eliminate manual compiling of metrics across tools for streamlined analysis.
- Follow strict tagging protocols attributing events and attributes to each variation like “button-click-varA”. This structures data to compare performance. Use naming conventions consistently across teams.
- Build scheduled reports on key metrics and create real-time dashboards of ongoing performance. Monitor dashboards daily to catch issues quickly. Establish alert thresholds linked to goals.
- Insert variation identifiers into page URLs like ?var=B to facilitate segmentation of metrics by version during analysis. Document conventions thoroughly.
- Conduct recurring data audits and statistical checks to validate completeness, accuracy, and consistency across sources. Identify any gaps or anomalies requiring correction.
- Meticulously document all data collection implementations, metrics definitions, calculation logic, dependencies, protocols, and locations. Detailed documentation prevents errors.
- Securely store raw data with redundancy, including dashboard exports, spreadsheets, and analytics exports. Process into analysis-ready formats using ETL tools or scripts.
Interpreting The Results Of Multivariate Testing
- Focus analysis on the specific metrics pre-defined upfront that align directly with core goals, such as conversion rate, average order value, subscriber retention rate, etc. Compare the performance of each variation against control on these key indicators.
- Supplement top-line metrics with detailed user behaviour data like click-through rates on buttons/links, scroll depth, video engagement, and page views per session. This reveals how users flow through variations.
- Look at metrics segmented by meaningful user cohorts like new vs returning visitors or geography. Any variance in responses to variations?
- Consider metrics in the context of external factors like seasonality, releases, and outages. How did confounding events potentially influence the environment?
- Leverage the multivariate testing tool’s statistical significance testing to validate metric differences exceeding 95%+ confidence as the minimum bar for identifying winners.
- Calculate lift vs the control for each variation’s metrics. Is the percentage increase large enough to meaningfully impact goals? Set lift effect size minimums.
- Visualize performance trends over time, side-by-side comparisons, and variation interaction effects through charts, graphs and heatmaps. Spot patterns.
- Review detailed user flows and behaviour data. Did combinations drive traffic to key pages as expected? Were unexpected interaction effects uncovered?
- Factor in qualitative assessments of ease of use, engagement, and branding fit. This contextualizes data.
- Recommend implementation of variations with statistically significant lifts on key metrics exceeding effect size minimums. Lifts should meaningfully impact goals.
- Be cautious of acting on small metric differences that fall within typical statistical noise levels. Seek directional validation through further testing.
- Document insights, projected impact on goals, implementation plan, optimization roadmap. Share results cross-functionally.
- Avoid over-optimizing for one metric without considering holistic experience. Evaluate strategic brand and UX alignment.
- Take an iterative approach optimizing through continuous incremental gains over time vs sporadic big swings.
Common Challenges and Mistakes In Multivariate Testing
- Running many variations spreads traffic too thinly across underpowered branches, preventing statistically significant clear winners from emerging above normal data fluctuations.
Solution: Carefully limit the number of variations and combinations tested based on available traffic volumes to maintain adequate statistical power in each branch.
- Without predetermining minimum statistical confidence and effect size thresholds upfront, normal data variance over time may be incorrectly interpreted as wins rather than inconclusive results.
Solution: Define minimum 95% statistical confidence and minimum effect size thresholds before testing that variants must exceed to be considered winners and move forward.
- Limiting data collection and analysis to just aggregate metrics like conversion rates provides no qualitative insights into changes in detailed user behaviours across variations.
Solution: Implement comprehensive event tracking and analysis across every variation to capture detailed user interaction data that reveals insights behind the metric differences.
- Constraining experimentation to just the homepage or landing page changes in isolation loses sight of critical cross-touchpoint effects across the entire user journey that shape outcomes.
Solution: Identify multiple significant experimentation points across the full user experience journey to account for cross-touchpoint interaction effects.
- Inadequate quality assurance testing of implementations often overlooks discrepancies between variations such as broken flows, error states, tracking issues or missing data that completely invalidate any attempted data analysis.
Solution: Perform extensive QA testing on each implemented variation to verify that expected behavior matches designs before launching live traffic to ensure data accuracy.
- Optimizing for isolated metrics in a vacuum can degrade the holistic user experience and brand alignment when evaluated from a broader strategic perspective.
Solution: Maintain a holistic perspective aligning proposed optimizations to overarching brand strategy and goals versus isolated metrics in isolation.
- Prematurely committing resources to implement initial data trends risks acting on normal data fluctuations before collecting sufficient representative data over extended time periods to validate results through repetition.
Solution: Collect adequate sample sizes over meaningful time frames and replicate results before committing resources.
Frequently Asked Questions About Multivariate Testing
1. What is multivariate testing?
Multivariate testing is a method of experimentation where multiple variables on a page are tested simultaneously to determine the optimal combination that achieves the desired outcome. It allows the testing of variable interactions.
2. How is it different from A/B testing?
A/B testing evaluates one variable at a time. Multivariate testing combines multiple variables into different variations for a single experiment. This reveals how variables impact each other.
3. What are some key benefits of multivariate testing?
- Test combinations of changes together vs. incremental one-variable tests
- Uncover interactions between variables
- Reduce the number of required tests and speed up optimization
- Gain greater insights from fewer versions and samples
- Identify positive and negative variable correlations
4. What metrics should I track?
Focus on key performance indicators tied to goals, like conversion rates, revenue, and lead gen form submissions. Supplement with detailed user behaviour data.
5. How many variations should I test?
Limit based on available traffic so each variation has a large enough sample for statistical significance. Too many underpowered branches produce unclear results.
6. How long should I run a test?
Run tests long enough to collect sufficient statistically significant samples. Test duration depends on variables like traffic levels. Let the data and tool indicate readiness.
7. What mistakes should I avoid?
Avoid too many variations, acting on insufficient data, over-optimizing for one metric, or failing to QA implementations before launch.
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