Multivariate Testing and A/B Testing Simplified: How to Test Webpage Optimization Techniques

Introduction:

A lot of applications and websites are commonly utilized with minimal consideration devoted to their development or optimization. Nevertheless, if there’s a flaw, it’s improbable users will revisit the website or application. In order to provide a satisfactory product, comprehension of webpages’ optimization methods is vital.

The thing to consider is: What is required for successful optimization?

A Study on Multivariate and A/B Testing

In the context of website optimization, “it” often refers to functionality, which can be tested through rigorous QA processes. However, “it” also extends to design, content placement, even color schemes – elements which impact how the product is perceived by users. Multivariate Testing and A/B testing aid in optimizing these facets.

In this article, Multivariate (MVT) testing and A/B testing will be thoroughly discussed. Both techniques are geared toward boosting conversion rates for websites.

Understanding Multivariate Testing

Let’s illustrate with an instance where a webpage desires to evaluate a particular page’s effectiveness by testing its image and related text. The webpage filters down to two respective images and sentences. The possible combinations are:

1) Image 1:

2) Image 2:

3) Headline/Sentence 1: “The Objective Should be Total Absence of Accidents”

4) Headline/Sentence 2: “Our MOTTO: Accident-Free”

Potential Combinations:

In this scenario, the variations of image and caption combinations are tested to find the most impactful fit. This highlights the essence of Multivariate testing.

Technically, the formula to calculate the number of combinations to be tested is:

[# Variations on Element A] X [# Variations on Element B] = [Total # Variations]

In this scenario, there are 2 variations for the caption and image each. Therefore, there are 4 combinations to be tested together to find the most effective combination.

• The goal of Multivariate testing is to quantify and evaluate the effect of each variation mix on the final product.
• Sufficient traffic must be gathered for a test that can determine the best-suited design.
• The performances of each variation mix are juxtaposed to find the best design for obtaining the ultimate objective.
• These statistics offer insight into the alterations’ impact and user engagement.

This continuous cycle of multivariate testing, design improvements based on findings, and attainment of business objectives is known as Landing Page Optimization. It encompasses testing several variations, collecting statistical data, and adapting based on the results obtained. Multivariate testing is applicable not only to websites but also mobile apps.

Multivariate testing is integral to an effective internet marketing strategy.

Differentiations in MVT testing:

There are diverse types of multivariate testing depending on the distribution of traffic to different variations:

a) Full Factorial Testing: All possible combination variations are tested equally until the optimal one is found. This method is reliable and yields statistically valid results, but requires abundant website traffic.

b) Fractional or Partial Factorial Testing: Only some of the variation combinations are exposed to website traffic. Mathematical calculations and analysis aid in determining the best conversion rate.

c) Adaptive Multivariate Testing: This novel approach utilizes real-time visitor engagement to decipher the optimum variation combination.

d) Discrete Choice: This methodology analyzes consumers’ trade-off during purchasing decisions by varying elements or content attributes systematically.

e) Optimal Design: This method includes iterative testing of the maximum number of creative variations in minimum time to find the ideal solution, taking into account relationships, interactions, and limitations across content elements.

Multivariate testing can optimize internet marketing by discerning what should be implemented or avoided. It offers superior insights into the influence of variables or elements on the conversion rate, along with allowing for adaptable design and layout alterations.

Executing Multivariate Testing

1. Spot the Problem: Identify the area that requires enhancement such as low download-button click-through rates.

2. Develop Hypothesis: Construct a hypothesis for improving the webpage, such as making the download button more noticeable.

3. Generate Variations: Pinpoint key variables and generate variations, like varying button designs or headlines.

4. Determine the Sample Size: Compute the volume of traffic and duration needed to conduct the test and accumulate statistically significant data.

5. Test Your Resources: Ascertain that all tools are accurately functioning, and the webpage or app is operational before initiating the test.

6. Begin Directing Traffic: Channel traffic to each webpage variation.

7. Analyze Test Outcome: After the test has been conducted for a suitable duration, assess the results through statistical data analysis.

8. Learn from Test Outcomes: Utilize test results to acquire insights and improve the webpage or app for subsequent tests.

Avoid erroneous practices during Multivariate testing, like incorrect variant choice, premature or protracted test termination, focusing on irrelevant indicators, and failure to analyze results for necessary alterations.

Essential best practices include a preview of all variant combinations, concentrating on combinations with high impact on the conversion rate, and estimating webpage traffic needed for significant statistical data collection.

Multivariate testing has both upsides and downsides. The benefits include a comprehensive understanding of how variables affect the conversion rate and flexibility in design modifications. However, drawbacks include longer test durations, need for substantial webpage traffic, and a more complex setup process.

Delving into A/B Testing

Another widely used method for webpage optimization is A/B testing, also known as split testing. In A/B testing two versions of a webpage are equally tested with visitors. The version yielding a higher conversion rate is declared victorious.

A/B testing is apt for redesigning webpages with varying concepts to enhance conversion rates.

Pluses and Minuses of A/B Tests:

Benefits of A/B testing include easy setup, dependable results with low traffic, rapid testing, compatibility with various technologies, and suitability for layout and content modifications.

Limitations of A/B testing entail restrictions on the quantity of simultaneous adjustments and lack of insight regarding the effect of different variables on one another.

Comparison: A/B Testing vs Multivariate Testing vs Split Testing

A/B testing, multivariate testing, and split testing (server-side adjustments) are all diverse methods implemented for UX variant testing. They differ on the basis of traffic distribution and the amount of changes permissible.

A/B testing divides traffic among distinct webpage versions, whereas multivariate testing combines variables to create versions. Split testing entails testing two different webpages against one another. A/B testing demands less traffic and tests one variable at a time, making it apt for redesigning. Multivariate testing, on the other hand, requires more traffic and tests multiple variables concurrently, which optimizes existing webpages without substantial redesign.

An Examination of Multivariate Testing

Split URL testing is another variant of multivariate testing involving server-side adjustments. It’s utilized for comparing two different webpages and is particularly useful when deciding on the most effective design for landing pages.

Instruments for A/B / Split / Multivariate Testing

The market is lined with various tools for A/B, split, and multivariate testing, for instance, Google Optimize, Optimizely, VMO, Qubit, Maxymiser, and AB Tasty.

Conclusion:

Both A/B testing and multivariate testing are potent methods for enhancing conversion rates, refining performance, and optimizing webpages and applications. Each technique comes with its specific pros and challenges, and it’s crucial to pick the best suited method based on requirements. As testers, comprehensive and careful testing of these changes is important as they directly influence business profits.