6 Steps to Boost Conversion Rates With Multivariate Testing
Testing is a crucial part of any online marketing strategy. It can help you figure out what customers want by telling you what is working and what is not. With this information you can then optimize your digital efforts and, in turn, increase leads and conversions.
There are many different types of testing you can engage in, from basic experiments where you try something new for a couple of months to A/B testing. However, for more advanced marketers, multivariate testing (MVT) is the next step.
According to recent research, more than half of successful marketers today engage in multivariate testing. However, there is still some confusion over what multivariate testing is, what it involves, and how it differs from more commonly-used A/B testing.
Put simply, multivariate testing involves testing multiple combinations of elements on a website or in a digital campaign simultaneously. For example, creating and testing multiple variations of a live webpage, each with subtle changes to the headline, CTAs, navigation, and images.
By comparison, A/B testing, also known as split-testing, only tests two options. For example, you might test two emails with different subject lines or test two website landing pages, each with a radically different approach in both design and content.
In A/B testing, 50 percent of your audience gets one version, while 50 percent get the other to see which performs better. In multivariate testing, multiple versions are split multiple ways to see how individual elements perform together to figure out which are the most engaging.
The Pros and Cons of Multivariate Testing
One of the big advantages of multivariate testing is that it allows you to alter and analyze many different elements at once, boosting efficiency and accelerating the optimization process. You can also be less cautious when it comes to new ideas as there are no limits on how many elements can be modified.
Multivariate testing also goes beyond just telling you that one version is better than another. Instead, it identifies which specific elements are working, as well as how elements work together and why, making it more informative and insightful.
You can also apply the data and insights from each test to future campaigns. For example, if you identify that red CTA buttons with a line of copy above increase engagement, you can use this in your next design.
Multivariate testing does, however, come with some drawbacks. One of the biggest is that it requires significant traffic or conversions to complete a test. This is because you need sufficient customers in each of the test groups to generate statistically significant results.
Multivariate tests can be complex. Plus, you need to keep the variation numbers in a reasonable amount. Otherwise, you could end up testing thousands of variations against the original, increasing the time to run the test and get results.
If you are only running a single multivariate test, it can also be hard to figure out the exact reason a design performs in a certain way. Therefore, the more tests you do, the greater insights you can glean.
When to Apply Multivariate Testing
While A/B testing is many marketers’ default method, there are many situations where multivariate testing can add real value.
Multivariate testing can be extremely effective for website optimization, where you have pages with multiple elements. It is a great way to gather data and to gain detailed insights into complex customer behavior. It can also work for digital ads, for example, where you want to test multiple variations of headlines, ad copies, images, and CTAs.
If you have less than 100,000 monthly site visitors, multivariate testing may not be ideal. The only exception to this would be if you have very high conversions. It might also be too early for multivariate testing if you are a startup engaged in customer development.
It often makes sense to start with A/B testing, which looks at broader changes first. You can then use multivariate testing to dig deeper and optimize further.
Conducting a Multivariate Test
Multivariate testing doesn’t have to be a complicated process. Thanks to today’s automated software, these types of tests are now easier than ever to run and analyze.
Here are the key steps:
Step 1: Review your site or campaign
Think about what elements of your website or ad campaign stand out or need work. Elements can include call to action (CTA) buttons, headlines, images, colors, navigation, or content tone.
Step 2: Formulate a hypothesis
Come up with a hypothesis as to why a specific element may not be working. For example, perhaps your CTA button is too small or in the wrong place, your headline is the wrong message or color, or your navigation is too complex.
Step 3: Create different variations
Create variations of all the different elements you are testing. Automated multivariate software can do this for you to generate unique versions of the page or campaign being tested.
Step 4: Determine your audience sample
For multivariate testing, your audience sample needs to be representative of the whole. You also need to make sure it is a large enough sample to be accurate. Again, automated tools can help you split traffic effectively among those variations.
Step 5: Run your multivariate test
Once your audience and sample size are defined, you can begin the MVT process. As MVT requires larger audience samples, it can take a bit of time to produce results.
To maximize the webpage or campaign performance and ensure the effectiveness of the test, you can leverage artificial intelligence (AI) to automatically adjust traffic distribution across the different variant groups. For example, AI can distribute users to the best performing combinations while keeping minimum users in the remaining ones to ensure statistical effectiveness.
Step 6: Analyze your results
Once you have enough traffic or conversions and your results are organized, you can then look at metrics such as your engagement, time per session, bounce, or conversion rates to identify increases or decreases to prove or disprove your hypothesis.
Multivariate testing can enhance your optimization efforts, where there are multiple elements at play. By using it alongside A/B testing and doing it periodically over time, you can keep your insights up-to-date, as customer behavior and preferences will change over time.
WE ARE HERE TO HELP
YOU MIGHT ALSO LIKE
Marketers have more data than ever at their fingertips, but how to make sure you are using the data to its full potential? Big data analytics can unlock the power of data, and offer insights on customer habits, interests and trends, helping you predict their future actions and plan your marketing campaigns accordingly. What Is Big Data Analytics? Big data refers to not only the huge amounts of data, but also the varied and numerous different types of data sets. This data includes customer behaviors like spending habits and interests, consumer trends, as well as hidden patterns and correlations that can help illuminate current market fluctuations. Marketers can make sense of this data through the methodology of big data analytics, which is a form of advanced analytics, requiring high-performance analysis systems. For many companies, the investment can really pay off. What Are the Benefits of Big Data Analytics? Knowledge is power, as the old adage goes. Big data analytics can unlock an enormous amount of knowledge about what your customers want, do, fear and dislike on your channels, but also on external websites. It can also provide unique insights on seemingly unrelated aspects of your customers’ lives –
When a shopper lands on your e-commerce site, she will either browse around or search for specific product info. What if you have a virtual assistant who can chat with her and guide her to exactly what she is looking for, just like in a physical store? This seamless, personalized customer experience is made possible through natural language processing (NLP). NLP is a field of artificial intelligence (AI) that trains machines to understand human language, interpret and converse with it. It takes into account the form of input (speech or voice). By leveraging deep learning algorithms, today’s NLP models are focused on “next sentence prediction”, which is a set of candidate sentences being ranked given an unfinished conversation. Initially, this was done through simple models based on statistical information. Given an input, they would generate the same result. However, using deep learning helps today’s models produce results with considerable accuracy as they are more complicated to extract information from the data. This has opened doors to a whole new host of applications. Robots in customer service, equipped with NLP technology, are able to understand conversations in limited domains and direct customers to relevant answers. Sentiment analysis also helps them analyze
In today’s technology-driven financial marketplace, digital-first consumers can apply for loans on a smartphone and invest in emerging markets with a simple swipe. For financial businesses in Asia Pacific, keeping pace with these fast-growing consumer expectations can be a challenge. In this blog, we will look at the real-world implications of three key consumer trends – and how financial institutions can leverage the power of artificial intelligence (AI) to exceed expectations, drive loyalty, and boost conversions. 1. Demanding for Real-Time Engagement Consumers in Asia Pacific live in a fast-paced digital world, and they have an insatiable need for speedy services and real-time engagement. More than 80 percent of consumers expect brands to respond to their queries within 24 hours, while 56 percent expect that within three hours. Keeping up can be a tall order, and call centers often struggle with the volume of incoming messages. Furthermore, lack of contextual information often prevents businesses delivering the personalized advice that today’s tech-savvy consumers expect. To meet the needs of always-connected customers, marketers are turning to AI tools, which leverage real-time data and predictive analytics to target customers with proactive recommendations and personalized services at every stage of their consumer journey. In