AI Integration – The Next Evolution in Multivariate Testing

‘Multivariate testing’ doesn’t exactly roll off the tongue. But it’s still the best way to optimize your marketing campaigns and on-page features for maximum conversions. 

It’s no coincidence that only 22% of marketers report being satisfied with their current conversion rates. That’s because when everyone is hounding users for their attention, they don’t have more than a few seconds to spare. So how do you stand out from the crowd?

Simple. Just use the ads and on-page features that test well with your target audience. Instead of deciding for yourself what consumers would like, let click through, and conversion rates determine for you. 

Alright, it may not be that simple after all, but that’s where multivariate testing comes into play. This type of testing allows marketers to test different ‘versions’ of live ads and on-page features. 

They can play around with different elements and create versions to see which one gets the most attention. Then, they can use the final version on their website sales funnel or Google Ads. 

This is usually the point when people ask, “but how do they execute the testing?”.

So here’s how it takes place.

Demystifying Multivariate Testing

Essentially, multivariate testing optimizes marketing campaigns and webpage elements by trying out multiple versions of a set of features. The reason it’s called ‘Multivariate’ is that it tests numerous variables at the same time.

For example, imagine you post an ad and test variations in three variables.

  • Variable 1: Headline
  • Variable 2: Body text
  • Variable 3: Background Image

Now you want to see whether a particular combination of these three variables is more effective than another version. So you change variable one and variable two, but keep variable three the same. This version is a new one of the original ad, let’s call it, version A.

Now you change variable one and variable three, but keep variable two the same as the original version. This version of the ad is version B. As you go on changing each element and trying out different combinations of changes, more versions get created. 

As such, multivariate testing can optimize the design for websites and ad campaigns. It’s most useful when you have multiple ideas you want to test out. By tracking user metrics such as bounce rate and time per session, you can test which combination is worth keeping. 

But keep in mind, multivariate testing only helps when you want to test out at least two or more elements. That way, versions A/B/C/D can be created and compared, each with distinct combinations of features you want to test. If there’s only one element up for debate, you better stick to A/B testing.

AI in multivariate testing

So What’s A/B Testing?

Now that everyone (finally) has a firm grasp of multivariate testing let’s look at A/B testing. Some may say A/B testing (or split testing) is the simpler version of multivariate testing. While that may be true to some extent, it’s not the whole story.

The main difference between A/B testing and MVT testing is the number of variables involved, and versions created. As mentioned before, MVT testing allows you to create multiple versions by changing several features or elements within the webpage or ad. A/B testing simplifies that diversity by allowing variation in only one aspect of a website or ad.

Let’s Look At an Example

For instance, if we want to test the engagement of a new headline on Lucrative.ai (and no other feature), split testing is the way to go.

Consider our original headline “Your Autonomous Marketing Ally with 99% Accurate Attribution”. Suppose we want to see the shift in user traffic and engagement by changing this headline.

So, we make another version of the webpage with a different headline, “The Next Stage of Marketing Automation with AI Capability.”

Half of the traffic diverts to the first version, or Version A. The other half of the traffic shifts to the second version, or version B. After that, we can analyze how much engagement each headline generates through website analytics. The results dictate which headline has better user engagement, and that version makes it into the next phase of testing.

From this example, it’s clear that split testing is simpler than multivariate testing. There’s only one element up for debate, and there’s only a couple of versions to try out. But this doesn’t mean A/B testing is somehow inferior to MVT testing. 

The type of testing that you use depends on your objectives, traffic, and resources. If you need to test multiple features at once, use MVT testing. Otherwise, A/B testing should suffice for testing two versions of a single element. 

There are many other factors you need to consider when choosing between A/B and Multivariate testing. But we’ll cover these in detail later on. If you want to read up some more on A/B testing, check this out

For now, let’s see how brands make the most of MVT testing.

Making the Most of MVT Testing

The main reason brands use MVT testing is to accelerate the flow of users through the sales funnel. Brands want users to click on their ads, go through their services, and generally spend time on their website. For that, they need attractive ad campaigns and compelling page features.

That’s why MVT testing is used widely by modern brands to keep users clicking through the ads and website. The main benefit of MVT testing is that it doesn’t need significant investment in design and development. Instead, business owners can optimize the features themselves.

It’s Not All Sunshine & Roses

But it’s not all sunshine and rainbows when it comes to multivariate testing. That’s because not every business has the time (or traffic) to utilize it. Remember that when you’re implementing MVT testing, you’re making quite a lot of testable versions. That means you need to redirect a significant amount of traffic through each version for meaningful results. 

But as many startup owners will tell you, that’s not always possible. Especially not at the beginning of the business when there is only low traffic on the website. That’s why some companies are still reluctant to opt for MVT testing, despite its many benefits.

Not to mention, multivariate testing isn’t exactly the fastest process. If you need quick, meaningful insights about which design features to use, MVT testing won’t help you that much. It’s an experiment, and all experiments need time to yield reliable results. So you’ll end up wasting time and traffic to test whether every combination is worth it or not.

MVT testing also forces you to try out nonsense combinations. Each element combines with every other component for a whole combination, so you end up with some bonkers combos. 

Surely, there must be a better way to benefit from MVT testing! Right? Well, there is.

The Advent of AI Integration

The beauty of artificial intelligence is that it makes our lives easier. Whether it’s through automated marketing tools or continuous multivariate analysis, the benefits of AI are innumerable. That’s why AI integration is the next evolution in MVT testing.

With data-driven marketing on the rise, the integration of AI to accelerate processes is becoming common. This acceleration can be found in multivariate testing too. The main drawback of MVT testing is that it needs a lot of traffic and time to get right. But not with AI.

AI systems can analyze the traffic heading into each version of the ad or the webpage. It can then present metrics on which version is better, based on your objectives. Since it’s machine learning algorithms doing all the work, it will take less time to complete it.

Besides, AI systems are capable of running continuous multivariate analysis on the traffic. So you can direct your time and efforts to other (more pressing) matters while AI takes over the domain of MVT testing.

You’ll get better results and faster, too, along with recommendations of what design changes are likely to work based on present data. So AI essentially does all the MVT testing for you, more efficiently in half the time. Indeed, the all-in-one solution for all multivariate testing pains out there.

AI in multivariate testing

Here’s Your Checklist for Effective Multivariate Testing

Finally, let’s answer the questions “When do you use MVT testing?” and “How do you make it effective?”. There’s a small checklist that comes in handy when answering these questions. Let’s take a look at what it is.

  1. The very first requirement is the clarity of objectives. Ask yourself, “What are my goals?” “What do I want to accomplish with MVT testing?”. Try to be as specific in answering these questions as possible.
  2. The next step is to ask yourself if you are using the right testing metrics? Be sure to match your metrics with the objectives you laid out at the beginning of MVT testing.
  3. Make sure you have adequate amounts of traffic. There’s no set rule for how much traffic is ‘enough,’ but some thousand visitors per month should do the trick.
  4. Do you need to implement MVT testing? Usually, if you think about this one, you’ll realize the answer is ‘no.’ That’s because there aren’t enough combinations to try out to warrant multivariate testing.
  5. What’s your time frame? How long can you afford to run MVT testing? As mentioned before, this type of testing takes time to yield valuable results. So make sure you have the time to implement thorough MVT testing without rushing it.

Once you have the answers to all these questions and are clear about what you want to achieve, you’ll get more out of multivariate testing than ever before. Coupled with the power of AI, there’s a good chance you will find some valuable insights about the design features that get users clicking.

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