What Is Multivariate Testing and Why Is It Important?

Multivariate testing or multi-variable testing is a broad term used to describe a type of testing that measures the effectiveness of a specific marketing campaign or piece of collateral by analyzing multiple metrics and/or performing multiple analyses. It’s often used to gauge the success of a multi-channel marketing plan such as a PPC (pay per click) campaign or a website redesign, for example. As a marketing tool, multivariate testing can be quite powerful and, depending on your marketing objectives, it may be the best choice for you.

Key Differences Between Multivariate And Univariate Testing

The main difference between multivariate and univariate testing is the focus on analyzing multiple metrics rather than just one. In univariate testing, usually just one metric is analyzed at a time – such as the number of leads that converted to customers on a website redesign project. With multivariate testing, however, multiple metrics are generally used at once to get a more complete and accurate picture of the testing result.

For example, if you’re testing the effectiveness of a PPC (pay per click) campaign on a single website, you might look at the number of clicks, the amount spent, and the conversion rate to determine how successful the campaign is. But if you’re also interested in measuring the impact of the PPC campaign on brand awareness and the likelihood of a conversion, you might want to analyze the number of impressions, the click-through rate, and the conversion rate on all three platforms (mobile, tablet, and desktop) to get an accurate view of how the campaign is performing overall.

The goal of multivariate testing is to gain a fuller understanding of the performance of a campaign or program by comparing several metrics – such as the number of sales, the conversion rate, and the average order value – over a defined time period. This allows you to pinpoint weaknesses in your approach and take corrective action before the performance of your campaign slips too far below what you’re looking for.

Why Should You Care About Multivariate Testing?

Let’s say you’ve launched a PPC (pay per click) campaign for your e-commerce store and you’re looking to see how effective it has been. You can select Google Analytics to track the number of clicks, the conversion rate (number of sales divided by the number of clicks), and the average order value for your campaign. These are all very useful metrics to track. You might even choose to look at the performance of different keywords and adjust your campaign budget based on which keywords are delivering the most valuable traffic and conversions to your site.

Now let’s say you’re also interested in seeing how much brand awareness your PPC campaign has generated. To measure this, you could look at the number of times your brand name or logo has been seen (impressions) along with other keywords and phrases relating to your brand (clicks), and compare the two to see how much brand awareness has been generated. If you run a pay-per-click campaign for your brand, you can use this tool to get a sense of how effectively your PPC campaign has been running, and whether or not it’s bringing in the right kind of traffic.

The Importance Of Being Accurate In Your Testing

Accuracy is extremely important in multivariate testing, especially with large volumes of data. If you aren’t measuring the exact same thing each time you perform a test, you risk creating inaccurate results that don’t give you the full picture. To ensure you’re not missing any important details, it’s important to use the same measure (metric, analysis, etc.) across all tests. This prevents inconsistencies that can throw off your results. If you aren’t consistent, you’re not providing stable data for your campaign managers or executives to analyze. This is why it’s important to write down the specific details of each step in the process of performing multivariate tests so that each one can be repeated exactly.

When Should You Run Multi-Variate Testing?

There are times and situations when you should run multi-variate testing and others when you shouldn’t. You need to use your best judgment to determine when this tool can add the most value to your marketing program. The most basic testing scenario is when you have a specific performance metric in mind for your marketing campaign – such as increasing the number of leads, boosting the conversion rate, or generating buzz around your product or service. This is called a uni-variate scenario because you’re looking to test just one variable (in this case, lead generation) at a time. When used in this way, multivariate testing can be quite a valuable tool because it provides a level of detail that uni-variable testing can’t.

You should also consider running multivariate tests when you’re working with a small data set – such as when you’re testing the effectiveness of a couple of ads on a single web property. In this case, it’s appropriate to test the performance of the ads individually rather than as part of a group. As data sets grow, it becomes more difficult to control for all the variables that can influence the results of a test. Running tests individually also prevents any inconsistencies that can arise from using aggregated data.

Though there are times when it’s appropriate to use multi-variable testing, it’s usually better to use uni-variable testing when you have an interest in just one variable – such as increases in leads, conversion rates, or average order values. Doing so provides you with a clear picture of how changes in that particular variable affect the rest of your program. In the event you do end up with inconsistent data, using that data in your individual uni-variable tests can shed some light on which changes caused what effect – rather than just using the results of the entire multivariate test.

How Do You Get The Data You Needs For Multivariate Testing?

Depending on your marketing objectives and the results of your test, you may need several different metrics for data. In the case of lead generation via PPC (pay per click), you may want to compare the number of leads generated by your campaign to those generated by your competitors’ campaigns. This is where a Google Search can help. For a given search term (or group of terms), you can use Google Adwords to compare the performance of your campaign with that of your competitors.

If you want to compare the performance of two different e-commerce stores, you can use online merchants like Shopify to create dashboards that show you the vital statistics for each business. This way you can spot trends and make informed decisions about your future marketing programs.

Though you may not always need to perform multivariate tests to analyze marketing results, it’s important to consider this testing option because it can provide a valuable overview of the performance of your entire program. If you’re looking to optimize the performance of your marketing efforts, running a multivariate test can help you pinpoint areas where you can make the most improvement – and just how much more effective your campaign can be if you do.

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