Advertising Impressions & How to Track Them: A Guide
Ad impressions can be one of the most confusing digital marketing metrics to understand and track. Here's a guide to help you track impressions.
In the age of big data and sophisticated analytics, marketers have the opportunity to test various designs, content, call to actions (CTAs), and messaging options to determine what works best for their audience. But, which testing method you use depends on several factors. We’ve outlined the differences between multivariate and A/B testing below, along with some of their common uses, advantages, and limitations, so you can decide which approach works best for your brand and target consumer.
A/B testing, also referred to as split testing, enables companies to better understand their audience and how to optimize their experience. Marketers use this method to compare two different design directions against each other, like two versions of your home page, email, advertisement, or another type of marketing collateral. You can also conduct A/B/C or A/B/C/D tests that run three or four different variations if desired.
For example, most people are familiar with A/B testing for email campaigns, which comes standard with many email marketing tools. Most marketers have used this to compare two different subject lines and determine which one leads to higher open rates. You create the two versions, and then traffic or views are divided evenly between the options to offer a direct side by side comparison. Companies often use this method to test small changes on landing pages, like the color of a CTA, or which mascot design should point to a sign-up button.
Multivariate testing uses the same core mechanisms as A/B testing, but it compares more variables and also reveals how they interact with one another. Its design is also more subtle. Rather than comparing radically different webpage designs, for example, multivariate testing focuses on comparing different elements within a single page design.
Just like the A/B option, this approach splits views or interactions between multiple versions of the design. But, instead of having two options to test, the multivariate approach analyzes every possible combination of the variables. The results are then compared to determine which combinations of variables proved to be most successful at accomplishing the goal (e.g., getting the most conversions).
For example, with multivariate testing, marketers can test two different lengths of signup forms, three headlines, two footers, and several graphics variations at once. The visitors or viewers would be split between all possible combinations of these elements until a decent sample size is achieved, and reliable conclusions can be drawn.
Now, you have the information necessary to make the right decision for your company and audience. Regardless of which one you choose, the value of this data is immeasurable. For a while, marketers have had to make their best guess at what works for their audience. Even when ads performed well, they weren’t sure exactly what aspect led to that result. In this modern era of data, we can test our hypotheses against actual data from our exact audience, and that’s powerful.
For tips on how to craft a compelling brand story, as well as the full A to Z on how to identify, grow, and maintain your audience through content and marketing strategies, check out AdRoll’s Ultimate Guide to Growth.
Last updated on August 16th, 2022.