How to Achieve Precision in Social Ad Targeting

Social media platforms are a dream for marketers who are interested in the granular aspects of consumer traits. While audience segmentation is not a new practice, it’s becoming increasingly sophisticated in an age where you have reams of data about consumer behavior at your fingertips. How you use that data effectively is another story.

At the moment, when marketers practise audience segmentation, what you are trying to do is analyze the data and identify traits that will indicate whether a customer is likely to take a desired action. You then use this to build a persona you use as a target for your ads.

It works, to an extent. However, as social media marketing become more expensive – Facebook’s cost per click increased 92 percent in Q1 2018 compared to the same period last year, while click-through rates remain flat – you should be asking yourself how to optimize audience segmentation to get the best return on investment.

First, Find Inefficiencies

There are two main areas of weakness for the current method of audience segmentation. The first is that segmenting by behavior relies on identifying the consumers by their past behavior, which doesn’t always correspond to future behavior. What you need is a dataset that gives you the leading indicators for future behavior.

Secondly, if you have any kind of audience overlap in terms of demographics or past behavior, it means that on a platform like Facebook you could be bidding against yourself for those traits, leading to spending inefficiencies.

That’s not to mention the limits of the Facebook overlap tool itself – due to privacy reasons, it won’t allow you to analyze an audience of fewer than 1000 people. If you could find a way to reduce the overlap but still aim your ads at the audience most likely to take the desired action, then you’ll become more efficient at using your budget.

How AI Can Help

Data doesn’t lie, but plenty of marketers misinterpret it. AI can remove the guesswork from remarketing by combing through the data for you to find patterns based on mass behaviors. What if you could identify the most valuable segments to bid on, while reducing the chances of overlap?

A tool such as Appier’s AI-powered ad solution is predictive. It identifies the people most valuable to you by behaviors – from frequency of visits and time of visit to viewing or buying a product – that are leading indicators for future actions. You can then remarket to them using the most effective ad channel and creative format.

For example, AI might notice three different behaviors, such as ‘visits the site twice in five days’, ‘adds to chart twice in 14 days’, and ‘purchases once in 10 days’. It then defines specific segments based on these behaviors. But how do you know which ones have the highest potential to convert after seeing your ad on Facebook? AI prediction combines the best segments based on massive rules, and then ranks the segment combinations based on their value. Such insight will tell you that the segment visiting the site twice in five days is more likely to take action, and therefore you might choose to increase the exposure of your ad to this segment.

Optimal Data Combinations

AI can also offer combinations of the best segments based on massive rules and then rank them according to how valuable they are for your brand. A visitor who has landed on your website twice in five days could be more valuable, for example, than a shopper who has added an item to their cart in the last 14 days. Having this level of specificity means you can see and target high-value audiences to make your marketing more effective.

The other area where AI provides insight is in finding discrete segments. Humans can identify valuable segments, but are less equipped to see the overlaps; AI can help marketers choose segments that overlap as little as possible, and so that you can maximize your spend.

When Appier pitched a human segmentation against an AI segmentation, AI scored 135.7 percent more actions, but cost 27 percent less, proving a much better return on investment.

AI tools are maturing fast and with so much data to sift through thanks to social media analytics, it makes sense for marketers to use smarter segmentation to optimize their cost per action and cost per lead. With more reach and less overlap, AI-led social media audience segmentation represents solid cost savings and a better return on investment.