Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

How to Achieve Precision in Social Ad Targeting

Audience segmentation is a science that has previously relied on data and analysis of past behavior. Here’s how marketers can use artificial intelligence (AI) predictively to segment their Facebook audience more effectively at a lower cost per action.

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.


Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.


Fighting Fire With Fire: Why AI Is the Best Security Defense for Your AI System

Author | Min Sun, Chief AI Scientist, Appier Breakthroughs such as deep learning for visual recognition and natural language processing underpin much of the excitement in artificial intelligence (AI) today. However, like all new innovative technologies, AI comes with its share of security concerns. It is always the way: While breakthrough technologies can revolutionize business and the way we work, they have to be handled carefully to avoid errors, misuse or worse. Thankfully, that very same technology could hold the key to making AI more robust.    A Double-Edge Sword: Why AI’s Biggest Strength Is Also Its Biggest Risk Remember that any kind of software system has its security concerns – it is not just AI. However, AI has two unique properties that make security more pressing. The first is its power. AI systems are typically built to increase human productivity – they are much more efficient than humans, especially at performing repetitive tasks. So, if malicious actors were to take control of such a system, their productivity would also greatly increase. This is a double-edged sword – AI’s immense power is its biggest strength, but this also makes it more dangerous if it falls into the wrong hands.  This

Pinpoint Your Ideal Audience With Machine Learning

Consumer behavior is increasingly fragmented, and that has resulted in the tsunami of data, which can be overwhelming for marketers. Thanks to the behavior-based model of today’s artificial intelligence (AI) tools, marketers can now leverage these tools to segment the audience beyond the traditional parameters to build a more accurate portrait of them as individuals. By employing AI, you will be able to segment your audience into more granular tiers, and to see which are more valuable to your end goals. Considering that more than half of customers say they will walk away from brands that send messages they find irrelevant, it’s vital that you understand your customers’ current and future intentions as well as possible.   Individuals vs Categories: Looking Beyond the ‘Types’ Artificial intelligence can find hidden user patterns that have a positive or negative impact towards achieving the goal that marketers want to achieve. Hence it can help marketers reach a predetermined end goal, be it finding the more-likely-to-purchase audience segment to drive sales volume, or giving an existing audience a personalized product or content catering to their interest. This is very useful for marketers. If a website has an audience of one million people, a marketer

4 Technologies That Are Transforming Customer Engagement Online

Consumers engaging with brands, seeking customer services and making purchases through social media is fast becoming the norm. Businesses need to constantly adjust their approach to reach out and respond to consumers with relevant and personalized messages. Take advantage of these four technologies that will help your online engagement with your customers reach a whole new level.   Chatbots: Manage the Volume of Online Messages, and Speed up Customer Service Chatbots are seeing a surge in uptake and are helping marketers connect with their audience in new ways. According to Gartner, by 2020, 25 percent of customer service operations will use chatbots across engagement channels, and Facebook reports that its Messenger has more than 300,000 active bots. Brands are increasingly using chatbots to manage the large volume of customer messages generated across social media, and respond to them in a human-like fashion. Powered by artificial intelligence (AI), bots become smarter over multiple interactions, and can be used to personalize marketers’ messages over time. They also help streamline and speed up customer service, allowing businesses to focus on bigger issues likes complaint resolution by taking care of routine customer queries.   AI-powered Prediction Engines: Access Granular Insights to Better Target Consumers