Breaking The Rules In Behavioral Targeting

In the constantly changing world of digital marketing, segmenting your audience using rules based on broad assumptions or unsubstantiated insights can work against your success. Unfortunately, many marketers rely only on limited rules-based targeting systems, without realizing that better options exist.

The reality is that given today’s complex relationships between businesses and their customers, traditional rules-based targeting systems are often not powerful enough on their own to give customers richer, more relevant experiences when moving through a digital universe.

We all know that even customers who might appear similar on the surface can ultimately have wildly different interests when it comes to what they buy and what prompts them to make a purchase. Instead of relying only on rules-based systems, marketers also need to tap into more automated, behavior-based targeting solutions built on powerful algorithms.

So what’s the difference? And why is at least knowing a little something about the underlying algorithms so important?

A lot of digital marketing technology vendors offer rules-based targeting systems that display certain content to visitors based on assumptions. For instance, a website visitor from California might be greeted with an ad for sunscreen or shorts, while someone from a colder climate might only see jackets and other gear to keep them warm. Unfortunately, this approach treats large customer segments the same, shutting out potential opportunities. There’s no real intelligence behind the automation, even if vendors claim otherwise. To make rules-based targeting work better, marketers have to spend a lot of time defining rules and coming up with hypotheses—which is impossible when dealing with a virtually limitless number of customer backgrounds, interests, etc.

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It’s important to note that rules-based targeting isn’t a bad thing, but digital marketers should know it’s only one of the things they should be doing as part of their site optimization program. They need more automated modeling capabilities as well.

With automated targeting built on machine learning, marketers don’t need to manually manage and build models, and instead can spend their time on more valuable activities. Even the staunchest proponent of manual intervention and control would find it hard to argue the fact that for many marketing activities, machines are much better equipped to quickly move through thousands of variables and make correlations between things that—at least to us—could seem completely unrelated. Machine learning also has the advantage of getting better at learning over time; targeting accuracy improves as data is collected and analyzed.

We know empowering customers to make smart decisions relies on giving them smart tools built on powerful algorithms.  As marketers, we all want to do right by our customers. That is why it is important to understand what and when rules-based versus more machine-based targeting is best.

Discuss This Article

Comments: 3

  • Catalina says:

    Any suggestions of algorithm based technologies? Thanks!

  • Drew says:

    Hi Catalina,

    Thank you for reaching out! The Target solution in the Adobe Marketing Cloud combines testing with both rules-based and automated targeting approaches, providing a lot of flexibility and customization for marketers to use the most effective approach at different cross-channel locations based upon specific or all available data on visitors and customers.

  • Also check out DM News and Causata’s recent article on Rules-based vs. Model-based vs. Machine Learning algorithms for customer personalization and targeting at Causata’s CXM solution is used by several of the U.S.’ largest brands for customer personalization and real-time decisioning.

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