Maximize your customer loyalty using predictive behavior insights

As marketers, we often get so caught up in using processes and strategies that work, that we forget to examine if they’re optimized.

Sometimes it’s important to step back and ask ourselves, “Could we be doing this better?” The answer is often, yes, we can. When it comes to data, this is quite a prevalent issue.

Data flows on different computer monitors.

For example, it’s easy to continue on that path already started upon with demographic data modeling. Especially if we’ve had success with it, it’s tougher to reevaluate our options. However, it’s becoming impossible to ignore that the predictive behavior model is superior to demographics.

This article will explore this topic further and give you ways to implement and maximize this strategy.

What is predictive behavior modeling?

Predictive behavior modeling is the process of using data to better understand customers based on the way they behave in relation to a business. These types of analytics give marketers key insight into what to expect out of different user groups that otherwise would have been nebulous.

A graph about predictive power.

Let’s take a look at the ways an understanding of behavior leads to greater success.

Predictive behavior modeling benefits

There are a plethora of benefits that come with trusting behavioral data. The following are some of the most helpful, as well as a brief explanation as to why.

Removes speculation and guesswork from the equation

A woman holding a question mark sign.

Before we had a good grasp as to what behavioral data was, the way in which we predicted future customer involvement was by making educated (albeit incomplete) guesses based on the data we had.

Though this removed some of the speculative aspect of the process, it was still far from scientific. Behavioral data cuts through all that and gives marketers much-needed precision.

Finding unexpected or less visible customers

We often forget that marketing data extends beyond finding deeper understanding for current customers.

It often lands us new customers, and in the case of behavioral data, the predictions it leads to often find customers we didn’t believe were available, interested or part of the target market.

An invisible person.

And don’t believe for a second that these less visible groups are inherently less profitable to reach. The fact of the matter is, they’re often much more profitable than we realize.

Are we there yet? (the marketing version)

“When?” is often the key question marketers want to know when it comes to their customers, particularly in terms of when a purchase is likely to happen.

Predictive behavior data answers the ‘when’ question with a very accurate estimate as to when a purchase or upgrade will go through.

Question marks making up the word 'when'.

This comes with the territory of having data and better understanding things such as purchasing patterns, especially when these data are fed into software systems with artificial intelligence and machine learning.

Maximizing the predictive power of behavior

If you understand how helpful behavioral data can be for marketers, then you also know why it’s important to optimize the process. Here are some of the most crucial ways to boost success.

Cover all your behavioral bases

There are a wide variety of different behaviors your data can clue you in on. Make sure that you identify your marketing team’s biggest needs in order to ensure success.

Megaphones speaking at each other.

For example, let’s say that the biggest area of need when it comes to identifying behavior is communication. In that case, you’d want a predictive model capable of telling you if and when a customer will open an email, newsletter, respond to a call, etc.

This in turn helps build more effective and desired communication between a business and its customers.

Validate value

It may sound strange, but even the most basic of behavioral data analysis gives marketers deeper insight into the value and potential value of customers and potential customers. When optimized, this creates a near-precise record that lays out which types of customers will have more value over the long term compared to others.

A magnifying glass over stick figures.

The better the machine learning within your analytics, and the stronger you interpret the information, the more you’ll know about what types of value each customer can bring. This helps with building better relationships and loyalty.

Software to optimize your results

The final thing to consider when implementing predictive behavior as a top data factor within your marketing teams is what type of software would best boost this process. Luckily, there is a clear answer – customer data platform (CDP).

A screenshot of a CDP interface.

A CDP is a marketer’s best friend when it comes to tracking, using and understanding behavioral data. The right customer data platform system should provide some type of behavioral rankings or scoring, capable of sorting users according to specific behavior in the data. This of course allows for stronger predictions about these groups and their future choices.

Lastly, the right customer data platform will provide a comprehensive approach to predictive behavior, giving in-depth analysis on common indicators, such as the frequency of behavior or how often the user engages with a site. This type of information gives marketers a huge edge.

Final Thoughts

Marketers aim to make the most of their data so that every project and campaign exceeds expectations. In the past, it seemed sufficient to rely on the ideas they got from demographic information. However, the future is now, and that future is clear: predictive behavior data is much more helpful. Don’t get left behind.