5 Steps to Build a Successful Customer Behavior Analytics Model

There isn’t a marketer on earth who would turn down the ability to look into the future. What brand manager wouldn’t want to know exactly what and how much of a new technology or a hot new gadget customer will buy in droves next holiday season? Predictive analytics is a relatively new tactic and has been met with mixed reviews and success. Executing it properly requires a hefty amount of dedicated resources and a fair amount of experimentation. Going through these exercises, though, can certainly give you some interesting insights into how well you can leverage customer behavior analytics to facilitate business growth.

What is Predictive Analytics?

Predictive analytics has been around for a long time in several industries, including insurance. In that model, actuaries use historical data to predict certain outcomes and apply a cost to risk assessment. So, for automotive insurance, a driver’s history factors into the likely cost of potential accidents, driving individual policy premiums up. A policyholder with safe driving record typically receives an incentive (or discount) as a reward for good behavior.

Using customer behavior analytics to predict buying patterns, prevent fraud and make other business decisions is a relatively new development. With advancements in machine learning and more insightful data, companies are starting to develop methodologies to make educated, scientific guesses as to what their customers are likely to do in the next buying season. Developing your own analytical framework is resource-intensive and will garner mixed results, but the process, if deployed correctly, is informative and educational.

1. Use Regression Models

You can’t guess what someone might do if you don’t study what they’ve already done. There is a lot of experimentation that goes into content marketing. Start with buying history. Look at responses from your previous campaigns and organize them. Some of the items you should study include:

  • Number of items purchased
  • Total purchase amounts
  • Discount codes
  • Purchase dates

If you’re looking for more information about the general use of regression analysis, which is used across the spectrum from science to education, check out this piece from the Harvard Business Journal.

2. Segment Customers into Groups

Now that you’re on your way to building up your historical data, it’s time to merge that with some customer segmentation. The more refined you can make these groups, the more you can stop guessing what customers want and simply observe what they do. Customer observation is at the heart of all good customer experience marketing. If you have a strong statistics team, use them to integrate one of the many accepted statistical models for segmentation. If not, building those groups manually may take time, but it could also still be worthwhile especially if you segment by:

  • Demographics (age, region, income)
  • Buying history (x purchases within the last x months)
  • Responsive behavior (referrals from social media, newsletters, email campaigns, etc.)

Amazon Prime Customer Segmentation

Amazon Prime Customer Segmentation

Before you begin, understand that statistical modeling is complex and requires active maintenance. However, the better you and your team get at it, the more sophisticated your marketing becomes down the line.

3. Deploy a Smart Suggestion Engine

Amazon mastered the suggestion engine early on and owes a huge amount of its success to this mastery. If you don’t have the resources in-house to build your own, there are plenty of enterprise solutions that use actual machine learning to make smarter suggestions to your buyers using customer behavior analytics and buying history. The advantage of a third party engine is that you don’t have to maintain or update it. The disadvantage is that you don’t own it outright; you may stumble into some interesting and useful IP if you DIY an in-house engine. In any case, if you’re selling goods online, you need a recommendation driver. So do your homework and figure out which solution works best for you and your audience.

4. Build Slowly

The good news about customer behavior analysis is that the industry is getting better and better at data collection. The downside: there is an almost unlimited amount of data to sort through. So before you throw everything into one pot and overwhelm your marketing team, try adding one to two factors at a time, experiment, and then add another factor. Predictive analytics for marketing purposes is still a developing field. You’ll need plenty of flexibility to integrate real-time data as you go. Doing so will make the process more exciting and engaging for your team.

5. Stop Making Assumptions

Let this process educate you. If (or more likely, when) you learn the data you used to collect isn’t proving to be as useful as you assumed it was, stop collecting it. For example, data researchers used to bank all of their messaging predictions on bounce rates. Today, most marketers tend to ignore that statistic. The Predictive analysis should stop you from guessing, not encourage you to guess more. As soon as you start to see new patterns emerge, respect the data and let it push you into areas where you never thought you’d go.

The most important takeaway in all of marketing is this: data never lies. Data and customer behavior analytics paradigms should become the drivers of all of your product development and marketing tactics. It’s never a perfect science, but it is a science. How well you use it can determine the difference between repeated successes or disappointment.

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