{"id":1758,"date":"2019-03-13T03:37:09","date_gmt":"2019-03-13T07:37:09","guid":{"rendered":"https:\/\/www.yupingliu.com\/wordpress\/?p=1758"},"modified":"2019-03-13T03:37:27","modified_gmt":"2019-03-13T07:37:27","slug":"pointers-on-behavioral-segmentation","status":"publish","type":"post","link":"https:\/\/www.yupingliu.com\/wordpress\/2019\/03\/13\/pointers-on-behavioral-segmentation\/","title":{"rendered":"Pointers on Behavioral Segmentation"},"content":{"rendered":"\n

Behavioral segmentation is the practice of segmenting your customers based on what they do. I wrote previously about the basics of behavioral segmentation<\/a>. During the last year, I’ve been putting it into practice through a customer segmentation project with a CASC<\/a> business partner. Today I’d like to share with you some of the lessons I\u2019ve learned through that experience.<\/p>\n\n\n\n

NOT Just an Analytics Exercise<\/h3>\n\n\n\n

Behavioral segmentation has the advantage of revealing segments of similarly behaved consumers that you may not have thought of previously. It requires a lot of data crunching. There is no doubt about that. However, it is important to remember that behavioral segmentation is not entirely an exercise in numbers. If data crunching is all that you do, you risk creating segments that either are based on artificial or fake relationships or are not very actionable from a target marketing perspective.<\/p>\n\n\n\n

Successful behavioral segmentation should be a collaborative exercise between your analytics team and those who have a good grasp of your business and your customers. The latter are most likely found in your marketing or sales department. The process of behavioral segmentation should be developed as an iterative process that goes back and forth between analytics and marketing. The analytics team should start by understanding from the marketing team the purpose of the segmentation exercise, the observed behaviors at hand, and the capability of marketing to implement behavioral segmentation insights. Based on this initial information, the analytics team can produce an initial set of behavioral segments based on customer data.<\/p>\n\n\n\n

This initial segmentation scheme should be presented to the marketing team both to make sense of the results and to see if meaningful actions can be taken to target each segment. The input from the marketing team is then fed back to the next round of data crunching to adjust the segmentation focus and approach. This process is repeated until both sides are satisfied with a meaningful set of segments to be implemented in practice.<\/p>\n\n\n\n\n\n\n\n

Data Quality is Really Important<\/h3>\n\n\n\n

The former CEO of Reddit, Ellen Pao, caused a stir in January when he stated that web analytics is fake<\/a>. Although his statement may seem radical, the underlying message was actually quite reasonable. His point was that user metrics in web analytics are often inaccurate and therefore are of limited value. This trash-in trash-out mentality applies to all data analytic situations, including behavioral segmentation.<\/p>\n\n\n\n

Before you dive into behavioral segmentation, first verify the quality and accuracy of your customer data. Are numbers free of errors? Do you see duplicate records? Are there significant omissions in a systematic pattern? Simple summary statistics are often insufficient in spotting these issues. You won\u2019t see the problems until you really dig deep into the data. I have no doubt that there are a few businesses out there that may maintain squeaky clean data. But in my experience, data cleaning and quality verification are usually the most time-consuming part of an analytics project. So, although discovering new customer segments is exciting, curb your enthusiasm and check your data first.<\/p>\n\n\n\n

Determine What to Include<\/h3>\n\n\n\n

In most situations, there are many different ways of describing customers\u2019 behaviors. Describing customers in a retail setting may include, for example, purchase frequency, order size, basket composition, brand choices, coupon usage, time of purchase, to list just a few. Demographic and psychographic profiles may also be mixed in with pure behavioral data.\u00a0The results of a segmentation analysis can be vastly different depending on which of these inputs you provide to your analysis.<\/p>\n\n\n\n

Which ones should you include?\u00a0Some might answer that we should just include all available information. But this is neither wise nor feasible in some situations. Depending on the size of the data (both the number of variables and the number of observations), the computational cost may be prohibitively high to justify the inclusion of all possible information. Including unimportant information can also clutter up your analysis with garbage.<\/p>\n\n\n\n

Here are a few considerations I would like to offer:<\/p>\n\n\n\n