Customer Marketing Technologies

Customer Marketing Technologies Should Be Agile and Dynamic, Just Like the Customers Themselves

Despite their mathematical complexity, most customer models are static and rule-based. People, however, are dynamic and ever-changing. Advanced models use machine learning and predictive analytics to bridge the gap

The holy grail of customer marketing is serving each customer the best possible offer in the optimal time, channel and place. This goal takes into account the vast differences between individuals. Each person has unique wants, needs and preferences, and the ideal marketer should be able to cater to each one on his or her own terms. This, of course, is highly impractical, and this is where Customer Modeling, also known as Customer Behavior Modeling, comes into the picture.

Customer Modeling is the creation of a mathematical construct to represent the common behaviors observed among particular groups of customers in order to predict how similar customers will behave under similar circumstances. It relies on the premise that although it isn’t feasible to treat every customer individually, customers can be grouped into clusters based on similar tendencies.

Think about your own purchasing habits. A few brands can rely on your RFM attributes to serve you optimally. Those are the ones you frequent habitually – your local grocery store perhaps, your dry cleaner or hair stylist. Your relationship with other brands, however, is a lot less predictable. Sometimes you shop for clothes on a whim. Sometimes your shopping is triggered by a special offer. Perhaps you are very predictable when you buy clothes for your children, but are all over the place when you’re shopping for yourself. Brands who will rely only on your past RFM data to serve you will have a hard time of getting it right, and their communications with you are prone to be so off the chart that you will cut your relationship with them completely.

To offset this negative effect of generic communications and maximize the potential of truly personalized customer interactions, customer behavior modeling needed to find more dynamic and precise tools to model behavior. In the past couple of years, the rise of machine learning and AI has enabled modeling methods which are far more advanced and effective than the conventional rule-based methods. By combining a number of technologies into an integrated, closed-loop system, today marketers can enjoy highly accurate customer behavior analysis in an easy-to-use application.

To achieve market-leading predictive customer behavior modeling, your technology should feature the following capabilities:

  1. Segmenting customers into small groups and addressing individual customers based on actual behaviors – instead of hard-coding any pre-conceived notions or assumptions of what makes customers similar to one another, and instead of only looking at aggregated/averaged data which hides important facts about individual customers
  2. Tracking customers and how they move among different segments over time (i.e., dynamic segmentation), including customer lifecycle context and cohort analysis – instead of just determining in what segments customers are now without regard for how they arrived there.

 

Customer Behavior Modeling

Despite their mathematical complexity, most customer models are actually relatively simple. Their biggest drawback is that they are static and rule-based. People, however, are dynamic and ever-changing. Because of this, most customer behavior models ignore many pertinent factors and the predictions they generate are generally not very reliable.

The RFM approach to customer behavior analysis is a good case in point. Many customer behavior models are based on an analysis of Recency, Frequency and Monetary Value (RFM). This means that customers who have spent money at a business recently are more likely than others to spend again, that customers who spend money more often at a business are more likely than others to spend again and that customers who have spent the most money at a business are more likely than others to spend again.

RFM is popular because it is easy to understand by marketers and business managers, it does not require specialized software and it holds true for customers in almost every business and industry.

Unfortunately, RFM alone does not deliver the level of accuracy that marketers require. Firstly, RFM models only describe what a customer has done in the past and cannot accurately predict future behaviors. Secondly, RFM models look at customers at a particular point in time and do not take into account how the customer has behaved in the past or in what lifecycle stage the customer is currently found. This second point is critical because accurate customer modeling is very weak unless the customer’s behavior is analyzed over time.

Customer behavior models are typically based on data mining of customer data, and each model is designed to answer one question at one point in time. For example, a customer model can be used to predict what a particular group of customers will do in response to a particular marketing action. If the model is sound and the marketer follows the recommendations it generated, then the marketer will observe that a majority of the customers in the group responded as predicted by the model.

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