Using Artificial Intelligence (AI) to improve Customer Behavior Modeling Technologies

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.

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.

Challenges of 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 money again. Also, 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 is critical because customer modeling can be very weak if the customer’s behavior is not analyzed over time.

Utilizing Customer Behavior Modeling with Artificial Intelligence (AI)

Think about your own purchasing habits. A few brands can rely on your RFM attributes to serve you optimally. Those are the ones that you visit frequently – your local grocery store perhaps, your dry cleaner, or your 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 you are all over the place when you’re shopping for yourself. Brands who rely only on your past RFM data will have a hard time getting your habits right, and their communication with you is likely to be so off the mark that you will cut off your relationship with them completely.

To offset the negative effects of generic communication and maximize the potential of personalized interactions, customer behavior modeling has been upgraded to utilize dynamic and precise measurements of behavior. Moreover, in the past couple of years, the rise of machine learning and AI has enabled the use of advanced modeling methods that are far more effective than conventional rule-based methods. By combining a number of technologies into an integrated, closed-loop system, marketers can enjoy a highly accurate customer behavior analysis in an easy-to-use application today.

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 specific behaviors – instead of hard-coding pre-conceived notions about what makes customers similar to one another, look into aggregated data that reveals important facts about individual customers.
2. Tracking customers along different segments over time (i.e., dynamic segmentation) using cohort analysis – instead of just determining what segments customers are, utilize context clues
3. Accurately predicting the future behaviors of customers (e.g., convert, churn, spend more, spend less) – instead of looking in the rear-view mirror of historical data, use predictive customer behavior modeling techniques
4. Using advanced calculations to determine the customer lifetime value (LTV) of every customer – instead of only looking at the short-term revenue that a customer may bring the company
5. Knowing, based on objective metrics, exactly what actions to follow for each customer – instead of trying to figure out what to do based on a dashboard or pile of reports, know how to maximize long-term value of every customer
6. Employing marketing machine learning technologies that can reveal insights and make recommendations for improving customer marketing that human marketers are unlikely to spot on their own.

One way to think of the difference between conventional approaches and the advanced approach is that the former is like a customer snapshot whereas the latter is a customer animation. The animated view of the customer is far more true to life, and therefore much more revealing, allowing much more accurate customer behavior predictions.