What is predictive behavior modeling?

Do you think predicting the future is a job for a fortune teller with a glass ball? Or, do you side with skeptics that consider even such ploys as a clever manipulation? Regardless of your choice, in both cases, you are partly right. Telling the future is impossible, yet outlining the direction where it is heading, isn’t. These days some techniques facilitate forecasting and predictive behavior modeling is one of them.

Definition of predictive behavior modeling

Forecast in the case of predictive behavior modeling is not based on a glass ball but on accumulating historical data. Harnessing the past for this process will deliver a variety of answers but rather an indication of which way to go and what to focus on.

Predictive behavior modeling is great for foreseeing customers’ purchasing decisions but also has a variety of other business applications. In the case of customers, using this type of tool helps tailor the offer to the specific needs of the individual. This makes the product or service more relevant in the first place. The customers know that and feel cared for, a sense of uniqueness. Besides, sending targeted offers also has an impact on the company’s image. Customers who do not get “spam” but concrete offers will certainly be more satisfied and positively remember the company.

Naturally, this brings benefits to the company, primarily when it comes to savings. Sending specific offers to customers who are essentially potentially interested in them allows you to get a greater return on the investment of resources allocated to communications. Properly developed predictive behavior models are a convenience for the marketing department and a chance to develop an accurate strategy.

It makes your specialists better determine when, to whom and by what route to send offers so that they are effective in terms of sales. The models can not only shape your offers to match the needs of a particular group of customers but also the likelihood of a particular consumer making a purchase.

What is the difference between predictive behavior modeling and predictive analytics?

Historical data is used to create predictive behavior models, while predictive analysis covers a broader area in which models are one of the elements to determine the direction of the future. In addition to statistical data, predictive analysis also includes various types of algorithms to analyze and evaluate data and estimate the probabilities of specific events.

Thus, it is safe to say that predictive behavior modeling is an element (subset) belonging to the broader concept of predictive analytics.

4 stages of predictive behavior modeling

1. Collect the most accurate data possible. It has to be diverse and real to develop a meaningful model. It is also crucial to properly prepare and process data so that the algorithm can make meaningful forecasts.
2. Teach the model. The key element here is not the proper selection of an algorithm, since several can just as well be used in parallel, but the determination of appropriate test assumptions. At this stage, model learning can be carried out on several versions, but the conclusion of this stage should be the selection of the one with the best generalization ability, and thus can most accurately assess future events.
3. Evaluate the model, estimate its effectiveness. Various methods are applied for this purpose, but the main idea is to test a given model on unknown test data and determine its effectiveness.
4. Put the model into use – forecasting.

What are the advantages of predictive behavior modeling?

Predictive modeling is the key element in understanding future behavior and shaping the direction of future strategies. However, for this to happen, it is necessary to collect data for analysis. What can you gain by applying predictive behavior modeling?

Better prediction of future behavior

It is impossible to say unequivocally how customers will act in the future or what will happen. It’s unrealistic, especially in such a rapidly changing economy. Still, determining the right direction is already possible, just with the help of predictive behavior modeling analyses.

Accurate decision-making based on reliable forecasts

You might say that some people have a good gut feeling or intuition that helps them make important business decisions. There may be something in that. However, a decision based on deep analysis and reliable facts will certainly be even more accurate. In this case, it is better to bet on reliable data than on luck.

Increase profits in the company

With predictive modeling, you can dispose of the resources at hand more effectively. In part, this is made possible by forecasting customer behavior, which translates into better resource management. This applies to virtually every aspect of a company’s operations, and a good example is sending targeted advertisements to customers, which is a cost-saver in itself, but also helps drive the customer to complete the purchase, which increases the company’s profits.

Reducing risk

By planning future activities or the direction of planned changes based on models and hard data, it is easier to manage risks and anticipate possible difficulties.

What are the challenges of predictive behavior modeling?

The basis and essential thing for creating predictive models is data. This is both the most challenging stage and the moment when the greatest number of mistakes occur. Collecting the data, assigning it to appropriate groups and determining its validity, is labor-intensive, but essential. Nonetheless, it is often the case that the data itself is not of sufficient value, and it is necessary to clean it, i.e. to extract what’s necessary to take to further stages of predictive modeling. Problems at this stage that can be encountered are:

• too small a group of respondents
• unreliable data
• excessive data matching
• unavailability of some data

The last point, data inaccessibility, involves some technical barriers, but also organizational ones. While the technical barriers are clear and do not require any deeper analysis, only adequate preparation, the organizational problem can be a bit more difficult to handle. These include the situation where a department or industry does not want to share its data, believing it to be its asset. In such a case, analytical teams may face an insurmountable barrier.

Forecasting customer behavior is an important element that helps in making the right decisions, as well as paving the way for change. Although those involved in the analysis may encounter a bit of difficulty along the way, there are tools with their powerful features available on the market that help avoid measurement errors and develop effective models. Contrary to appearances, creating such models of customer behavior is not only a solution for large companies but can also be useful for small businesses.

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