Categories

Archives

Back to Blog
How predictive analytics can help you forecast next quarter's sales, calculate customer lifetime values, and more.

A Look at Predicting Future Customer Behaviour Using Data from the Past

Data analysts have the special ability to predict the future. Using data from past behaviours, analysts can determine the likelihood of these behaviours repeating themselves. When it comes to product marketing, being able to see the future is what separates you from the competition and keeps you ahead of the game.

More businesses are catching on to predictive analytics. There are many ways to use predictive analytics for your business, including:

  • Calculating customer lifetime value. With predictive analytics you can determine how much a customer will buy from your company throughout a period of time;
  • Offering product recommendations. These are based on predictions of what product or service you customers will likely want to buy next;
  • Forecasting next quarter’s sales; and,
  • Use digital marketing models to decide which ad to place on specific websites.

To make accurate predictions about future customer behaviour, you need enough quality data from the past, statistical expertise, and the ability to make wise assumptions. Here are the basics on predictive analytics to help you understand the results and make better decisions for your business.

Data

First, you will need to have a substantial amount of data on your customers’ behaviours in order to make better predictions about what their buying patters will be in the future. You should aim to consistently capture data through all the channels your customers go through to make purchases. This will ensure you gather as much quality data as possible.

Customer data includes:

  • What they are buying—this data can be gathered from loyalty programs or credit card purchase histories;
  • What they have bought in the past;
  • The product attributes—this helps improve accuracy of product recommendations; and,
  • The demographics of your customers—i.e. age, gender, location, marital status, and socioeconomic status.

Statistics

Predictive analytics require the use of regression analysis. In this type of analysis, the analyst will create a predictive model by:

  • Making a hypothesis—with set of independent variables (i.e. gender, age, income, or number of visits to your website) that are statistically coordinated with the purchase of a product for a sample group of customers;
  • Performing a regression analysis—determine exactly how correlated each variable is. This usually requires multiple attempts to determine the ideal combination of variables and the best model. The analyst may find that each variable in the model is important for explaining the product purchase. The variables explain the variation in the product’s sales; and,
  • Using the regression coefficients—how much each variable affects purchase behaviour—with the regression equation to calculate a score that predicts the likelihood of the purchase.

This predictive model will help you make predictions for customers who aren’t in the sample. You can compute their scores, and if the scores surpass a specific level, offer the product to them. If you’re working with an experienced data analyst and using good data, then you can count on the likelihood of high-scoring customers buying the product.

Assumptions

Any predictive model relies on assumptions. One major assumption is that the past behaviour will continue into the future. This assumption is based on the idea that people develop habits—strong patterns of behaviour—that last over time. But people can also change their habits, so there is always a possibility that their behaviour patterns will change and not always be predictable.

Over time, assumptions may lose their validity. Predictive models from a few years ago may not be accurate to predict customers’ current behaviours. And as more time passes, the more likely the assumptions will become invalid due to changes in customer behaviour.

Assumptions may also become invalid if a key variable is missing in the model, and that variable has changed significantly throughout time. But you can avoid using invalid assumptions by working with experts in data analysis. Both you and the analyst should continue to monitor the key factors involved with these assumptions since they are likely to change over time.

To gain a better understanding of your predictive models, ask the analyst:

  • What is their source of data for use in the analysis?
  • Is the sample customer data representative of the population?
  • What are the outliers in the data distribution, and, if there are any, how do they affect the results?
  • What are the assumptions used in the analysis?
  • What conditions would make the assumptions invalid?

Although predictive analytics requires plenty of work, it is worth it to be able to predict customers’ future behaviours. If you use enough of the right data, statistical analysis, and cautious, valid assumptions, these predictions can be very accurate. So delve into your customers’ future behaviours and start offering products and services they are likely to purchase.