Predicting Customer Behavior with AI: A Starter Guide

March 30, 2019

By Matthew Moxam, Senior Consultant and Pratik Panchal, Technical Consultant

With the advent of the Cloud and increased computing power, AI and machine learning have given rise to the ability to process massive amounts of data at scale. On the surface, this may seem like it has little relevance to the practice of business, and really applies to cutting-edge research or informatics.

The eCommerce industry has been harnessing the power of data for decades, using it to predict customer behavior. Even the least digitally conscious of today’s consumers have recognized that Amazon knows their favorite brand of toothpaste.

What this means is that our ability to predict customer behavior has also massively improved.

Few companies, however, have the horsepower behind Amazon’s data tracking and processing. So how can the average business utilize these tools to drive value?

The answer varies depending on what your needs as an organization are, but a typical starting point is to first identify something you would like to know about your customers.

Customer Behavior Questionnaire

Simple standard questions to ask yourself about your customers are:

• What is X customer most likely to buy next?
• How many of Y items will X customer purchase next?
• When is X customer most likely to next make a purchase?

This list can quickly grow long and complex.

For this article, we’ll focus on how we can drive value by answering even one of these questions, and more importantly, we’ll explore how to answer them.

The value of knowing what your customer will buy next seems self-evident. On the business end, it ensures you have adequate amounts in stock and helps suggest other products relevant to their needs, personalizing their experience.

Accordingly, if you know what people are going to buy, and how much of it, you can ensure that everything is in place to make that customer’s experience smooth and painless. It allows you to stock adequate amounts, prepare for spikes in traffic, and set systems in place to collect solid data and better analyze purchasing patterns.

Thus, the value of answering one question can impact business processes, minimize disruption, improve customer satisfaction…and lead to even more questions.

For example, you now know what and how much – but when are these purchases going to happen?

Knowing when a customer will make a purchase seems an ambitious question.

Nonetheless, it’s one we tackled for a large ecommerce B2B retailer.

We approached it using the standard Machine Learning tools Keras and TensorFlow and utilized Regression and LSTM models for make predictions.

These are common machine learning tools and algorithms that allow us to train and make predictions based on our data. We also employed Elastic Search to store and visualize our data and prediction results.

Of course, even the best tools are obsolete without solid data on which to pull and train prediction models. We used data on users’ search history, clicks, filters, purchase histories, and any other useful site activity metrics in the customer journey, even if it was just while browsing.

How accurate is customer prediction?

Standard machine learning works like this:

• Create your training data
• Create a model
• Train the model
• Make predictions against the model

None of this is magic or rocket science: ultimately, we are simply drawing a curve across similar outcomes, and predictions are pulled from the points closest to the curve. The complexity of this can grow, of course, and different algorithms are used depending on the type of problem to be solved.

We created a model based on 2 years’ worth of data and then made predictions against data that was collected several months after.

The results are summarized below: