How Machine Learning can be used to Predict Customer Behaviour
One of the biggest challenges for the modern business is learning to utilize all of the data available to them in a way that is both meaningful and actionable. However, the potential for using data generated by a website is often left unexplored, and as a result, the intentions and reactions of individual digital customers can be overlooked.
Focus is often placed on the broad strokes – key metrics such as the number of page views this month, or the number of unique visitors. While these figures have their place, we lose the ability to shape our individual customer’s journey, or to identify the customers who need engagement most. As a result, customers who may be on the verge of signing up for a trial, completing a checkout, or any other desirable outcome, can fall through the cracks. We know the outline of the picture, but we are missing all of the shades and complexities needed to understand our customers’ online experience entirely.
On the average website, there is an abundance of information to be collected about who interacts with your site and how. By leveraging all of this data, we can gain insights into customer behavior. Machine learning techniques can be used to determine which customers may be interested in achieving an outcome on your site.
For instance, if a customer is not en route to achieving a desirable outcome, a content offer or a chat offer could help to steer them in the right direction.
Predicting customer behavior can tell you which customers to reach out to on your site, in real time, to convert website visits into tangible outcomes.
So how do we do this? We can use machine learning techniques to create a model, using data collected about customer behavior to date. This model will then tell us how likely a customer is to achieve an outcome, based on what we know about that particular customer.
Gather appropriate data
When trying to predict the likelihood of an event occurring, we look at what has happened so far. We begin by gathering data about every customer visit to the site. This includes demographic information such as location and device type, as well as behavioral data such as how many pages they have viewed and how long they were on the site. To data scientists, these are known as features. We also record whether or not a customer has achieved a particular outcome. These are known as labels.
From here, the premise is simple enough: if we are aware of the features of customers who have previously achieved an outcome, future customers with similar combinations of features are the most likely to also achieve this outcome.
Prepare and transform data
This step, while often overlooked, is usually the most work-intensive. Now that we have collected relevant data, we must change it into a form where it can be used with a machine learning algorithm. Categorical data, such as location or device type, usually needs to be binary-encoded. This is so that it can be recognized in a form that our algorithm can understand. Numerical data often needs to be normalized. Many machine learning algorithms perform better when numbers are scaled between 0 and 1. For instance, the number of pages a customer has viewed would be normalized. We use these techniques on both the features and the labels, with the labels requiring binary-encoding.
Sometimes, certain features can be detrimental to overall performance. It would be more advantageous to omit the feature from the model than to leave it in, as that particular feature does not give us much information. This is where feature selection comes in. Feature selection is the process of deciding which features to use for the model. While some techniques may not require feature selection in all cases, it is a key step in most machine learning algorithms.
Although different algorithms may require slightly different steps to prepare the data, the above process is common for the majority of them.