How To Predict Purchase Probability With Random Forest Models

Predicting_Purchase_Probability_With_Random_Forest

Will the user abandon my shop without purchasing? This is one of the most common questions every shop manager is facing. A lot companies are collecting and analysing huge amounts of user data (click behavior, previous purchases etc.) to answer it. Did you ever wonder how you can predict purchasing probability while the visitor is still surfing the shop site? First you need to collect the right user data. Finally smart algorithms like Random Forests provide you with valid predictions who will buy or not.

Steps For Predicting Purchase Probability
Steps For Predicting Purchase Probability

Multi-staged Questions Help To Identify Buyers

It is necessary to know about Decision Trees to understand Random Forest algorithms. A Decision Tree is a group of questions you ask to get to a conclusion step-by-step. In case of our purchasing probability example you could start with “Do we know the customer?” followed by further questions like “Has the user viewed more than 3 products?”, “Does the visit last longer than 5 minutes?”, “Are already products in the basket?” and many more. In the end you classify the visitor as a buyer or a non-buyer for every single branch of the tree. The graphic below shows the principle of a Decision Tree analysis. Of course, in reality the analysis will depend on a variety of additional factors. For example, Akanoo uses combinations of over 50 independent variables to calculate purchasing probabilities.

Example_Decision_Tree
Simplified Graphic For A Decision Tree Analysis

How Can I Use Random Forest To Increase My Online Shop Revenue?

Although it looks like a great tool to predict probabilities, there is one essential problem: Overfitting. Questions for a Decision Tree are created with the help of a training data set. For this training data set the predictions are very reliable. But if you are trying to generalize and adapt it to new data sets, the predictions aren’t that good anymore. The Decision Tree is adapted too much to the initial training data set – it is overfitting.

To avoid it, you can use a combination of different trees – a Random Forest – and built an average value. Usually a Random Forest consists of about hundreds of different Decision Trees and supplies more precise, applicable results to new data.

Apart from predicting the purchase probability, it is useful for a wide range of E-Commerce questions, e.g. regarding:

  • Sales increase: Will the visitor add a second product to the shopping cart?
  • Sales decrease: Will the visitor remove a product from the shopping cart?

Finding answers to questions like this will be very helpful for optimizing your online shop. For example, Akanoo uses the prediction results to show users personalized campaigns highlighting top sellers, giving individual coupons or guiding the visitor to more relevant selections of products to increase revenues and profits of online shops.

If you have any questions, how your online shop can profit from using Random Forest and personalized incentives, send us a message. We are happy to help you.