One interesting technique in feature engineering is the use of Decision Trees (and other models) to create or derive new features using combinations of features from the original dataset. Here,...
While the Recency, Frequency, Monetary value or RFM model for customer segmentation might be old, it’s based on sound science, so no matter what customer model you’re building, it’s generally...
Calculating Customer Lifetime Value or CLV is considered a really important thing in marketing and ecommerce, yet most companies can’t do it properly. This clever metric tells you the predicted...
Purchase latency or customer latency is a measure of the number of days between a customer’s orders and is one of the most powerful features in many propensity and churn...
Most datasets you’ll encounter will probably contain categorical variables. They are often highly informative, but the downside is that they’re based on object or datetime data types such as text...
When you’re building a machine learning model, the feature engineering step is often the most important. From your initial small batch of features, the clever use of maths and stats...
When dealing with temporal or time series data, the dates themselves often yield information that can vastly improve the performance of your model. However, to get the best from these...