Bholane and Mahavidyalaya (2025) explore how AI can be used to predict consumer behavior. Their use of the term feature engineering caught my attention.
Bholane and Mahavidyalaya described:
Identify significant behavioural attributes such as frequency of purchases, average spend, and product preferences. Identify engagement metrics such as interaction with emails, app usage, and response to promotions. Use feature engineering techniques to create variables that enhance model performance.
So, what specifically is feature engineering?
In my own words, feature engineering is the process of transforming raw data into meaningful information that can be used by AI to predict consumer behavior.
Let’s look at an example: date of last purchase.
While this data does provide insights into customer behavior, it can be challenging to use it directly for predicting future actions.
That’s where feature engineering comes in, by transforming these raw data points into more actionable metrics, such as days since last purchase: the number of days elapsed since the last recorded transaction.
Using historical data, we might observe that the time between a customer’s initial purchase and subsequent purchases typically averages around seven days.
With this insight, we gain the ability to predict when the customer is likely to make their next purchase.
This, in turn, help us to create a marketing campaign targeting the customer just before their next predicted purchase, say, two days prior.
By leveraging feature engineering with the use of AI in marketing, you can optimize your customer engagement strategies and maximize the impact of your marketing efforts.