Data Mining For Business Analytics

With five practical use-cases: Attrition, conversion, units sold, churn, and cross-selling.

Photo by Jonny Caspari on Unsplash. Edited by author.

Data mining means collecting and analyzing data to find patterns. This technique spans many industries. The NSA collects billions of pieces of private data to find patterns in the behavior of targets. Analysts collect and analyze horse racing data to find patterns in winning horses. It’s even used in connected cars, to find patterns in vehicle and driver performance.

Here, I’ll explore the applications of data mining for business analytics, or finding patterns in data to solve business problems. We’ll cover five use-cases using the no-code analytics tool Apteo: Attrition, conversion, units sold, churn, and cross-selling.

Defining the Problem

The first step to data mining is getting laser-focused on your objective. It’s not a random search for anything interesting, but instead is a way to meet a specific business need and achieve a measurable goal.

For example, increasing the average customer order size isn’t the same as increasing the number of customers, or keeping the customers you already have. It’s important to define your problem, because you need to use data according to the business question at hand.