Advanced Customer Segmentation: A Look at the Data
We have a new video for you (see below). But first, some introductory thoughts:
At MindEcology, we see essentially three ways to approach customer segmentation:
1. Simple segmentation: this is the easiest method, but also the most limited in terms of its potential applications. Simple segmentation involves grouping your current customers by total annual revenue (or some other metric) in order to differentiate between them in your ongoing marketing and CRM efforts. While this method is effective when marketing to your current customers, it does not help you reach out to new prospects since there is no link that can be derived between your segmentation model and those people in your trade area who have not bought from you yet.
2. Traditional demographic data-based segmentation: this method allows you to apply your model to people in your market other than your past/existing customers. It involves appending demographic data such as age, income, sex, etc. to your customer data. Traditional demographic segmentation represents a step up since it gives you the ability to directly target prospects in your market who meet your “best customer profile.”
3. Advanced segmentation: by layering in psychographic data with demographic and geographic data, you can potentially gain much greater lift or leverage from your segmentation work than you can with mere traditional demographic data-based segmentation alone. With advanced segmentation, you can find groupings of customers who are more likely to become your customer – and more likely to spend money with you once they do. Once you have it, this type of information is like money in your pocket. It allows you to only target your marketing and advertising toward those prospects who are 3-5 times more likely to buy from you than would be the average prospect.
The following (roughly 11-minute) video compares the merits of these three types of segmentation using a sample data set. If you like data mining, customer segmentation and marketing,you will enjoy this video. It presents a data-oriented argument for engaging in advanced segmentation techniques: