Customer segmentation models calculate risk scores based on the organizations’ historical data. This includes finding patterns within the transactional data, demographic data etc. The risk scores can be combined with the total customer spend in order to discover actionable insights for planning marketing strategies. We talked about the risk-value matrix briefly in a previous blog - let’s go
ahead and take a deeper look at it - the machine learning segmentation model assigns a risk score to all the customers depending on their buying patterns and demographics. Based on the risk score and customer value, we assign them to a cohort to implement specific marketing strategy. The four major groups are – High Risk - High Value, High Risk - Low Value, Low Risk - High Value, Low Risk - Low Value. Creating marketing strategies in a way that aims at a specific target group can make a huge difference in the overall ROI.
Game Plan for Different Customer Segments
There are different ways to create a marketing strategy for each customer cohort to maximize ROI. Customers falling in the High Risk - High Value bracket are the most important to cater to. These are the customers who should receive discounts and promotions, so they do not switch to an alternative. Low Risk - High Value customers are the loyal customers. These have a low risk of leaving the company, so we should try to cross-sell by recommending different products based on their buying habits. Low Risk - Low Value customers have a low risk of leaving but are also low in value. There is an opportunity for us to up-sell here and create marketing content that converts this group to high value customers. High Risk - Low Value customers should be the last to be taken care of. These customers are of low value and we do not want to spend many resources on them. Hence, taking care of each customer segment and personalized marketing is the way to go.
In conclusion, targeted marketing is an important aspect in generating higher ROIs. The biggest challenge here is to create segmentation models and assigning risk scores to the customers by leveraging the power of Machine Learning in the right way. Dunn Solutions’ data scientists have helped many companies overcome this challenge!