Create meaningful customer groups for more relevant interactions

What a group of customers has in common isn’t always obvious. Certainly, there are better ways to find commonalities than simply grouping by age, gender, income and geography – or even recency, frequency and monetary value (RFM). Data science can help by digging deep into all your data to find hidden insights and patterns that create truly meaningful customer segments. Then you can align everything from marketing to product development to customer service to these segments, ensuring every interaction is relevant and engaging.

Know your customers and prospects

Understand what characteristics really matter when it comes to segmenting customers into meaningful groups. Get beyond mere demographics and RFM to find deeper insights.

Tailor each interaction

Use segmentation to ensure every communication, be it through sales, marketing, customer service, is structured to meet the unique needs of that customer’s segment.

Discover new opportunities

Get beyond a single view of your customer base to see the nuances between each individual segment, and the opportunities they represent – for new products or marketing strategies.

Design better products

Determine not just how to communicate to each segment, but what they each want out of your products. Use these insights to improve product design, and sales results.

Customer Segmentation Use Cases

“The seamless integration of RapidMiner’s lightning fast data science platform and QlikView’s strong visualization capabilities provides a Customer Segmentation solution to drive revenue optimization.”

“Because of RapidMiner’s ease-of-use, especially the drag and drop modeling interface, within days our team was using it productively to create customer targeting and segmentation models.”

Get started on your customer segmentation project today!

View Other Use Cases

Churn Prevention

Identify customers likely to leave, take preventative action.

Customer Lifetime Value

Distinguish between customers based on business value.

Demand Forecasting

Know what volumes to expect to improve planning.

Fraud Detection

Identify fraudulent activity quickly, and end it.

Next Best Action

The right action at the right time for the right customer.

Predictive Maintenance

Predict equipment failure, plan cost-effective maintenance.

Price Optimization

Set prices that balance demand, profit, and risk.

Product Propensity

Predict what your customers will buy, before even they know it.

Quality Assurance

Resolve quality issues before they become a problem.

Risk Management

Understand risk to manage it.

Text Mining

Extract insight from unstructured content.

Up- and Cross-Selling

Convince customers to buy more.