Data Mining in CRM
To manage the relationship with the customer a business needs to collect the right information about its customers and organise that information for proper analysis and action. It needs to keep that information up-to-date, make it accessible to employees, and provide the know how for employees to convert that data into products better matched to customers’ needs.
The secret to an effective CRM package is not just in what data is collected but in the organising and interpretation of that data. Computers can’t, of course, transform the relationship you have with your customer. That does take a cross-department, top to bottom, corporate desire to build better relationships. But computers and a good computer based CRM solution, can increase sales by as much as 40-50% – as some studies have shown.
This is where Data Mining, Artificial Intelligence, and intelligent search applications come in.
Data mining commonly involves four classes of task:
- Clustering– is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
- Classification– is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam. Common algorithms include decision tree learning, nearest neighbor, naive Bayesian classification, neural networks and support vector machines.
- Regression: Attempts to find a function which models the data with the least error.
- Association rule learning: Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.