INTICS - Intelligent Analytics
INTICS - Intelligent Analytics
INTICS - Intelligent Analytics
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INTICS - Intelligent Analytics
INTICS - Intelligent Analytics
Overview

Customer Relationship Management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendor needs. Although the concept and technology of CRM are barely old, there is already feeling among clients that it may not live up to its promise. The perceived lack of delivery has its roots in three areas:

  • Organisations who were keen to replicate the apparent successes of the earliest adopters, did not subject the idea to a rigorous business case. Because CRM worked well for a competitor, it was assumed that it would work equally well for them.
  • CRM often is concentrated on creating a dialogue with customer through trial and error, rather than on a single, operational deliverable, and this left a significant roster of operational issues still to address.
  • Sometimes low quality of services provided by outsourcing contact centres and vendors.

Our experience shows that at present there are still issues in integration into existing systems and processes. The challenge is to develop more specific applications of the CRM concept and apply intelligent analytics to the collection of data, information and knowledge. This aims to suit a client’s unique set of circumstances, rather than assuming that one size of CRM fits all.

 
INTICS - Intelligent Analytics
 
 

Our work in Analytical CRM and CLV

We differentiate between analytical CRM and behavioral one which is focus of another section. Analytical work involves using customer data to design statistical models of choice over the breadth of products and using them to increase the revenues from customers over their lifecycle, so called Customer Lifetime Value, or CLV.

The problems our customers face:

  • CRM applications typically involve vast amounts of data. This includes many observations and many variables.
  • Analysis involves an inward looking view of the customer, as well as competitive information available from credit reference agencies.
  • It is a dynamic process, where low response rates are often the norm, calling for more flexible response and adaptive models than the popular regression model.

Our work on customer acquisition, development and retention strategies has been well known and recognised in financial services industries in the UK, US, Europe and Middle East. The models we developed are helping in early detection and prevention of customer attrition, which also enhance the total lifetime of the customer base, when the efforts are focused on the retention of valuable customers. Applying INTICS CLV framework in your organisations is a key for success and better business performance. Read more ...


 
 
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