Analytical CRM in CCE defined





At the end of this lesson, readers should be able to:

Ø  Define Analytical CRM.

Ø  Differences between Analytical CRM and operational CRM.

Ø  Why Data warehouse is necessary for CRM.

Ø  More about Business intelligence.

Ø  Details about Customer Value Management.


Analytical CRM is a group of applications that utilize the data gathered from core CRM systems to enhance understanding of customer behaviour. It uses the information gathered from customer-facing applications to know more about customers and their purchase behaviours, which is later used to segment the customers and come up with products that can satisfy their needs. CRM analytics is a set of programs that come up with information that helps the organization to solidify their relationship with their customers and come up with a mutually satisfying relationship which allows the organization to make quicker and reasonable decisions about their customers.

Analytical CRM

Analytical CRM is a very much different form of operational CRM in that, it focuses on creating relevant content to customers. It takes time to study customers in order to know what they want and pull all resources together in order to ensure that the organization is able to meet customers at the right place with the right products and services.

There are some things that are unique to CRM analytics. Some of it is segmentation studies, customer migration analysis, cross-sell/up-sell analysis, new + customer model, customer contact optimization, customer attrition and Lifetime value.

When we talk of segmentation studies, we are talking of grouping your customers together based on a certain characteristic that they share. So that you can come up with products that meet their needs. Etisalat, for example, came up with “Etisalat Elite” to meet the needs of the rich on their network.

We can talk of customer migration analysis which has much to do with the customer chose in between the different product that the organization have to offer for sale.

We have cross-sold and up-sell which talks about the opportunity to introduce new products to existing customers or getting to new leads to your lifecycle based on how far you have been able to satisfy your loyal customers.

We can also talk of new customer model which talks about packages that you can introduce, or what your eligible leads are likely to see in your product before they can decide that they want to join your lifecycle. You can come up with models that will attract them to join your lifecycle.

We have customer contact optimization, which has much to do with getting the best of an act of your customer contact. Many organizations believe that having customer contact is all about having your customer residential address and next of kin details. They don’t know that it can be an avenue for the organization to retain such customers for life.

CRM analytics you should know must have a data warehouse and a well planned data structure that allow the organization to have a customized reporting, analysis and data mining may allow extracting of data from operational CRM into a complex database where the organization is able to segment and profile their customers based on facts that they are to draw out from their data warehouse. In that case, the analytics might draw its data from multiple sources as well as call centres.

One of the sources of CRM analytics is Business intelligence applications which consist of tools that include query and reporting tools, business graphics, online Analytical processing (OLAP), statistical analysis, forecasting and data mining. All these tools often package to become one comprehensive tool for analysis purpose.

Some of the software available for CRM analytics did more than analysis, they also provide support and perform campaign management functions such as:

  • Mailing, faxing or email offers to targeted customers.
  • Initiating and managing a program to reacquire former customers.

CRM analytics is quite different from a data warehouse as it feeds the result of analytical processes into operational management tools. This allows the organization to continue to evolve policies that will ensure the actualization of total customer experience.

One after reasons why organizations invest in analytical CRM is based on its ability to manage customer value. It has the capability of making sure that the organization is able to serve their customer across multiple channels.

Above all, Customer Value Management (CVM) is a process that refines and leverages the benefits of customer relationship management. In a broad context, it encompasses customer identification, contact management, campaign management, advanced data modelling and customer scoring.










Fig 30: A diagram showing Business Intelligence functionalities.



At the end of this lesson, we have been able to discover that:

  • Analytical CRM is a group of application that uses data gathered from operational CRM to analyze and under customer behaviour.
  1. Analytical CRM studied customers in order to know what they wand and mobilized resources together in order to give customers exactly what they want.
  • CRM analytics make use of data warehouse and business intelligence.
  • Business intelligence includes Query and reporting, Business graphics, Online Analytical Processing, Statistical Analysis, Forecasting and data mining.
  • CVM includes customer identification, contact management, campaign management, advanced data modelling, and customer scoring.

Brainstorming Session

  1. Organizations need data about their customers in order to understand their behaviours and serve them better. Also, the organization needs to protect their customers’ information. How can an organization ensure that security of customers” information does not affect the achievement of CRM goals?
  2. Many organizations have been selective in the way they approach CRM strategies. Some limits CRM application to call centre development and online relations. What are some of the reasons why many organizations are afraid of embracing CRM (doing business from a customer perspective) in full?




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