9 Various Analytical CRM Application Areas

Introducing some of the Analytical CRM Application areas



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

  • Define Analytical CRM
  • Discuss some of the key areas where analytical CRM can be deployed in an organisation.


There are some key areas that analytical CRM can be used in any organisation, some of these areas include:

  1. Loyalty Analysis
  2. Customer segmentation
  3. customer Profitability
  4. Predictive Modeling
  5. Event Monitoring
  6. Channel Optimization



  1. Loyalty Analysis: Analytical CRM can help the organisation to know the rate at which customers are living their channel to join the competitors, life cycle.  They will be able to know their defection rate.  It will also portray their customer segment.  This will also expose behaviours that are common among customers, the ratio of acquisition to defection among others.  This will also chaw the effectiveness of defection prevention, strategies embarked upon by an organisation.

The package will allow the organization to know the rate at which customers are leaving and why they are leaving and where are they going?  You will have a competitive advantage if you have this information.  It will allow you to beat your competitors at their game.

As I said when I was discussing customer loyalty in series one, I said loyalty analysis is based on REM Analysis.  This has to do with recently, frequency and monitory commitment of the customer to your products and services.

  1. Customer segmentation as part of analytical CRM application areas: Analytical CRM also gives room for customer segmentation.  Under segmentation, you try to break down your customer base into smaller groups based on certain characteristics that they shared together.  Customers in the same customer segment are always similar but they are different compared to customers in other segments.  Here, the organisation tries to segment their customers based on the value they add to the business.  You can read more about customer segmentation in series one of this book.

analytical crm application areas

  1. Customer Profitability as part of analytical CRM application areas: This has much to do with identifying the historical, current and the projected value of the customers and then using it to improve segmentation and to implement customer strategies.  Customer profitability analysis is one of the most important and under-appreciated components of Analytical CRM.

Under customer profitability, you try to know the value of your customers at present and what they are likely to become in the future, using the contemporary information you have about them now as a standard.  In doing this at times, you will need external information.


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After profitability analysis, you need to determine the lifetime value of the customer and what they are likely to worth if they are well taken care of.  In order to do this, you have to perform data mining.  You must be able to differentiate between their potential value and lifetime value.  The differences between the actual value and potential value are simply the differences between the value the customer will bring to the company if the status is maintained and the value he or she could bring if he or she is well taken care of.



One of the best ways to increase the usefulness of strategic analyses is to perform “Understanding/Profit link Study”.  This process establishes emphatically links between the primary intangible qualitative measures of customer understanding (e.g. customer satisfaction, brand perception/strength and customer loyalty) with the critical business outcomes or quantitative data (e.g. market share, profitability or lifetime customer value).

Analytical CRM application areas and forecasting techniques are parts and parcel of analytical CRM.  It helps the organisation to determine.  Customer revenue and predict future receipts.  You should know that before you can accurately predict this, you must understand customers’ profile and behaviours, and know-how they will respond to some marketing and supply strategies such as cross-selling, up-selling, retention among others.



Data mining allows assessment of different, predictive models and the subsequent selection of the best fit.

  1. Predictive Modeling: Predictive modelling is a system that aids an entity in prediction what one of their users will do next.  Multiple actions by the user are considered in determining the identical outcome.
  1. Event Monitoring as part of analytical CRM application areas: triggers the information or action based recognition of specific patterns of data within or among application systems.  Data mining can be used to identify a pattern of interest for each event.  This can be as a result of customer behaviour or other processes.

In monitoring events, the Organisation’s can activate automated e-mails or submission of actions on the event to the customer service centre.  It is very useful in selling and retention services of the organisation.

Event modelling, on the other hand, enables the organisation to carry out successful campaigns and retention strategies.  It will enable them to minor customers behaviour and predict how they are going to respond on different occasions.  They will also be able to target prospects for future promotional offers.  Event modelling will also enable the organisation to know which event is most important to a particular customer and they can use it to the organizational advantage.


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Event modelling seeks ways to reduce the number of promotions, manage the costs of business strategies, and increase the percentage response rate to promotions – all of which ultimately boost profit.


  1. Channel Optimization: The purpose of channel optimization is to find ways of driving sales and services to the lowest cost channels that can bring about the desired value proposition.

The main purpose of channel optimization is to ensure that customers desire maximum satisfaction irrespective of the channel they have chosen to relate with the organisation.

  1. Personalization as part of analytical CRM application areas: Personalization has to do with the act of getting to know the customer through well-informed data gathering.  You are able to look at the past interaction of the customer with the organisation and use the knowledge to come up with personalized services for the customer.  This might also include the provision of personalized contents to the customer irrespective of the communication channel that he has chosen to interact with the organisation.



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

  • Some of the application areas of analytical CRM includes loyalty analysis, customer segmentation, customer profitability, predictive modelling, event monitoring, channel optimization and personalization.
  • Loyalty analysis helps Organisation’s to know the rate at which customers are defecting to competitors’ lifecycle
  • Customer profitability has much to do with identifying the lifetime value of a customer through proper assessment of their past, present and future with the organisation.
  • Customer segmentation has to do with breaking down of customer segments into various groups with the same characteristics
  • Predictive modelling uses customers past information with the organisation to predict customers’ next move.
  • Event monitoring monitors important dates in the lives of propels and customers and uses it to come up with strategies that will allow those dates to be used to the Organisation’s advantage.
  • Channel optimization ensures that customers have the same experience irrespective of the channel they are using to interact with the organisation.
  • Personalization allows the organisation to come up with personalized contents that help the organisation to customers for life.

Introducing the five requirements for Analytical CRM Application



One thing you should know from the onset is that a true test of an analytical CRM application is its ability to deliver information in real-time (24 hours).

Also, talking about requirements from an Analytical CRM application. We should know that it is always for dynamic scoring using in session data from various touchpoints or communication channels that the organisation have.  This is grouped along with pre-interaction data which are being stored in the data warehouse to facilitate serious analysis of the data



When an organisation wants to store data in real-time, they need input from the models in the data mart, pre interaction data in session data.  This is then combined with business logic to define the most suitable offer to customers.  Once a decision is taken, the offers made in real-time to the customer.  This includes real-time customer discount, cross-sell/up-selling activities.

analytical crm applications

In order for the software to qualify as a CRM analytic application, such software must be able to support business processes, separation of functions, time-oriented and integration of data across multiple channels.

The requirement of time orientation exposes a major stumbling block for real-time analytics because true analytic as we have said requires the integration of data forms the past.  As analytical CRM now became integrated across the various department in the organisation, it becomes more important to assure the continuity of the experience from the users’ point of views.


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At the end of this lesson on requirements from an analytical CRM application, we have been able to discover that:

  • The main quality of a good Analysis CRM is its ability to deliver information in real-time.
  • All data from various sources are needed in order to score customers.
  • To qualify as a CRM analytics application, software products must:
  • support business processes
  • Separate functions
  • Be use time-oriented
  • Integrate data from multiple sources.



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Adeniyi Salau

Adeniyi Salau Scrum Master Certified , CCNA R&S , BeingCert and Scrum Certified Digital Marketing Professional, CEP, MOS, MCP, CSCU (Project 2016), Microsoft Certified Security and Networking Associate is a Google and Beingcert Certified Digital Marketer, Project Manager and SEO Expert of repute with about a decade of Blogging and online marketing experience. He is always ready to share his experience with others.

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