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Analytical CRM

Analytical Customer Relationship Management Systems

TQM graphics Graphics courtesy of CRM-NeedsOpens in new window

Analytical CRM, also called analytic CRM, is concerned with capturing, storing, extracting, integrating, processing, interpreting, distributing, using and reporting customer-related data to enhance both customer and company value.

Whereas operational CRMOpens in new window systems support front-office business processes, analytical CRM systems provide business intelligenceOpens in new window by analyzing customer behavior and perceptions.

For example, analytical CRM systems typically provide information on customer requests and transactions, as well as on customer responses to the organization’s marketing, sales, and service initiatives.

These systems also create statistical models of customer behavior and the value of customer relationships over time, as well as forecasts about acquiring, retaining, and losing customers. The Figure below illustrates the relationship between operational CRM systems and analytical CRM systems.

chiasmus diagram showing abba pattern

Analytical CRM builds on the foundation of customer-related information. Customer-related data may be found in enterprise-wide repositories: sales data (purchase history), financial data (payment history, credit score), marketing data (campaign response, loyalty scheme data) and service data.

To these internal data can be added data from external sources: geo-demographic and lifestyle data from business intelligence organizations, for example. These are typically structured datasets held in relational databases.

A relational database is like an Excel spreadsheet where all the data in any row is about a particular customer, and the columns report a particular variable such as name, postcode and so on.

With the application of data mining tools, a company can then interrogate these data. Intelligent interrogation provides answers to questions such as:

  • Who are our most valuable customers?
  • Which customers have the highest propensity to switch to competitors?
  • Which customers would be most likely to respond to a particular offer?

Important technologies in analytical CRM systems include data warehouses, data mining, decision support, and other business intelligence technologiesOpens in new window. After these systems have completed their various analysis, they supply information to the organization in the form of reports and digital dashboards.

In recent years, we have seen the emergence of “big data”. Although the expression “big data” has been around since 2000, it is only since 2010 that businesses have become seriously interested in these huge datasets.

According to IBM, Big dataOpens in new window comes from everywhere: from sensors used to gather climate information, posts to social media sites, digital pictures and videos online, transaction records of online purchases, and from cell phone GPS signals to name a few.

Big dataOpens in new window extends beyond structured data, including unstructured data of all varieties: text, audio, video, click streams, log files and more. The tools for searching, making sense of, and acting on unstructured data differ from those available for data-mining structured datasets.

Analytical CRM systems analyze customer data for a variety of purposes, including:

  • Designing and executing targeted marketing campaigns
  • Increase customer acquisition, cross selling, and up selling
  • Providing input into decisions relating to products and services (e.g., pricing and product development)
  • Providing financial forecasting and customer profitability analysis.

Analytical CRM has become an essential part of many CRM implementations. Operational CRMOpens in new window struggles to reach full effectiveness without analytical information about customers. For example, an understanding of customer value or propensities to buy underpins many operational CRM decisions, such as:

  • Which customers shall we target with this offer?
  • What is the relative priority of customers waiting on the line, and what level of service should be offered?
  • Where should I focus my sales effort?

Analytical CRM can lead companies to decide that selling approaches should differ between customer groups. Higher potential value customers may be offered face-to-face selling; lower value customers may experience telesales.

  • From the customer’s point of view, analytical CRM can deliver timely, customized solutions to the customer’s problems, thereby enhancing customer satisfaction.
  • From the company’s point of view, analytical CRM offers the prospect of more powerful cross-selling and up selling programs, and more effective customer retention and customer acquisition programs.
See Also:
  1. Gamble, P., Stone, M. and Woodcock, N. (1999). Customer relationship marketing: up close and personal. London: Kogan Page; Jain, S. C. (2005). CRM shifts the paradigm. Journal of Strategic Marketing, 13 (December), 275 – 91.
  2. Evans, M., O’Malley, L. and Patterson, M. (2004). Exploring direct and customer relationship marketing. London: Thomson.
  3. Kotler, P. (2000), Marketing management: the millennium edition, Englewood Cliffs, NJ: Prentice-Hall International.
  4. Engle, R.L. and Barnes, M.L. (2000). Sales force automation usage, effectiveness, and cost-benefit in Germany, England and the United States. Journal of Business and Industrial Marketing, 15(4), 216 – 42.
  5. Buttle, F. (2004). Customer relationship management: concepts and tools. Oxford: Elsevier Butterworth-Heinemann.
  6. Payne, A. and Frow, P. (2013). Strategic customer management: integrating CRM and relationship marketing. Cambridge: Cambridge University Press, P. 211. See also Payne, A. (2005). Handbook of CRM: achieving excellence through customer management. Oxford: Elsevier Butterworth-Heinemann; Payne, A. and Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69 (October), 167 – 76.
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