Everybody is talking about CRM analytics -- it's one of the industry's latest buzzwords. As today's companies collect more and more customer data, they seek technologies that allow them to use this data to uncover additional
Due to the rise in popularity of CRM technologies over the past 5 years, there has been a growing tidal wave of customer information -- account information, buying history, service requests, order status and channel purchase preference. Companies realize that there is a significant opportunity to increase revenues and customer satisfaction by exploiting the knowledge within this data as part of CRM analytics. The potential payoff is significant: Companies that can effectively manage and analyze their data are able to develop important and actionable insights into customer behavior, including:
-- understanding buying behavior through RFM analysis (recency, frequency and monetary)
-- modeling customer behavior and designing marketing programs that incent profitable relationships
-- executing marketing campaigns and promotions through the optimal channel based upon customer purchase preference
The evolution of CRM Analytics
OLAP (Online Analytical Processing) tools came into vogue in the early 90's. These systems analyzed data from a variety of sources within organizations, and did not necessarily focus on customer data. Financial reporting and analysis, budgeting, planning, and sales reporting and analysis were common OLAP applications. However, most of these applications required aggregated data in order to achieve reasonable, timely access. This became a point of frustration for many users because the real 'critical learning' was in the more intricate details -- for example, 'Which customer is likely to churn'?
At about the same time, a new class of data mining applications was gaining popularity. Like OLAP, these tools were designed to analyze data from a variety of different data sources. However, unlike OLAP, the data mining tools were explicitly designed to "tease" the knowledge out of details within the transactional database. Unfortunately, the new data mining tools were hampered by a lack of accessible and consistent information.
Consequently, another new class of technology has emerged -- Analytical CRM, or CRM analytics. Analytical CRM blends the ease-of-use characteristics of OLAP with the insight afforded by data mining tools. According to Merrill Lynch, the total market for CRM analytics applications in 2003 is expected to be $3.5 billion. It's no surprise that this market is large and growing at a rapid pace since companies are now beginning to realize they are sitting on a wealth of information that is just waiting to be tapped.
While the potential benefits of CRM analytics are exciting, companies should be aware of the complexities and potential challenges when selecting and implementing these applications. We live in a world where customers crave convenience, and they have the freedom to choose their channel of interaction. Successful CRM analytics implementations are always built on a solid, customer-centric transaction database that integrates information from all customer touchpoints -- call centers, branch offices and Web sites. Many well-intentioned CRM projects fail simply because they lack a complete cross-channel history. Once collected, the data needs to be deposited into an environment optimized for sophisticated analysis.
However, this effort should not be underestimated. Because of the large data volumes, the need to integrate external data sources (such as demographic overlay), and the overall richness of the content, this effort can be difficult and may require a substantial professional services engagement. Companies are wise to choose a vendor that has taken the guesswork out of this process by providing them with a pre-configured data warehouse optimized for CRM analytics. Finally, it is imperative that the system support real-time deployment of results. After all, today's insight is today's opportunity.
The future of CRM analytics
As companies continue exploration of CRM analytics and reap incremental benefits, we are seeing a gradual move away from periodic reporting and analysis to real-time analysis that allows for immediate re-execution. For example, using a periodic approach, a company closes the month and then loads the data into a data warehouse. However, that company likely misses many opportunities to intercept their customers and influence their purchase behavior. Accordingly, the next wave of CRM analytics is based on real-time analysis, and will allow companies to intelligently and expeditiously interact with their customers across all channels.
Consider the following example: A customer calls in to a bank's call center to change his or her address. When the zip code is changed in the customer's record, a data mining algorithm deployed at the call center indicates that there are no branch banks in that zip code, and further, that the customer has a very high probability of leaving the bank for a competing bank. A second model interrogates the profitability of the overall customer relationship, and detects that that the customer has just made a very significant deposit of $50,000 to his checking account. Based on this information, the call center operator executes a campaign in real-time and offers the customer a significantly reduced-rate on purchases made with the bank's credit card and a very attractive rate on a certificate of deposit to lock in the $50,000 deposit. In the old, periodic paradigm, it would have taken the bank too long to analyze the impact of the customer's move and the opportunity to cross-sell a CD. Real-time analytics allows companies to move from insight to action and build profitable and persistent customer relationships.
These are exciting times, and the ability for CRM analytics applications to drive process improvements in operational systems is substantial. As a result, we will continue to see new entrants into this market -- all claiming best-of-class functionality and the ability to take the output of analysis and translate it into immediate action. Companies evaluating these solutions should consider the following:
-- Complete multichannel integration - call center, sales force, wireless, and the web
-- A complete corporate memory of all customer interactions
-- An optimized environment for reporting and analysis
-- Tight synchronization between analysis and execution
-- Ability for companies to react and refine in real-time
Dan Lackner is the General Manager of Marketing and Analytic Products at Siebel Systems, Inc.
This was first published in June 2001