Bolstering the bottom line with analytics

By James R. Borck, InfoWorld |  Software

The CRM (customer relationship management) analytics model is nothing new; it's an old-school concept that has evolved to meet modern-day requirements for recognizing customer desires. Analytical CRM is the mining of data and the application of mathematical, and sometimes common-sense, models to better understand the consumer. By extrapolating useful insights into market and customer behaviors, companies can adjust business rules and react to customers in a relevant, personalized manner.


Although the concept hasn't changed much from the halcyon days of small-town local grocers, the process certainly has become far more scientific. Because we conduct business with fewer face-to-face exchanges, getting to know and understand the customer has become even more complicated. The rise of e-business has driven the demand for more comprehensive toolsets for data mining and knowledge interpretation.


The InfoWorld CRM Survey provides strong indicators that CRM is of critical importance to the vast majority of the 500 respondents. But the results also reflect the nagging concerns most have in taking the CRM plunge. Part of the problem stems from a lack of consensus as to what precisely CRM is. For some CTOs, CRM is the ability to track customers and launch personalized e-mail campaigns. For others, it takes on a broader definition that encompasses automated sales force and marketing tools. CTOs tend to share two notions about CRM: Implementation is costly and complex, and the resulting solution often has poorly demonstrable ROI.



CRM analytics

BUSINESS CASE

CRM analytical models provide insight into interdata relationships, such as customer behavior and buying patterns. Predicative modeling helps streamline enterprise workflow, improve supply-chain efficiency, and enable increasingly personalized customer experiences.

TECHNOLOGY CASE

Packaged implementations are easily installed, but few offer fully comprehensive solutions yet. The need for tighter integration into existing enterprise systems is imperative.

PROS

+ CRM makes "sense" of complex data resources.

+ Predictive modeling builds efficiency.

+ Iterative process continually hones data.

CONS

- Predictive modeling of human nature is never a sure bet.

- Most vendors lack deep enterprise integration.

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