How predictive analytics adds value during & after selection of your CRM system – Part 2

August 25, 2011 - 4 minutes read

Customer Relationship Management and Predictive AnalyticsYesterday we posted the first tip of how to use predictive analytics to make your CRM system even more valuable. Today we share several more tips …

Retain focus on business objectives

The excitement of implementing a tool that solves basic operational problems is understandable. The front-end responsibility of reliability, inter-operability and security is clearly with IT. These challenges are significant.

But it is important to go beyond the technology’s bells and whistles. By establishing a vision for analytics – metrics, measurement methods, forward-looking indicators and performance management – and incorporating these in the design, the rationale for the CRM system and its ROI can be validated. Through predictive analytics, business processes can be mapped and modeled, and benchmarks created for delivering quantifiable goals to the enterprise via the CRM system.

For example, is the primary objective of your CRM to support lead generation, product penetration or customer retention?  Based on your needs, predictive analytics can help develop appropriate forward-looking indicators, expected results and diagnostics of the results at all levels of activity – customer, sales people, products and operational areas. This will allow ongoing correction and calibration of your activity within the CRM system that maintains the focus on the business outcomes, not just at preset review times or at the end of the year.

Implement analytics-based decision processes

Because initial concerns of getting the CRM system up and running usually consumes all priorities, many organizations do not plan enough for life after implementation. There are three specific areas where predictive analytics can help drive ongoing adoption and  value of the investment.  Design these components at implementation:

  • Data integration:  To produce valuable predictive analytics, a significant volume of data regardless of source must be brought together, cleansed and summarized.  Design the system to provide succinct information at the rep’s fingertips, so they get big-picture visibility about customer trends.
  • Action recommendations:  Translate insights into specific steps within a decisioning process. This should start with the sales rep at the center, looking at the customer portfolio holistically. Set an amount of time to spend on each customer based on expected return for each customer interaction. These actions should be part of a deliberate, systematic, established process flow that sales and business leaders know will be acceptable to the users.
  • Performance feedback:  Integral to the decisioning process is showing sales reps (the users) the results and consequences of their actions. No sales person likes leaving money on the table, we think. Feedback should show relative performance of the reps against a control group (also called “business as usual”), peer group and team. These results should be delivered timely – latent enough to be meaningful and short enough to correct developing trends.

Predictive analytics can add significant value when you are considering, selecting and deploying CRM systems. Including your analytic needs up-front rather than as an afterthought can ensure that the CRM system supports your business objectives and outcomes. While the importance of technical requirements are the backbone of a great implementation, making business analytics a close partner can pay rich dividends.

When presenting our solutions to address issues like new customer growth or optimizing relationships, clients often ask, “but isn’t that what our CRM is supposed to do?” Yes, it can, but the intelligence to empower the CRM system can be driven by predictive analytics.

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