Customer Relationship Management (CRM) systems are the currency of customer-sales interactions. Effective, simple CRM software helps sales reps to focus on content of conversations rather than the mechanics of conversations, resulting in sales empowerment and productivity gains.
A CRM system can be a boon to sales people. CRM helps overcome the technology hurdle of accessing information over disparate systems. CRM systems help improve collaboration within, above and across the entire organization, allowing the company to speak with one voice. And from a governance perspective, these systems help elevate the customer relationship from individual dependencies to an enterprise-wide strategic asset.
When you add the potency of predictive analytics, a CRM system can be even more valuable. Leaders in analytics, sales operations and technology can fulfill their obligation towards sales empowerment by creating a cohesive approach that brings these disciplines together.
How well we achieve this determines if a CRM system just gains basic acceptance, or whether it is fully adopted and even embraced by sales people who realize its benefits for themselves as well as for their customers.
Here are guidelines to help make that happen:
Consider a multi-stage deployment
In the first stage of CRM implementation, deliver base functionality to the users so that their immediate, tactical pain points are addressed. This often involves getting the system up and running and available without interruption – the kind of stuff that builds rapport with the sales team. It can include consolidating contact hierarchy and transaction history, integrating with hardware (i.e., computer, phones) and software (procurement, shipping), and even interacting with social media.
In subsequent deployment stages, add features – often not present in out-of-the-box CRM systems – that build credibility with sales, extend the functionality and improve the outcomes of customer interaction. This is where predictive analytics can lead the charge. In order to identify hidden opportunities and capitalize on customer interactions, predictive analytics requires three components:
- Synthesizing extensive amounts of data, including cleansing and reduction base on insights
- Applying data mining and robust statistical methods
- Integrating relevant and distilled intelligence back into CRM
These stages need not run in sequence. You can put the basic team in place while initiating the gathering of data relevant for analytics. Then insights gained from analytics can help you prioritize implementation decisions.
Tomorrow, we’ll share more guidelines to help you get the most out of your CRM system with predictive analytics …