How predictive analytics adds value during & after

selection of your CRM system

Customer Relationship Management (CRM) systems are today’s currency of customer-sales interactions. Effective, simple CRM software helps sales reps focus on content of conversations, bringing sales empowerment and productivity gains.

CRM systems help:

Overcome the technology hurdle of accessing information over disparate systems.

Improve collaboration within, above and across the entire organization, allowing the company to speak with one voice.

Elevate the customer relationship from an individual dependency 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 empower sales by creating a cohesive approach that brings these disciplines together.

How well we achieve this cohesive approach determines if a CRM system gains only basic acceptance …

… or whether it is fully adopted and even embraced by sales people.

3Following are guidelines to make that happen …

1 Consider a multi-stage deployment

In the first stage of CRM implementation, deliver base functions so that immediate, tactical needs are addressed.

Get the system up and running and available without interruption –build rapport with the sales team.

Consolidate contact hierarchy and transaction history, integrate with hardware (i.e., computer, phones) and software (procurement, shipping), and even interactwith social media.

1In later stages, add features – often not present in out-of-the-box CRM systems – that bring value to 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 …

Consider a multi-stage deployment

Whatthree

components

1 Meaningfully synthesize extensive data Apply data mining and robust statistical methods Integrate relevant, distilled intelligence back into CRM

These stages need not run in sequence. Get basic team in place while gathering data relevant for analytics.

Then, insights gained from analytics can help you prioritize implementation decisions.

Consider a multi-stage deployment

2 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 …

2By establishing a vision for analytics: • metrics• measurement• management of performance

… and incorporating in the design, the rationale for the CRM system and its ROI can be validated.

Through predictive analytics: map business processes, create benchmarks, deliver quantifiable goals to the enterprise via the CRM system. For example …

Retain focus on business objectives

2Is the primary objective to support lead generation, product penetration or customer retention?

Based on your needs, predictive analytics can help develop forward-looking indicators, expected results and diagnostics of the results at all levels of activity – customer, sales people, products and operational areas.

Allow ongoing correction and calibration of your activity within the CRM system that maintains focus on the business outcomes throughout the year.

Retain focus on business objectives

3 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.

Predictive analytics can help drive ongoing adoption and value of the investment in three ways.

Design these components at implementation …

3 Implement analytics-based decision processes

Data integration: To produce valuable predictive analytics, significant volume of data regardless of source must be brought together, cleansed and summarized. Not just any and all data. Prioritize, prioritize and then prioritize.

But design CRM system to provide succinct information at the rep’s fingertips, so they get big-picture visibility about customer trends.

3 Implement analytics-based decision processes

Action recommendations: Translate insights into specific steps within a decisioning process. Start with the sales rep, looking at the customer portfolio holistically.

Set time to spend based on expected return for each customer interaction.

Actions should be part of a deliberate process that sales and business leaders believe is valuable to users.

3 Implement analytics-based decision processes

Performance feedback: Integral to the decisioning process is showing sales reps (the users) the results and consequences of their actions.

What sales rep would want to leave money on the table?

Feedback should be relative performance of rep against a control (“business as usual”), peer group and team.

Results should be delivered timely – latent enough to be meaningful and early to correct developing trends.

So there you go.

Predictive analytics add significant value when you consider, select and deploy CRM systems.

Including your analytic needs up-front ensures that CRM supports your business objectives and outcomes.

While technical requirements are the backbone of a good implementation, business analytics can pay rich dividends forever after…

When presenting our solutions 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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Predictive analytics for salesLead gen, call timing, cross and upsell, customer

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