Sales Intelligence: How Reps Find Insight in Customer Data (Part 2)

Six Internal Data Sources to Generate Customer Insight

Inspired by Dave Brock’s post on Sales Intelligence, we began Part 1 of this series by describing the conceptual difference between data/research and sales intelligence: surfacing deeper insight about customer needs. In the spirit of showcasing tangible examples, Parts 2 and 3 of the series will focus on the full spectrum of internal and external data sources available to sales professionals to conduct research. Part 4 will then discuss what it means to distill data and research into insight.

Today’s post (Part 2) is all about internal data. What does ‘internal’ mean? When we talk about internal data, we’re talking about the data that a company (including its employees and systems) generates about its own customers and prospects. Not all of this data is readily available to sales reps, nor is it equally valuable (See the illustrative internal data matrix). The list below is based on ease of access for the rep (highest first). The list is by no means exhaustive, and I invite all of you to add your examples.

1. CRM: Since they’re used daily by reps, accessing CRM systems is a snap. Of course, the value that CRM data provides depends at least partially on the diligence with which reps have entered account, contact and opportunity information in the first place. Still, with a modest investment in data entry, it’s possible for reps to run basic segmentations (e.g., “accounts with no opportunity created this year”). And I said “at least partially” upfront because sophisticated sales organizations are pushing vital customer and prospect intelligence from other data sources to reps via the CRM system.

2. Marketing Response Data: Related to CRM, is marketing response data, which can encompass website visits, website download activity and responses to outbound marketing campaigns. This data is increasingly integrated into CRM so that reps can track the engagement of specific contacts with the company’s marketing activities.

3. Contract Data: Critical nuggets of intelligence are embedded in the actual (and often lengthy) customer contracts, such as discounts, bundled pricing deals and specific service level commitments. The downside is access. Companies have made great strides to digitize contracts. However, the content is still not always easy to search and reps still spend considerable time flipping through the (digitized) pages to find the relevant information.

4. Customer Service Data: Understanding when and why a customer calls for support generates insight. On one hand, you may want to avoid initiating a sales cycle with a customer that’s currently having a lousy sales, implementation or customer service experience. On the other hand, inbound calls can often reflect an underlying customer need. Maybe that recent request for a new password indicates an organizational change you should be aware of. No wonder that some organizations generate 30% of their leads through internal referrals from customer service agents.

5.Customer Purchase Histories: Before you can predict what a customer will buy tomorrow, you should understand what they purchased in the past. Savvy reps can recognize purchase patterns that reflect a likely future need for another product. You’d think that understanding what customers have already purchased would be a relatively straightforward exercise. Yet, complexity can easily creep in. For example, the rep’s view of the account in CRM may not correspond exactly to the way the business bills the customer (e.g., maybe it bills three different locations). Perhaps different products might be billed from different, non-consolidated systems. Or, customers might transact so often that viewing line-item activity is simply not meaningful. But since this data is so valuable, I am not surprised seeing reps diligently taking the extra time to view this data in legacy 1980’s green-screen systems.

6. Product Activity: Many companies are fortunate enough to sell products that ‘call home.’ This means that they send signals back to the company on customer usage trends. A simple example might be a color photocopier designed for 10,000 color copies per month. The copier would send back actual usage data on an ongoing basis. If the usage reaches 15,000 copies, the rep should proactively engage on up-selling the customer on a higher-volume machine. If on the other hand, the data comes back indicating 6,000 Black & White copies, this would signal a mismatch between the product and actual customer needs. Most people are surprised to learn about the breadth of products that ‘call home,’ including hardware, software, credit cards and other payment solutions, payroll systems, online information services, etc. This is one of the most valuable sources of insight for reps, albeit somewhat more difficult to access.

We invite you to add other internal sources you’ve used to conduct research and generate customer insight. We look forward to hearing the specific examples of how you used the data and what you learned to improve your engagement with customers and prospects.

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