Unlocking CRM’s Data Treasure Trove

Loraine Lawson

Updated · Jan 28, 2015

CRM systems hold much of an organization’s most valuable data, including customer records, product purchase and return information. So it makes sense that it would be among the first enterprise applications business leaders would tap for business intelligence or analytics projects.

“CRM applications provide a central and shared database of customer information, interactions and contact information,” Gartner notes in its 2014 Magic Quadrant CRM Lead Management. “This information helps sales staff, managers and executives manage critical opportunity management, forecasting and reporting requirements.”

CRM metadata can also hold valuable data, such as customer ages, demographics, product purchase and return history. While CRM systems can perform reporting functions that relate to sales, marketing and other customer-centric tasks, they aren’t designed to handle the larger, strategic questions. For those types of analytics, you’ll need to either integrate with CRM or extract the data.

CRM Data’s Pain Points

And that’s where the problems begin, experts say. CRM systems weren’t designed with data sharing in mind. This can lead to serious problems, whether you’re conducting a one-time analytics project, hoping to leverage CRM data for BI or simply migrating to a new CRM solution. Even data experts such as Peter Ku, Informatica’s senior director, Global Industry Marketing, would rather forget the pain of migrating one company to Salesforce.com.

“After 90 days of rolling out SFDC, we ran into some old familiar problems across the business. Sales reps continued to struggle in knowing who was a current customer using our software, marketing managers could not create quality mailing lists for prospecting purposes, and call center reps were not able to tell if the person on the other end was a customer or prospect,” Ku related in a recent blog post. “C-Level executives were questioning our decisions and blaming the applications.”

But it wasn’t the application, Ku wrote: It was the data, which had been moved with unresolved data quality problems, including duplicate records and tagging errors.

The integration and data quality issues created major problems, including $1.44 million in bad data to marketing, a 30 percent drop in sales productivity and a customer support team that spent 50 percent of its time dealing with billing issues due to bad contact data in the CRM system.

The way CRM applications function often creates these data problems. First, CRM relies on manual data entry by customer-facing employees. That alone makes it prone to data errors. Also, CRM systems tend to be a black-hole for customer data. Tien Anh Nguyen, director of Market Insights at venture investment firm OpenView, said it’s not unusual to be overwhelmed by the volume of data contained in CRM.

As head of the Research and Analytics team, Nguyen conducts extensive data analyses on CRM databases for expansion-stage technology companies. It can be daunting, he admitted in a blog post on CRM data analysis, due to:

  • Endless variation in configuration
  • The combination of structured and unstructured data fields
  • Problems with standardization
  • Poor data quality

“When we dug deeper into the datasets, the skeletons kept coming out and forcing us to review and revise our analyses, or even redo them to ensure that our insights were still true,” Nguyen wrote. “Often, the insights and data you uncover point toward conducting new analyses, which will require even more data preparation work. This becomes extremely frustrating, and I know that it is a common issue that happens with CRM data analyses everywhere.”

Tackling CRM’s Data Problems

How you deal with data from CRM depends upon what you want to accomplish. If you’re working on a business intelligence project, you’ll want to be quite specific about what data you need and where it exists, advised Phil Rhodes in a recent InfoWorld article about how to get a successful BI project off the ground. “Understand the current data environment and its impact on the BI solution,” he wrote.

That may mean you’re only extracting one type of data — such as customer addresses — from CRM. In that case you might want to use a data discovery tool, but you’ll also need to reconcile any differences between CRM and other enterprise applications or databases. Master data management practices and tools can help IT reconcile these differences before the data’s loaded into BI systems, Rhodes suggested.

If you happen to need a one-time or infrequent analysis that gives you a snapshot of your CRM data, then Nguyen’s lengthy series, “The Customer Data Mother Lode: How to Conduct CRM Data Analysis,” is an excellent roadmap. But for those who need real-time or on-going analysis of CRM data, Nguyen suggests one of two approaches:

  • Use an out-of-the-box sales data analytics tool. These tools are designed to integrate with CRM and marketing automation, plus they provide benchmarks and dashboards based on best practices. The downside is they’re only available for the most popular CRM platforms, he warns. They also lack the flexibility or customization offered by full-fledged analytic tools, and you won’t have full ownership of the analytics produced with these platforms. You can find a list of 48 sales analytics tools on Docurated.
  • Connect your CRM with your business analytics platform. You’ll gain customization, access and ownership of the data and analytics by taking this option, but Nguyen really only advises it if you have a broader use case for BI.

Many of these same concerns will apply if you’re involved in a CRM migration project, but be sure to check out Ku’s complete post for more recommendations unique to migrations.

Loraine Lawson covers integration technology and business issues for IT Business Edge. She is an award-winning journalist and a former writer and editor for TechRepublic.


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