Organizations establishing a customer data analytics strategy need to look first not to the data they’ve accumulated...
By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time. Contact TechTarget at 275 Grove Street, Newton, MA.
or the software that’s available, but rather the questions they want to answer, according to Gartner Inc.
“There’s almost no question in the marketplace you cannot ask and get an answer to,” said Gareth Herschel, principal analyst at the Stamford, Conn.-based research firm at its recent Customer 360 conference. “If you want to put the resources into it, you can analyze it and understand it. So, what prioritizes what we analyze? It’s a question of what decisions do you want to take. The decisions are the center of our business strategy.”
Companies competing in the same market often collect similar data about their customers and have the same access to analytical tools. Typically, organizations have the same type of data on the same type of customer as their No. 1 competitor, Herschel said. So, competitive differentiation comes with how they conduct analysis.
“To boil down ‘what is your strategy,’ it has to be about ‘what is the decision,’ not the data,” he said.
Getting started with a customer data analytics strategy
A customer data analytics strategy should begin by identifying which area of analysis offers differentiation, such as operational efficiency, customer intimacy or product leadership.
“Analysis does not equal customer intimacy,” Herschel said. “Analyzing customers will support all these dimensions, but prioritization is key. Start with a business strategy. Hopefully, that gives us a slight edge in more customers.”
Identifying that initial focus was a clear takeaway for Marsha Perry, IT business relationships manager for CMS Energy, a utility company in Lansing, Mich., and an attendee at the conference. CMS Energy is in the midst of an upgrade from SAP CRM 4.0 to CRM 7.0 and is looking for a campaign management tool to connect and analyze multiple channels.
“We are looking at what channels to look at for feedback,” Perry said. “We need to figure out where we want to be, to predict what’s going to happen. We’re just starting to learn.”
Trust in the data
More data is better than more customers, Herschel said, though when questioned during his presentation, he admitted that too much data can be a problem as well. What organizations want to do is understand what data is best suited for their analytics projects. That is much more difficult if you don’t have it, he said.
Additionally, it isn’t necessarily the most complicated analysis that wins out.
“It’s not the companies that do the most sophisticated analysis [that win],” Herschel said. “It’s the questions companies could have asked but didn’t.”
With an understanding of what sort of questions and information an organization needs to differentiate itself, it needs to ensure that the relevant data it collects is trusted. Multiple industry surveys suggest that few in the organization trust their corporate data. Around 20%, on average, say they have a high level of trust in the data, according to Herschel.
“That high level of trust is critical,” he said. “The whole point of analysis is to disconfirm our assumptions. You’re looking for what you didn’t know or what you thought you knew.
If people don’t trust in the data, then they won’t believe it when it disconfirms their assumptions.”
How to structure customer data analytics teams
Traditionally, analytics teams have been closely aligned with the IT organization, but that’s not the way to move forward.
“It’s very important we position the analysts between IT and business,” Herschel said.
In fact, organizations are increasingly outsourcing their customer analytics or at least a portion of it.
At CMS Energy, Perry is dealing with disparate levels of analytics expertise spread across departments.
“Some business partners are at the report level; others are better able to visualize opportunities,” she said.
The analytics software vendors have focused predominantly on the business user, Herschel said, providing things the business user wants -- ease of use, prepackaged reports, prepackaged integration. What IT wants, Herschel noted, is vendor viability, scalability and integration with published standards.
Herschel offered a series of steps organizations can take to initiate an analytics strategy. Companies should track their capabilities. For example, is the marketing or sales organization conservative or aggressive with change? What about IT?
“In theory, these two should match, but in reality one falls off,” Herschel said.
If IT lags behind, outsourcing or Software as a Service may make sense. Other times IT may have built a sophisticated data warehouse but can’t get the business side to make use of it. A stakeholder analysis will inform an organization what areas it needs to address.
Additionally, organizations should prioritize what area it wants to analyze -- the organization itself, the market in which it operates or its customers. Armed with a sense of what areas to address, organizations can simplify the technology purchase.
“Say you have customer churn problems,” Herschel said. “Different vendors will all tell you they have case studies proving they have the answer. But the objective is to identify what drives customer dissatisfaction. Real-time decisioning won’t help there, but social media analytics will.”
Finally, an organization can take the step of making a technology purchase, and many Gartner clients wonder whether they should go with a platform or choose components. The answer is both, Herschel said.
Ultimately, organizations are going to need to turn to multiple vendors for customer analytics. While the analytics market has undergone a wave of consolidation with SAP buying BusinessObjects, Oracle buying Siebel and Hyperion and IBM buying Cognos and SPSS, new niche categories and vendors will emerge.
“You cannot expect a single analytics solution to emerge unless your requirements are standardized and consistent -- which goes against the idea of specializing,” Herschel said. “You cannot assume working with a megavendor will solve all your analytics needs.”
Barney Beal is the News Director for the Business Applications and Architecture Group at TechTarget. You can follow him on Twitter at @barneybeal.