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Gartner customer data mining Magic Quadrant author discusses best software and buying tips

Gartner's customer data mining Magic Quadrant author discusses top-ranked vendors, market trends and software buying tips -- including five questions to ask during evaluations.

Many organizations know they want customer data mining software as part of their enterprise analytics strategy this year -- but they're uncertain about how to evaluate and deploy tools, according to a Gartner analyst.

There's a good reason for that, according to Gareth Herschel, research director with Stamford, Conn.-based Gartner Inc. and author of its recent Magic Quadrant for customer data mining. Buyers are hearing mixed messages from vendors and colleagues about the best way to approach customer data mining software evaluations, he said. These tools support sales, marketing and service departments with descriptive and predictive analytic capabilities such as clustering, segmentation, estimation, prediction and affinity analysis, according to the report and ranking of top software vendors. But customer data mining software evaluations can be challenging, Herschel explained. It's often not an apples-to-apples comparison -- and other organizational requirements, such as who will use the tool and where analysis will be deployed, need to be factored into the equation.

"What's confusing organizations is that there's no single accepted best-practice approach to doing customer data mining," Herschel said. "There are different types of vendors out there, each of whom has a legitimate argument. The challenge for organizations is to understand which of these valid arguments should weigh most heavily for them."

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 Many organizations are looking to apply customer data mining in new areas within their business, he said -- and vendors are addressing this by building more packaged applications that solve specific problems. Some companies are also stymied by a wide variety of past analytical tool purchases and have a newfound desire to consolidate and rationalize existing investments, Herschel said. To this end, vendors are scrambling to expand their product lines so that they become the primary platform upon which many capabilities are consolidated. To help organizations sort through all this, the study placed software vendors into one of four quadrants, based on each vendor's vision and ability to execute on that vision.

The reigning analytical heavyweights -- Cary, N.C.-based SAS Institute and Chicago-based SPSS Inc. -- both appeared in the leader's quadrant, where they were last year, based on exceptional performance, vision and ability to execute. These vendors provide analytical workbenches and have a reputation for producing some of the highest-quality, most accurate results, Herschel said. For organizations that require this level of analytical accuracy, and are willing to pay for it, these leaders are a good choice. Herschel expects that other vendors may soon challenge the leaders with visionary technology and purpose-built applications designed for less analytically savvy business users.

But at the moment, the study's challengers quadrant is empty. This is normally where Herschel would place vendors that have customer data mining capabilities complementary to their business applications and a strong customer base. However, CRM leaders such as Microsoft, Oracle, Siebel and SAP haven't had strong offerings, he said. But that could change this year. Once these "800-pound gorillas of CRM" -- particularly Microsoft and Oracle -- come out with more market-impacting, visionary strategies, they could easily take over the challenger's quadrant as soon as next year, Herschel said.

There's a crowd of five in the niche quadrant, though, which includes vendors targeting a specific market segment -- such as a particular function, geography or business problem. Of the niche players, Toronto-based Angoss Software Corp. and Glasgow, U.K.-based ThinkAnalytics Ltd. have broad platforms, which they continue to expand, he said. Others address much more specific problems. Unica Corp., based in Waltham, Mass., is focused on marketing campaign management; Minneapolis-based Fair Isaac Corp.'s strength is enterprise decision management; and Alpharetta, Ga.-based Infor Global Solutions (formerly known as Epiphany) is focused on real-time data mining.

Finally, the visionaries quadrant includes technology innovators with less execution experience than the leaders. Herschel describes this year's visionary vendors as "under the radar, but doing very interesting things." San Francisco-based KXEN Inc. has developed automated model development for organizations that need to quickly create and deploy many, as in thousands, of models. Portrait Software plc, based in Henley-on-Thames, U.K., is doing more on the application side, developing tools that address specific business problems.

Gartner's recommendations for customer data mining software evaluations

Organizations evaluating customer data mining software should avoid getting confused by the market by carefully considering their needs upfront, Herschel said. He suggested some critical questions to ask and answer before venturing into evaluations.

  • What type of questions is an organization hoping to get answered with customer data mining?
  • Who is going to be asking those questions -- analysts, business users or both?
  • Who is going to be responsible for delivering the answers, and what is their skill set?
  • How, and where, will the results of analysis be deployed into the business? Will they go into one specific application or potentially to many places -- such as difficult call centers, point-of-sale or retail applications?
  • Where is the data? Is it all cleansed in a data warehouse or spread out in different data marts? What form is it in? will there be unstructured text or voice involved?

"If you can answer that initial set of questions, then you're in a much stronger position to really look at the vendors in the marketplace," Herschel concluded.

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