SAS and SPSS are the leading vendors in data mining, according Gartner's "Magic Quadrant for Customer Data Mining, 1Q06." Both companies are considered the clear leaders in this space, with no other vendors listed as challengers. Chordiant Software, Fair Isaac, KXEN, Teradata, ThinkAnalytics, and Unica all are listed as niche players. Portrait Software/Quadstone is the single visionary company, defined as having a strong vision for the evolution of the industry.
SAS offers the most complete set of data protection and analytics tools in the market, according to Gareth Herschel, research director at Gartner and author of the report. Still, Herschel says, "the general perception of SAS is that its products are expensive and difficult to use. Consider SAS as a best-of-breed data mining tool to perform the most complex or critical customer data mining tasks."
Herschel cites SPSS as a company that has grown through strong acquisitions, most notably Clementine for data mining, DataDistilleries for real-time predictions, and NetGenesis for Web-site analytics. "These applications have good market visibility and sales traction, and reflect a strong vision for the analytical side of CRM," he says. "Consider SPSS as a best-of-breed data mining vendor with a strong focus on CRM."
While most enterprises rely on a combination of vendors to enable customer data mining, Herschel says companies in this market are doing a good job of offering solutions with wide-ranging functionality. "While SAS and SPSS are the leading vendors of data mining to support a CRM initiative, a large number of niche vendors offer complementary or better solutions for specific aspects of customer data mining," he says.
SAS and SPSS offer broad solutions that will serve most needs that enterprises may have in this market. Chordiant Software, Fair Isaac, Teradata, and Unica have demonstrated synergy between their data mining solutions and their other applications. KXEN, Portrait Software/Quadstone, and ThinkAnalytics are considered complementary to the larger vendors for enterprises willing to trade some analytical sophistication for faster model deployment, according to Herschel.
In general, this is a market that still suffers from a degree of confusion and uncertainty, Herschel contends. While traditional approaches have emphasized analytical excellence and have been provided by vendors of best-of-breed data mining tools, this approach only works well in environments with a small number of models and are not as successful in the context of CRM. "Vendors are continually emerging with value propositions based on more than accuracy, such as automated model building or real-time recommendations for customer interactions, offering the promise of a new source of competitive advantage, even for companies well-versed in data mining and CRM," he says.