Customer analytics, number-crunching algorithms that make sense of raw data, has increasingly become a hot technology.
What is driving the market? In times of economic uncertainty, companies more than ever want to walk in lockstep with their best customers, according to the Patricia Seybold Group, which unveiled portions of a report released this month, entitled, "Comparative Analysis of Customer-Centric CRM Analytics."
The report compares analytics software from vendors Business Objects, Oracle, PeopleSoft, Teradata, SAP and SAS. Four factors played into the evaluation: functionality; architecture; product marketing; and company viability. While Patricia Seybold Group did not disclose its main findings, the research firm did provide some guidance on defining CRM analytics, as well as key considerations when assessing the technology's real-time promises.
Tactical use of CRM analytics vary greatly, from identifying loyal customers to understanding back-end processes that affect customer experience to measuring the effectiveness of marketing and sales efforts. But analytics' end goal is almost always to reduce costs by giving companies a better idea of their customers' wants and needs. This can be summed up in a measurement called the Quality of Customer Experience (QCE), a Patricia Seybold Group term.
"QCE is our approach to measuring and monitoring how your business processes and the systems that implement them contribute to the service that you provide to your customers and with the level of satisfaction your customers have with that service," says Mitchell Kramer, an analyst at Patricia Seybold Group and author of the study
Before jumping into the analytics arena, here is a warning: There are dozens of technologies and products that are described as analytic applications, and all of them promise to deliver the benefits of customer-centricity faster, cheaper and simpler than the next, Kramer says. In addition to the companies mentioned above, vendors include E.piphany, Siebel, Blue Martini, Microsoft, among others.
Analytic applications boil down to their ability to process data -- not spew out reports. More often than not, analytic systems are designed to perform complex and long-running processing to a few concurrent users, says Kramer. This differs from operational systems that deliver lightening-fast responses to many users. "Processing and database technology has yet to be developed that can balance between these two types of processing without compromising operational responsiveness or analytic performance. As a result, each should run in its own environment," he says.
So where does this leave real-time analytics? As customer-facing applications such as campaign management, self-service customer support and call center convergence come to the fore, real-time analytics that put information in the hands of customer service representatives and salespeople has become a key selling point. Customer service representatives can cross-sell products to customers while talking on the phone; while salespeople can be armed with the most up-to-date, customized sales strategy as they head to a meeting with a potential customer. "In theory, real-time analysis is a great concept," says Kramer, adding, "In practice, real-time analysis can have significant disadvantages."
Real-time analytics can consume significant CPU resources or require significant database processing, which may compromise a system's responsiveness and result in a poor customer experience, according to Patricia Seybold Group. Real-time analysis may also change marketing, sales and service programs quickly -- sometimes for the worse -- without having undergone a measurement process to gauge its effectiveness.
"There are analyses that are simple enough to be performed in real time," says Kramer. "Our question about these analyses is whether they really add value to the customer experience. They might be so trivial that they add little value... bottom line, take care in using real-time analysis."
Tom Kaneshige also writes for Line56.com
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