With Clementine 12.0, SPSS Goes Deep
Analytical software provider SPSS announced today the latest upgrade of its data-mining workbench Clementine 12.0 and an accompanying Text Mining software solution. The release aims to provide significantly more advanced analytical and predictive power, more comprehensible and visually rich presentations, and all the while making the software more user-friendly, according to Richard Hren, the company's director of product marketing.
Clementine 12.0 offers a suite of solutions focused on data mining. The software covers everything related to source data -- grabbing, merging, transforming, and cleaning it -- and then helps derive actionable insight from that data through modeling, reporting, and analytics. The goal, Hren says, is to help companies address three primary themes: Understand, predict, and act.
"Companies have moved beyond 'business intelligence to report on what's happened backwards' to look forwards," says Rebecca Wettemann, vice president of research at Nucleus Research. Wettemann says her clients are now asking, "How do I predict opportunities for growth?...How do I really leverage all the data that I have...in a meaningful way to better understand my customers?" SPSS users, she says, benefit from what she calls the company's "deep analytics experience and knowledge," as well as the ease of use and scalability of its software.
Hren emphasizes that the new release makes the mining capability more accessible for the entire organization. "You don't really need to be a programmer to do this, or a heavy-duty statistician, or [a] data miner -- the push of all this modern software is really to make the software do all the heavy lifting behind the scenes," he reassures. "You shouldn't have to struggle to get something out of this."
Another important feature in version 12.0 is the Automated Modeling Node, which allows companies to generate hundreds of models in mere moments, whereas the task once took hours or days. The results come so fast, Hren says, "you barely have enough time to get a cup of coffee." Modeling can either create solutions with simple "yes or no" outcomes -- Did this person respond? Yes or no?
-- or continuous variable outcomes, such as predicting customer spend, customer value, or campaign effectiveness. With the ability to input different variables and different assumptions, the software provides deeper customer insight and tells you which model is best, Hren says. Essentially, he adds, the software "speeds the time to an answer," which in turn, allows companies to respond and react faster. "I wish I would have had that capability many, many years ago. I would probably still have some hair on my head," he jokes.
One of SPSS's intentions with this release was to further enhance communication within the company, Hren adds. "Predictive analytics and data mining should not exist in a vacuum," he says. "We don't like generating data for data's sake." The purpose is to use this knowledge to improve business processes and to better understand the customer. Therefore, information intended for audiences beyond the technology department or the marketing department has to be communicable to senior level management -- hence, high-quality visual reporting with graphboards. Not only does Hren claim that Clementine makes graphboards very easy to generate, but the images are interactive, putting users literally in touch with their data.
Hren says that SPSS recognizes that not every business problem can have a prepackaged solution. The more advanced programmer, therefore, will find Clementine's platform continues to be open for any user to take control and tailor the product to best fit a particular business need.
Hren describes the sophistication of SPSS's Text Mining solution as "the next big critical edge." He reports that anywhere from 80 percent to 90 percent of any organization's information is just sitting around -- either in paper or electronic form -- stored, unlooked at, and unused. Today's text mining software, he says, is predominantly being used for content management, but SPSS is unleashing the capability into the predictive world, integrated with the company's existing data mining software. Information -- in the form of contact center notes or blogs or RSS feeds -- can finally be gathered and analyzed to derive insight.
And not just written information, either: In a partnership with speech analytics solution provider CallMiner, SPSS will convert verbal recordings into written documents. In addition, another partnership allows the company to provide translation services for more than 20 languages, with more languages added each month.
The market is just beginning to catch on to how powerful text mining can be, Wettemann says, and its use is already spreading in industries like telecommunications and financial services, where companies want to understand customer churn. Nevertheless, she adds, the full advantage of this capability still relies on business sense. Companies will need to understand not only "the right opportunities where they can apply text mining to deliver value, but also to really understand how they can expand it across the enterprise, and take advantage of [those] benefits," she says.
SPSS may have succeeded in making its product accessible to a wider audience, but this continues to be the company's ongoing challenge, Wettemann says. Most of all, she adds, while SPSS needs to make the software "as intuitive and as usable as Google," focusing on functionality, the firm also should "provid[e] customers the guidance and thought leadership on how they can apply this across the enterprise...[and] how they can have a broader impact on the business."
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