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"Predictive analytics is no longer an IT spend, but a business investment," asserted Colin Shearer, SPSS's senior VP of market strategy. "In today's uncertain economic environment, organizations are using predictive analytics to find, grow and retain their customers more effectively."

"Few industries try to sell without at least a basic grasp of the end customer -- but pharma has been operating like that for years, due to regulatory and industry constraints," Colin Shearer, senior vice president for market strategy at SPSS, told CRM Buyer In one project he ...

Colin Shearer is senior vice president of market strategy at SPSS ...

One area that is largely still uncharted is the use of Web site data and its incorporation into a general CRM/BI strategy, Colin Shearer, senior vice president of market strategy for SSPS told CRM Buyer "Typically Web data has been carried separately from customer data. Yes, i...

More companies would like to use the massive influx of unstructured data they receive from customers' e-mails, Colin Shearer, senior vice president of market strategy forSPSS, told CRM Buyer. They certainly don't want customers to walk away with the perception that their missives are not read...

The consumer backlash against unsolicited telemarketing and e-mails coupled with ever-increasing competition for customer attention have forced companies to take a new look at their inbound call centers ...

Predictive Web analytics: Combining Web analytics with predictive analytics, such as data mining and text mining, provides both historical and predictive insights. Examples of the type of intelligence predictive Web analytics delivers include: detecting which paths lead to online sales or a particular business goal; predicting the likelihood of a visitor to respond, buy or churn; understanding content and product affinities; and automatically discovering visitor segments. End Result Although developing a Web-analytics strategy and selecting the right analytics may seem overwhelming, the end result most often proves to be worth the effort. A case in point is Sofmap Company, one of Japan's leading computer retailers. Sofmap discovered that its online customers had difficulty making hardware and software purchasing decisions, which was hindering the company's online sales. The company used SPSS predictive Web analytics, based on the Clementine data mining workbench, to build an engine that recommends appropriate products based on customer profiles, which are created with information gathered during the online registration process and from past transactions. After the predictive recommendation engine went live, page views increased by 67 percent per month, and Sofmap's online profits increased 300 percent. In another example, Thomas Cook, which ranks among the world's leading international travel groups, used Web analytics to generate online customer behavior reports and analyze why some customers had unsatisfactory visits. Using NetGenesis Web analytics from SPSS, Thomas Cook was able to turn a mass of raw, unmanageable Web data into actionable knowledge. As a result, the number of site visitors increased by 100 percent over the previous year, and click-through rates and sales doubled. As these success stories demonstrate, the Web is indeed a viable business channel. It offers more detailed customer data in one convenient place than any other channel, providing a wealth of information on how customers think, act, browse and purchase. To tap into this information gold mine, however, companies must include Web analytics strategies in their broader analytical CRM strategies, and fill in the gap between simple Web metrics that provide counts and advanced Web analytics that provide predictive customer insight. With this multichannel, full-view approach, both companies and their customers will reap measurable rewards. Colin Shearer is vice president of Customer Analytics for SPSS. ...

Monitor Campaign Results. With predictive analytics in place, companies can monitor the entire CRM process to determine whether current marketing campaigns are generating the expected results. Marketers can easily track and evaluate customer metrics on an ongoing basis for instant insight into current customer behavior, as well as statistically sound calculations for predicting future activity. By keeping a close eye on customer metrics such as sales, retention rates, and churn propensity (the likelihood that current customers may be lost to competitors), marketers can revise marketing campaigns to respond to a customer's actual behavior at any given time and continue to monitor the success or failure of marketing efforts. Deep Insight Satisfying customers in today's highly competitive global marketplace has never been more challenging. Having a deeper insight into customer expectations and future behaviors is the key to successful marketing campaigns. Predictive analytics enables marketers to understand the key factors that drive customer value and loyalty, and attract more customers. As they measure and monitor the effect of marketing campaigns on customer profitability, marketers can manage their organizations around the goal of improving customer value by meeting the individual needs of each customer. Colin Shearer is vice president of customer analytics for SPSS Inc. ...

Predictive Web analytics enables companies to move beyond simple page counts and referral information -- to gather historical and predictive Web metrics. Predictive Web analytics' four analytical capabilities help organizations: segment visitors based on their behavior, detect content and product affinities, automatic identify the most significant paths taken through a Web site, and predict visitors' propensity -- for example, to make a purchase, view particular content, or churn. A personal computer and software reseller based in Japan used predictive Web analytics to build a recommendation engine that suggests appropriate products to its Web site visitors based on their personal profiles. This has resulted in a 67 percent increase in average monthly page views, an 18 percent growth in sales, and a 200 percent gain in profits. More Scientific Approach With the massive amounts of customer data generated every minute, and the absolute necessity of managing each customer relationship by understanding current needs and anticipating future needs, predictive analytics is no longer a nice thing to have; it is essential. Today's customers want to feel unique and recognized, and they are forcing a more scientific approach to CRM. Businesses must take the time -- and use the right tools -- to truly understand and satisfy each customer, today and in the future. Colin Shearer is vice president of customer analytics for SPSS Inc. ...

Text mining analyzes unstructured textual data by finding and discovering the patterns and relationships within thousands of documents, such as e-mails, call reports, Web sites and other information sources. Text mining extracts terms and phrases, and then automatically classifies the terms into related groups, such as products, organizations or people, using the meaning and context of the text. Text mining can be used to analyze call agent notes and to provide real-time feedback, such as scripts that can be used to pitch cross-sell and up-sell offers. With the combination of text mining and data mining, call center scripts can be changed instantly to reflect how the caller matches the pattern of previous calls. As the customer speaks or writes, the agent is immediately able to analyze their current and future needs. In this new era of one-to-one marketing, analytics will add more science to the art of marketing. With analytics, businesses will gain a deeper customer understanding that will enable them to market more efficiently and effectively. The result will be what businesses have always wanted: higher profitability and the ability to keep customers loyal and happy -- one customer at a time. Colin Shearer is vice president of customer analytics for SPSS Inc. ...

One approach that could help companies gauge customer happiness involves pooling customer data to create a holistic view, rather than isolating it in silos of information, according to Colin Shearer, vice president of customer analytics at SPSS As Shearer explained in an inter...