CRM - October 2008 - (Page SF19)

Sponsored Content October 2008 19 Data Mining and CRM Predicting (and Improving) Sales Performance Your customers and prospects are more predictable than you think. They engage, inquire, negotiate, and purchase according to quiet little patterns etched into transactional and behavioral histories stored in your very own CRM system. Organizations commonly focus on using this data to foster insight into understanding “what happened” or “what is happening”. And while applying a BI strategy to grasp “rear-view mirror” sales and revenue trends is important, the same pool of customer data remains a largely untapped source for powerful predictive insight. FOCUS ON SALES PRODUCTIVITY executive is ultimately accountable is only as reliable as the individual contributions of those very human sales reps and account managers themselves. Both these issues are fundamental – how can organizations become dispassionate in their interpretation of buyer intentions, and how further can sales productivity improve? GETTING PREDICTIVE WITH SALES AND FORECASTING eliminate the surprise factor that often impacts sales teams at the end of fiscal periods. A LITTLE BIT OF WORKFLOW GOES A LONG WAY There are two basic business challenges in the sales channel that operational CRM systems cannot fully address. First, there is the cost of sales itself, or the net sales expense. Sales representatives need to build pipelines, develop accounts, and mine leads in order to ultimately close business. These activities, as well as the efforts of those that support this process, are all costly. Sales and business leadership remains under increasing pressure to meet targets without increasing cost, which can only be accomplished through further improvements in productivity. BEYOND THE HUMAN FACTOR Secondly, sales teams - and the sales representatives and account managers that comprise them - are given to very human foibles, and not least among these is the emotional subtext that enters into the sales process. The purchasing intentions of clients and prospects are mediated and interpreted by sales who in turn apply the sum of their experience and intuition to forecast outcomes of these engagements. As a result, the forecast for which the sales Given that wealth of untapped sales and customer data, this is where predictive analytics comes in. Consider some of the inefficiencies in the demand generation process. It is common for organizations to maintain thousands or even tens of thousands of sales leads in their systems. Yet statistically, only a very small percentage of these ever convert. With black-and-white probabilities to convert, predictive analytics can provide an indication of which leads from a database of several thousand actually matter. This bolsters productivity for sales teams by quickly and easily delivering insight to convert more leads (and close more business) with dramatically fewer calls. Similarly, consider the scenario of the sales manager looking at a pool of open opportunities and a looming quarter end. The most productive use of resources would be one where the sales leader could ensure coverage of the opportunities most likely to close by the end of the quarter, another application of predictive analytics. Predictive analytics can also address the human issues inherent in forecast reliability. Individual interpretations of buying propensity give way to a scientific extrapolation of forecast results that not only applies data mining and statistics to improve accuracy, but also helps to With predictive scores for lead and opportunity conversion and account purchasing and attrition integrated into the CRM environment, users can couple these results with the workflow capabilities native to these systems to automate simple, but potentially critical business interactions. Account managers can be alerted by email when a client’s ‘attrition score’ becomes concerning, allowing steps to be taken to retain the customer before it’s too late. Opportunities or leads can automatically be generated and assigned to the account owner when a client’s ‘up-sell score’ indicates they are a good candidate for a new product offer. Sales leaders can be notified of significant changes in their predictive forecast, long before such changes would have bubbled up organically. The breadth of applications for predictive analytics for sales and marketing are many. With the maturity of CRM systems and the wealth in client data they are silently accruing, the convergence of these technologies is not only complementary, but imperative. ABOUT ANGOSS SOFTWARE Angoss is a leading provider of on-demand customer analytics, providing data mining and predictive analytics software and solutions to many of the world’s foremost financial services, telecom, retail, and life sciences organizations. Angoss offers KnowledgeSEEKER for salesforce.com to deliver the benefits of predictive customer analytics to salesforce.com users. http://www.salesforce.com http://www.salesforce.com

Table of Contents Feed for the Digital Edition of CRM - October 2008

CRM - October 2008 Contents Front Office Feedback Reality�Check Customer Centricity The Tipping Point Sprinting Toward Disaster? SAP�Retains Market-Share Lead in�CRM A�Week of Strong Customer�Service CRM�on Twitter Build a Good Event and They Will Come Required Reading There's No Place Like Home The New Breed of CRM�Consultant The Price is Right...You Hope How Much Marketing is Too�Much? The�Sweet Smell of High-Quality�Service The Next Act! For An Acquisition Some Stories Never Get Old CRM�Eases the Pressure For WIKA�Instruments Secret of My Success Re:Tooling Scouting Report Pint of View

CRM - October 2008

For optimal viewing of this digital publication, please enable JavaScript and then refresh the page. If you would like to try to load the digital publication without using Flash Player detection, please click here.