In case you haven’t noticed, the Internet has fundamentally changed how people gather information. This has affected business marketers in particular. Because Web sites provide information that previously came from salespeople, marketers stay engaged with prospects for much longer. This means they must do a better job of understanding and responding to prospects’ interests and deciding when it’s finally time to turn them over to sales. The change from generating leads to nurturing them is the main engine propelling growth of demand generation vendors. Their products manage traditional lead generation campaigns. The goal is no longer getting a name and handing it to sales; instead, it’s to draw people to the company Web site, where they will join equally anonymous visitors from print ads, Web ads, trade shows, search engines and other sources. The real work of the demand generation system starts with the first Web visit. The work begins with tracking visitors’ behavior, delivering the information they need at the moment they need it and convincing them to surrender information about themselves in return. If this sounds like seduction, that’s because it is. The moment of truth comes when marketing sends the lead to sales. If the lead isn’t ready, then sales will complain about low quality. If marketing waits too long, opportunities may be missed. The tool that measures this is lead scoring. Demand generation vendors recognize how important lead scoring is and are rapidly improving their scoring systems in response. Typical enhancements include increasing the scope of data that can be scored, adding precision to the score calculations - for example, reducing the value assigned to each event based on recency - and making it easier to set up the scoring rules. These efforts face a fundamental problem. Traditional lead scores were built by marketing and sales experts who decided what weight to assign to each attribute. This worked well when not much information was available: typically little more than source, company, job title and BANT (budget, authority, needs, timeline) information gathered at the start of the process. In fact, jointly defining the scoring rules was one of the best ways for marketing and sales to align their understanding of lead quality. Today, the volume of data has exploded. Demand generation systems track each page view, document download and email open. They combine information about different visitors from the same company based on a shared Web domain. They also look at the timing of these events to understand when prospects are reaching peak interest levels. Rules of thumb collapse under so much detail. Marketers need formal data mining projects to identify the most important events and behavior patterns. These projects correlate prospect attributes and behaviors from the demand generation system with results captured in the company’s sales automation applications. Assembling this data is relatively easy, because the demand generation systems are designed for tight integration with sales automation systems. However, these systems do not provide data mining and predictive modeling tools. This is no problem for data mining, where most work is done by statisticians who prefer their favorite systems. But for predictive modeling, most scoring formulas are too complex to replicate manually in other systems. The demand generation systems will eventually need to import scoring formulas from external modeling systems or call those systems to generate the scores and return them. Other lead scoring enhancements will follow. Current systems require marketers to manually assign a weight to each event or class. The work limits how precisely the weights can be tuned to each item. But content analysis systems already exist that automatically classify the actual message within each item, allowing more precise weighting with no manual effort. Similarly, existing systems that search the Web and assemble information about a company or individual could easily enrich the prospect profile with new scoring inputs. Content classification and Web searches will initially be provided by third-party systems. The demand generation vendors may eventually build these directly into their products, but in most cases a better solution will be to simplify integration with external specialists through application program interfaces (APIs) or Web services. This will let the demand generation vendors focus on their core products and let their clients benefit from continued progress in other fields. These enhancements will be valuable even if they are not immediately integrated with lead scoring. Salespeople already use demand generation systems to generate automated alerts based on customer and lead behaviors, and then to list those behaviors for manual review. Better content classification and automated external search could make the alert rules more powerful and organize the data better for review. The fundamental challenge for demand generation vendors will be adding these and other capabilities without making their systems too hard for marketers to use. This is a painfully common dynamic in the software industry. Competitive pressures force vendors to add features, and as a result, complexity grows. Demand generation vendors face an unusual counter-pressure from systems targeted at small businesses, which are purposely kept simple. We’ll see if this keeps them from following the usual path.
David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. Raab is also the author of the book The Marketing Performance Measurement Toolkit (Racom Communications). He can be reached at draab@raabassociates.com or via his blog at http://customerexperiencematrix.blogspot.com/.
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