2008 Aug 01
Lead Scoring Takes Center Stage
David M. Raab
DM Review
August 2008
In case you haven’t noticed, tadalafil the Internet has fundamentally changed how people gather information. This has affected business marketers in particular. Because Web sites now provide so much information that previously came from salespeople, buy marketers stay engaged with prospects for much longer. This means they must do a better job of understanding and responding to prospect interests, and of deciding when it’s finally time to turn them over to sales.

The change from simply generating leads to actively nurturing them is probably the main engine propelling growth of “demand generation” vendors like Eloqua, Vtrenz, Manticore, Market2Lead and Marketo. Their products, and at least a dozen competitors, manage traditional lead generation campaigns. But the goal is no longer just 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 that first Web visit. It begins tracking visitors’ behavior, trying to deliver the information they need at the moment they need it, and convincing them to surrender information about themselves in return. If this sounds like a seduction, that’s because it is one.

The moment of truth comes when marketing sends the lead over to sales. If the lead isn’t ready, then sales will complain about low quality. If marketing waits too long, opportunities may be missed. Like Goldilock’s porridge, the leads must be not too cold or too hot, but just right.

The instrument used to measure their temperature is lead scoring. Demand generation vendors recognize how important this 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, by reducing the value assigned to each event based on recency—and making it easier to set up the scoring rules.

But these efforts face a fundamental problem. Traditional lead scores were built by marketing and sales experts deciding how 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), 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. And they look at the timing of these events to understand when prospects’ interest is reaching a peak.

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, since the demand generation systems are design for tight integration with sales automation systems, and Salesforce.com in particular. But 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 anyway. 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 to 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 of events. The work involved limits how precisely the weights can be tuned to each item. But content analysis systems already exist that could 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 a better solution in most cases will be to simplify integration with external specialists through 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 better organize the data presented for review.

The fundamental challenge for demand generation vendors will be to add 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 complexity grows as a result. 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.

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David M. Raab is a Principal at Raab Associates Inc., a consultancy specializing in marketing technology and analytics. He can be reached at draab@raabassociates.com.

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