Hitmetrix - User behavior analytics & recording

Lead scoring by the numbers

Successful B-to-b marketers are able to achieve a flood of inquiries and information requests from tradeshows, online events, whitepaper downloads, e-mail campaigns, a friend-of-a-friend and elsewhere. It’s generally wasteful or impossible for sales to try to contact every one who has expressed any level of interest in the company. Yet, it’s important that the sales force be able to contact prospects that are ready to buy.

Certainly, the task of separating the highly motivated prospective customer from the mildly interested tradeshow booth visitor takes effort. But with a strong lead-scoring model in place, it’s easier than you think.

Lead scoring is a method of assigning points to each prospect you come across. Points are assigned based on specific criteria you set — those attributes you’ve identified as being most often associated with serious prospective customers. The higher the score, the more likely they’re the right target prospect who is actively engaged in the buying process, and should be routed to sales. Naturally, when you’re able to send sales a list of prospects based on criteria indicating they are ready to buy, sales representatives have better luck at actually engaging in meaningful conversations with potential customers.

Also, a solid lead scoring approach can help you build a more powerful and accountable marketing organization — one based on rigorous analysis and testing, rather than intuition and educated guesswork.

The most accurate lead scoring models comprise both explicit and implicit information. Explicit scores are based on information provided by or about the prospect, for example,  company size, industry segment, job title or geographic location. Implicit scores are derived from monitoring prospect behavior; examples of these include Web site visits, whitepaper downloads or e-mail opens and clicks. Taken together, explicit and implicit factors create a much more nuanced picture of prospects.

Although most models account for explicit data, and some can also score based on implicit actions, few are able to incorporate frequency and recency evaluations into the model. And yet how often and how recently a prospect visited your Web site or downloaded a whitepaper can make a very big difference in determining how interested he or she is in your company’s offerings. By adding frequency and recency elements to the evaluation criteria, the model enables scores to go up or down based on prospect behavior.

Recency metrics are powerful because they add the dimension of time to a lead’s score. Rather than heaping new points on top of old scores when someone repeats an action, a model that takes into account recency will peel off points over time and reset the score when an action is repeated. A good rule of thumb is to have the recency score decay to zero after twice your average sales cycle.

If you haven’t yet implemented an automated lead management program, now is the time. A strong lead-scoring model helps close the gap between marketing and sales. Not only does the sales team enjoy a flow of better-qualified leads, marketing is able to monitor the results of its programs and track its successes. Adjustments can be made to the marketing plan to improve the quality of leads generated, and resources can be better managed accordingly. It’s win-win all the way around.

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