Temporal criteria are hugely important to the forecast; a deal delayed is typically a deal denied. The opportunity score should be decremented if there is no activity or update in the last two weeks, if the close date moves out or if the deal stage goes backwards. Client actions that almost certainly mean a delay, such as changes in the deal team on its side, should pull the score down, too.
Social criteria have the benefit of being observable during the sales cycle. It's difficult to get credible numerical scoring on the quality of the client's deal team, but the really sharp sales rep can provide a realistic estimate of the win. Getting that information, however, requires carefully designed incentives for and measurements of forecast accuracy.
Third Quarter: Using CRM for Customer Scoring
This is the most often ignored area of scoring, but it's also the area that has the best chance of being accurate and the biggest payoff in profitability. There are three reasons why.
First, in almost any industry, the marketing and sales cost involved with a new customer acquisition is between five and 10 times the cost of getting an existing customer to buy more from you. While the deal sizes of re-up business may be smaller, the really profitable sales happen over the life of the customer relationship, not during the initial sale.
Commentary: Top 10 CRM Tricks Guaranteed to Lose Customers
In addition, with an existing customer, you have the most credible sources of data coming naturally out of your service, support, consulting and training organizations. If you harness the data properly--and use automated Web-based surveys as often as you can--you have an almost continuous stream of signals from the customer.
Finally, you can construct models of customer behavior that involve fewer assumptions, shorter extrapolations and more interpolations. This means you can tune the scoring algorithms better, as you have tighter feedback loops.
Fourth Quarter: Using CRM to Set Expectations Around Scoring
Scoring systems are relatively easy to set up, but it's difficult to get really good results from them. Technology is not the problem here. Rather, scoring must be based on a model and a set of assumptions, and nobody has a solid model for how individuals make purchasing decisions. If somebody did, then you'd never see mass-market advertising, spam email, or pot-shot marketing campaigns.