If you’re a marketer, you’ve probably heard of lead scoring. Let’s recap: Lead scoring is a “rules-based” technique to help you organize and prioritize sales-ready existing customers by using certain data attributes, such as the size of a company or past purchases. For example, a Fortune 1000 company may receive a higher priority than a company with smaller annual revenues.
For sales teams, getting leads from marketing that are worth pursuing is essential. However, most marketing teams aren’t as selective as they should be when it comes to delivering leads to sales. According to MarketingSherpa, 61 percent of B2B marketers send all leads directly to sales—only 27 percent of which are actually qualified. Despite these low MQL-to-SQL conversion rates, MarketingSherpa goes on to report that 79 percent of B2B marketers have not even established lead scoring.With today’s advances in data science, there’s no need to be a part of that 79 percent.
We’ve got World Cup fever, so let’s think about lead scoring in terms of soccer. Lead scoring in the soccer world would equate to D.C. United (Washington D.C.’s soccer team) estimating their win rate by seeing who they’re up against in the soccer club schedule. D.C. United may be able to gauge what games they’ll win (or lose) based on their existing knowledge of other teams’ rosters, how good the coaches are, or past performance.
KNOCKOUT: PREDICTIVE LEAD SCORING
So, you’re able to predict how you might stack up against the other American soccer clubs. But say you’re the US national team six months out from the World Cup: How do you predict how you’ll fare in the World Cup when you don’t know all of the external influencing factors, such as the outcome of randomized qualifying rounds, injuries and red cards, or changes in team rosters? Therein lies the limitation of lead scoring. While lead scoring does prioritize the leads in your pipeline, it does not take into account any external data or activity that could measurably affect the worthiness of that lead.
Predictive lead scoring, though, is a math-based approach to prioritizing leads, which takes into account multiple sources of data (behavioral data in addition to attribute data) to score a prospect’s likelihood to buy. As we found out while interviewing Data Scientist Mio Alter, by understanding what behavior typically leads to a sale, models and algorithms can be trained to recognize those same patterns to predict future sales. The more data, the more accurate the score. While lead scoring alone can lead to a revenue lift, predictive lead scoring is 1000x more powerful.
GOAL: THE RESULTS
Using predictive lead scoring will measurably improve conversion rates and growing sales. It’s like having a Magic 8 Ball that predicts the outcome of the World Cup qualifying rounds, in addition to the season’s intra-national matches.
If you’re interested in seeing how predictive lead scoring can impact your business, let’s talk.
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