3 Ways to Use Lead Scoring Within Your Marketing Automation Programs

I wrote last week about the difficulty of linking marketing leads to sales results. One reason the topic was on my mind is I’m also thinking a lot these days about lead scoring. The practical use of lead scoring is to decide which leads to pass from marketing automation to sales, or, even more pragmatically, to predict which leads will be accepted by sales.* But the ultimate goal is to identify the leads most likely to generate revenue. Building an accurate scoring model therefore requires an accurate view of how leads and revenue are connected.

For all the reasons I discussed last week, that lead-to-revenue connection is hard to make. This is one reason that most lead scoring projects focus instead on the criteria that salespeople use in judging which leads to accept. The other reason is that salespeople can decide which leads they’ll work on – so giving them what they want, regardless of whether it’s what they really need, is the key to lead scoring being considered a success.

Many companies today have inserted a phone call between marketing automation and the sales department, screening every plausible lead before sending them to actual salespeople. This reduces the need for scoring accuracy because the phone call will clarify whether the lead is sales ready.  Since the cost of a missed opportunity is much higher than the cost of a wasted phone call, scoring in this situation must simply find all leads with a reasonable chance of success.

In short, scoring programs face two scenarios:

- for scores that directly determine which leads are sent to sales, accuracy is needed but data on past results (necessary to build a good model) is scarce

- for scores that determine which leads get a screening call, accuracy isn’t very important.

Perhaps this is why so few companies use lead scoring (just 19% in a recent MarketingSherpa study) and why the scoring models tend to be simplistic. Investment in more sophisticated techniques, such as statistically-based predictive models, is rarely worth the cost.

There is, however, another use for lead scoring: assigning leads to stages as they move through the marketing funnel.**

Conceptually, assigning leads to funnel stages is quite different from calculating their probability of making a purchase. A funnel stage is defined by meeting specific criteria such as BANT (budget, authority, need and timing) and engagement (downloading a paper or providing contact information). This is more like a checklist than a numeric score, although items like the number of specified behaviors may be calculated. Still, it's sometimes convenient to use score ranges as stage definitions.

In this context, scoring can be used in three ways.

- assign points  to directly to stage criteria.  For example, imagine a three-stage funnel of Respondent (replied to an email), Qualified Respondent (meets BANT conditions) and Sales Ready Lead (demonstrates engagement). If the scoring rules give 100 points for a response, 100 points for meeting BANT criteria, and 100 points for demonstrating sufficient engagement, then people with 100 points are Respondents, people with 200 points are Qualified Respondents, and people with 300 points are Sales Ready Leads. This is a common approach, although it’s not much different from applying the same rules to classify leads directly.


- treat the score as a probability estimate of reaching the final goal (sales readiness, sales acceptance, or revenue). Under this approach, a Respondent might be someone with a goal probability of under 10%; a Qualified Respondent might have a goal probability of 10% to 50%, and Sales Ready Lead might have a goal probability above 50%. This method avoids the need to define specific lead stage criteria, replacing them with objective predictive modeling methods that are likely to be more accurate.

- treat the score as a probability estimate of reaching the next stage (Respondent, Qualified Respondent, etc.). This retains the explicit stage criteria, which may help marketers visualize who is in each stage and how best to treat them. The predictive model provides additional segmentation within each stage, so marketers can focus their efforts on the most promising leads. Since linking leads to stage movement is easier than linking them to revenue, these predictive models are easier to build.

Today, most companies probably do a hybrid of the first and second options. That is, they assign points based on specified criteria (first option) but assign stages based on point ranges (second option). This combines the familiarity of criteria-based scoring rules with the convenience of numerical stage definitions, making it the easiest method available. But it is also doubly arbitrary, since neither the point values nor the range boundaries can be measured against an objective standard.

I’d suggest that marketers move towards a purer version of the second method, building statistical models that predict the final goal (revenue if available; sales acceptance or sales-ready lead criteria if not). Stage definitions can be arbitrary ranges but correlated against existing stage criteria. Eventually, marketers may want to move toward the third method, with separate models for each stage. This makes it easier to focus on advancing leads from one stage to the next while retaining the rigor of a statistically based approach.


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* For example, Marketo’s Definitive Guide to Lead Scoring defines lead scoring as “a shared sales and marketing methodology for ranking leads in order to determine their sales-readiness.”

**Eloqua’s Grande Guide to Lead Scoring puts it nicely: lead scoring “helps marketing and sales professionals identify where each prospect is in the buying process.”