You already know why both Sales and Marketing teams need lead scoring. But how can lead scoring help your marketing team make better decisions faster to invest your time and energy in the right place?
In my role as Marketing Director at InsightSquared, I need to have a thorough understanding of how our demand generation campaigns are performing. While I can easily and quickly get an understanding of key metrics such as cost per lead, it can take 2 to 4 months before I really understand the quality of the leads that were brought in. I may find out months after running what I thought was a strong campaign that it ended up generating very few opportunities and deals.
Predictive lead scoring can help close this information gap. A fit-based predictive lead score gives me an early indication of the quality of leads generated by various campaigns. Instead of just measuring the volume of leads generated, I can forecast pipeline (or deal) generation and use that to evaluate campaign performance.
Calculate Historical Performance
To start, I analyze the historical performance of different lead score groups. For this example, let’s bucket our leads into A, B and C, with A being the highest-scoring leads. By analyzing down-the-funnel conversion rates of each bucket of leads, I can determine the likelihood that a lead of a given score will convert into an opportunity.
You can calculate your lead to opportunity rates by looking at leads created within a specific time period and then determine what percentage of those leads converted into opportunities. Remember to choose a timeframe that allows enough time for your leads to have converted into opportunities; this will depend on how long your marketing cycle is. Let’s assume that our leads have the following conversion rates from lead to opportunity
Compare to Current Lead Mix
Using these historical conversion rates, I can compare the lead mix (the number of leads in each lead score bucket) generated by different campaigns. Let’s take a look at the cost and leads generated by two different campaigns:
On first glance, it appears that Campaign 1 was the stronger performer, since for the same cost, it generated more leads. However, if I now look at the lead score mix and use historical conversion rates to forecast opportunity creation, a different picture emerges:
Lead scoring shows me that, while Campaign 1 produced more leads at a lower average cost / lead, Campaign 2 is forecasted to produce more pipeline. I no longer have to wait months to see that Campaign 2 looks like a much better investment. By using lead scoring to shorten the test cycle, I’m able to make more informed decisions quickly and drive higher returns on our marketing efforts.
