Predictive Lead Scoring Made Simple
With more and more B2B companies flocking to predictive lead scoring (14x more than in 2011) and more vendors like Mintigo, Lattice Engines, Infer, Fliptop, and others offering it as a service, it raises a simple but important question: what the heck is predictive lead scoring, anyway?
To get the answer, one great place to start is this webinar from Mintigo featuring Kerry Cunningham, Research Director at SiriusDecisions. Go ahead and open that up in another tab or window. If you’re short on time, here’s an abbreviated take on the subject along with a few additional resources below.
For starters, to get a super simple definition of predictive lead scoring, let’s break down the term:
What is it?
Cunningham has a great explanation that doesn’t mince words: “Not all leads convert. Lead scoring is an attempt to predict which will.”
How does it do that?
By looking for clues to indicate conversion is more likely. As my colleague Blake Harris notes, in traditional lead scoring models, those clues typically fall into two camps:
- Activity/engagement-based: A prospect downloading an eBook, registering for a webinar, viewing a pricing page, submitting a form, opening an email, etc.
- Firmographic/demographic-based: A prospect fitting the right criteria such as a particular title, role, company size, industry, etc.
Is it effective?
Those types of clues can sometimes give companies a nice start, but they aren’t enough to enable us to draw truly reliable and accurate conclusions. The only way a marketer or salesperson can make a prediction based on those clues is by taking leaps (ex: do two CMOs at similar-sized companies in the same industry really have the same goals and needs?) and making assumptions (ex: does downloading an eBook really reflect an interest in buying?).
In other words, traditional lead scoring can provide us with clues, but those clues are limited, and putting together a prediction based on those clues still involves a hefty amount of good old-fashioned guesswork.
As a result, salespeople aren’t seeing the value (40% fail to recognize any value from lead scoring, according to SiriusDecisions), and they’re still having to do much of the qualifying work themselves. On average, only 33% of marketing automation qualified leads make it to sales qualified. That means, on average, sales is still doing 2/3 of the qualifying, themselves.
If the job of lead scoring is to make salespeople’s lives easier by removing qualification time and effort on their part, it doesn’t seem to be working very well.
How can we improve lead scoring to do a better job of what we set out for it to do? By building it around predictive analytics.
Predictive Lead Scoring
What is it?
Again, let’s go to Cunningham for the succinct scoop: “The promise of predictive technology is to perform deep qualification of accounts and opportunities through data science and automation.” In this case, we think we can do him one better: Think lead scoring on steroids.
How does it do that?
Again, by looking for clues. The difference is by replacing guesswork with data science, predictive lead scoring bumps up both the quantity and quality of clues at your disposal. In fact, data science can help you uncover and take into account qualifying factors that you may never have considered.
Is it effective?
Adopting a predictive lead scoring model isn’t a guarantee for better results. It may not be the right fit for every company, and there’s still the matter of executing against it effectively (marketers still need to understand key considerations for making good predictions). That said, it can be a very powerful tool that can significantly improve the impact and results of your qualification efforts in the following ways:
- Build a better list: Data science can reach more deeply into online digital artifacts (websites, press releases, job postings, patent filings, social activity, advertising spend, — the list goes on and on) to unearth evidence of business problems and buying initiatives.
- Find the best contacts: Predictive lead scoring can reach much deeper into a contact’s world to determine who is the most likely to be involved in the buying cycle.
- Score and prioritize existing prospects
- Score and prioritize existing customers on propensity to renew/buy more
Bottom line: When qualification is much more in-depth and accurate, sales becomes more efficient as a result.
More About Predictive Lead Scoring
- Demystifying Predictive Lead Scoring (webinar from Mintigo)
- Lead Scoring is Dead: Two New Innovative Approaches by OpenView’s Blake Harris
- Predictive Lead Scoring FAQs by Lattice Engines
Photo by: David Tan
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