Having too many leads may appear to be a good thing. In fact, most CMOs would love to have this problem. However, being flooded by leads unlikely to buy is a problem in itself, as they bog down your sales time, thus damaging sales activities towards potential customers more likely to buy.
There is a significant difference in being flooded by poor leads versus having quality leads who are just a bit too early in the funnel.
Being flooded by leads unlikely to buy is a problem in itself
There are two solutions to the first situation: either you get rid of the low-quality leads altogether, or you filter them out. In the second situation, you have quality leads worth nurturing, but they aren’t yet ready to purchase.
With a stretched sales organization, both situations lead to the same conclusion: your valuable sales reps ought to focus on the right leads.
In practice, this means prioritizing the leads most likely to purchase soon. The other leads might be nurtured using traditional marketing automation systems until they become warmer.
How do you do this filtering and prioritization?
Traditionally, marketing automation systems allowed manually configured lead scoring algorithms to rate the leads versus each other. This is done by attaching credits to various activities. Recurring website visits and certain page views increased the lead score.
For example, it might be +1 for each page view, but visiting the pricing page gives +10, and a visit to the job opportunities page gives -15 (since the user’s interest isn’t in the product). Every PDF download may give +10, and watching a video on the website gives +5, and so on.
You get the point.
Various activities add or subtract to the lead score, and the total lead score gives a rating of how “hot” a particular lead is estimated to be. This is great in theory, but is subject to certain problems.
For example, the manually designed scoring formula might give the wrong perception of how likely a lead is to purchase. You may assume that certain types of page views are more or less important than they really are.
Manually designed scoring formula might give the wrong perception of how likely a lead is to purchase
You may think that downloading PDF documents is a stronger purchase signal than something else. Sometimes, these assumptions are wrong.
This is where machine learning and predictive lead scoring comes to the rescue.
With predictive lead scoring, a software algorithm compares the past behavior of those who later became customers with a particular new lead who is not yet a customer. In effect, the digital behaviors of a lead are compared to the behaviors of actual customers before they became customers.
The digital behaviors of a lead are compared to the behaviors of actual customers before they became customers.
Depending on the similarity in behavior, a lead score (usually in the range 0-100%) is calculated to assess how likely a new lead is to become a paying customer.
For leads passing the threshold of a lead score about 25%, a marketing automation system may send a sequence of nurturing emails with a soft sales message. Leads having a lead score over 50% may get emails with a harder sales message. Those with a lead score about 75% may be put in the queue for manual follow-up by a sales rep.
Several software tools perform predictive lead scoring like this. Many marketing automation systems and CRM systems, including HubSpot, Marketo, and SalesForce can do this too.
Infer (now acquired by Ignite Technologies) is one of the vendors who offer dedicated predictive lead scoring solutions, thus helping the sales team focus on opportunities with the highest probability of closing.
They use a combination of data gathering and machine learning to classify and rank leads and accounts on the likelihood that they will convert. I asked Rob Franks, Vice President of Professional Services at Ignite Technologies, how this happens.
He says, “Infer works by gathering data, called signals, about the lead from over 4000 different sources. The sources include syndicated data providers, proprietary data gathered from web crawling and the inputs and behaviors of the customer. Initial models are built by taking a history of data, classifying the signals and using win loss history to train a predictive model.”
In essence, the prediction algorithm learns what lead behaviors and attributes have led to a won or lost deal in the past, and uses that to assess new leads. Franks explains, “The models are reviewed and optimized in Infer’s proprietary workbench tool by experienced data analysts and scientists. When the model is ready, it is deployed to score all active leads and new leads as they are created. Scores can change as signals evolve so the models are periodically re-built with a more recent history of wins.”
Predictive leads scoring works well in combination with marketing automation workflows
I’ve used predictive lead scoring myself for a few years, and it is indeed a handy solution to filter out what are predicted to be low-quality leads. It is also great to use different lead scoring thresholds in marketing automation workflow trigger conditions.
As mentioned above, a marketing automation system may send different types of messages to clients with different leads scores.
Many other vendors offer predictive lead scoring systems, including Lattice, Maroon, and Mintigo, and several CRM system suppliers we discussed above. You will have to assess what system works best for your organization.
In my experience, predictive leads scoring works well in combination with marketing automation workflows. You can use lead scoring alone to prioritize leads for manual outreach or as a handover threshold from marketing to sales, but automated email sequences or other workflow logic makes this capability shine.
