August 20, 2015
In the time traveling film “Back to the Future II,” the villains (Biff and his grandson Griff) steal a time machine and go back in time to 1955. Biff then uses his knowledge of the future to become wealthy by making predictions.
Although, to my knowledge, there are no marketers driving around in flux capacitor-equipped Deloreans, using their miraculous knowledge of the future to gain an edge, it’s beginning to feel that way — especially if you’ve been tracking the remarkable evolution of predictive lead scoring.
A Quick Rundown of Predictive Lead Scoring
Although there is no such thing as a flux capacitor (yet), there are some serious SPAs (Smarty Pants Applications) out there that are beginning to make all of our science fiction movies feel more like reality.
For example, we now have analytics applications that suck up data from your marketing automation and CRM, combine that internal data with an analysis of external information that it scans: i.e. social media profiles of customers, receipts from their trash, and the contents of their car’s glove compartments (okay, just kidding about those last two…I hope).
But that’s not all. These applications, with their red-glowing, all-seeing eyes (sort of a combination of Sauron, Skynet, and Santa Claus), also scour third-party data sources online.
And then they combine everything above and magically score and rank those businesses according to how likely they are to buy from you. (At least that is the official story that companies are telling the public. I still suspect Biff.)
The Good: It Helps You Make Better Use of Your Time and Resources
In truth, the general concept behind predictive lead scoring is not new. Companies have, in one way or another, tried to predict these things all along. But the difference here is volume and agility. We can now access far more data than ever before and actually do something useful and specific with all of it; and we can do it quickly. If used properly, we can see needle-in-a-haystack buying signals that would’ve gotten lost before. VentureBeat.com put it this way: “That type of [analytical] framework provides an objective way to compare intent data with other lead sources or list buys.”
And recent statistics show why predictive lead scoring has been a good thing for marketing. David Dodd summed up nicely in his June 2015 article about B2B marketing technology, SiriusDecisions has published an illuminating report about this trend:
- In 2014, there were nearly 14 times more B2B companies using predictive lead scoring than there were in early 2011.
- 78% of the companies using predictive lead scoring are in the high tech industry.
- Over half (56%) of current users have annual revenues of $50 million or less.
- Nine out of ten current users say that predictive lead scoring provides more value than traditional lead scoring.
And in another great piece on the topic, Jon Bara of LinkedIn’s Marketing Blog explains the utility of predictive lead scoring this way:
[It] is based on a scientifically proven and sound statistical modeling approach. The result is a score (typically from 0 to 100) that signals the likelihood of turning a lead into a customer. The score is based on a deep, data-driven analysis of how closely each lead resembles an existing set of customers.
But is it really that simple? Is it guaranteed to give every company results?
The Bad: It Is Not Something to Rush Into if You’re Unprepared
When adopting new technology, it’s far more than just getting some shiny new toys and teaching people how to press the buttons, as if you’ve just installed a new printer. The key is to have a clear vision of how your company’s operations will look when the technology is working well and to understand exactly what it will take to achieve that ideal result.
You need to work backwards, in other words.
In a July 16, 2015 VentureBeat.com article, Vik Singh and Jamie Grenney described it this way:
If you’re considering external intent data, the most common mistake you can make is to jump in without defining clear use cases or your ideal result…it is important to start with your criteria for measuring success and work backwards…it’s just a question of how many leads you can generate, how much sales effort it takes, and ultimately whether it’s worth the cost per good lead.
In some cases, it’s just premature for companies to adopt predictive lead scoring because they don’t have enough data coming in. As David Dodd pointed out: “if you don’t have enough good CRM/marketing automation data to work with, the value of predictive lead scoring will be more problematic.”
The Creepy: It Does Carry Privacy Concerns
With the NSA revelations and Snowden leaks in recent years, many people frown on privacy invasion, especially when it feels like companies are stalking them. In Singh and Jamie Grenney’s piece on predictive lead scored, they outlined the details of how deep predictives go to get their information:
External Intent Data (also referred to as third-party data) is collected by publisher networks either at the IP level, or through user registration and shared cookies. These sites track the articles a user reads, content they download, their site searches, and potentially even comments they leave…many publishers are pushing the boundaries of privacy and finding new ways to monetize their traffic.
On the other hand, if I believe I offer a valuable service that can help companies succeed in their mission, I shouldn’t have to apologize for trying to connect my service or product with people who actually need it — especially if I’m presenting well-crafted advertising done in good taste with a genuine human-to-human feel to it.
Don’t Forget The Basics
Even though predictive lead scoring and analytics can give you a sterile and data-rich view of your leads and audience, it doesn’t go deeper into the psyche of your potential customers. And this is still an important factor in marketing and sales. To ensure you know what drives your customers and causes them to take actions, you should still perform the old-school tactics of buyer persona building and customer journey mapping. This causes you and your team to really explore the deeper levels of your targets and understand them at a emotional and psychological level.
When you’re able to combine the tech driven data of predictive lead scoring with the psychological mapping of your customers, you’ll find that your efficacy in sales and marketing increase significantly.
With the allure of automation and machine learning tempting us towards a fully hands-off approach to most business actions, we must also remember that we’re still in the infancy of much of this technology. Because of that, we should temper our dependence on the tech and still maintain a foothold in the past until we’re certain the future can completely fulfill on its promises.
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