What Is Predictive Lead Scoring and Why Does it Matter?
One of the trickiest enigmas involved with operating just about any business is determining which sales leads to prioritize. Can big data and machine learning change the game for your team?
The practice of lead scoring is extremely useful to companies that engage in inbound marketing because is makes good sense logically that prospects who are more actively interacting with your content are those that more likely to close.
What Is Predictive Lead Scoring?
By associating various patterns of onsite visitor activity with sales-readiness, marketing teams can hand leads over to sales teams for more effective phone pitching. Lead scoring, therefore, increases sales conversion rates, since sales reps are able to reach out to only those who are ready to buy – in theory, at least.
The Flaws with Yesterday’s Lead Scoring Solutions
Lead scoring pitfalls come when sales and marketing don’t share or review one another’s data, and the criteria that make up a sales-ready lead remain static, at risk of obsolescence. It makes sense, though, that many companies would neglect upkeep of their paths to purchase and the point systems that drive their lead scoring logic. We get so caught up with nurturing and closing that it can be hard to shift focus to the cumbersome and overwhelming practices of funnel analysis and experimentation.
That’s why marketers are increasingly turning to big data, where enterprise-level computers capture and process massive amounts of recorded information, for help. The more factors that you can automatically correlate with closed deals, the more effective you can be at nurturing leads.
“Predictive lead scoring,” then, which throws big data – in this case, powerful scouring of third-party sources, machine learning and sophisticated predictive analytics – into the mix, represents the future of inbound marketing.
How Predictive Lead Scoring Works
Let’s take a look at a hypothetical scenario to illustrate the power of predictive lead scoring technology and insights. Without it, lead scoring requires that we engage in a whole lot of conjecture. For example, you may be under the impression that visits to specific content pages on your site, originating from specific geo-locations, company sizes and job titles are the parameters that are most likely to correlate with a sales conversion. In this case, the modeling overlooks too many factors for it to be reliable in any meaningful way.
When you add all the factors available through “big data” to the profile details that you already have for lead scoring, predictive solutions may reveal that companies from certain niche verticals are more likely to close with you than others – or companies with certain Twitter follower count ranges, or those headquartered next to interstate highways, or those that downloaded an obscure white paper of yours from two years ago, and so forth. You never know what these intelligence tools will reveal that can change the game for you.
Moreover, this method is not something you need to conquer in-house. There are SaaS tools already on the market that specialize in predictive lead scoring. All of these factors together explain why predictive is all the rage – and rightfully so.
How Predictive Lead Scoring Boosts Efficiency
When computers do the heavy lifting, as they’re required to do for this kind of info scraping and analysis, you’re free to focus on running other aspects of your company. Once you provide the key points of information, then you can go ahead and step aside.
Fundamentally, you’ll end up with more time at your disposal, while the crawlers and algorithms crank out the information you need so your marketing and sales teams can do their jobs better.
Leads who are less likely to eventually become customers should hardly be shunted aside, but predictive lead scoring can help you when a lead should be considered lower priority.
How Predictive Lead Scoring Enhances Accuracy
Before you had the possibility of enlisting big data in the service of your lead scoring, your point system was inherently subjective and limited in scope, and, therefore, subject to human error. You depended on your assumptions and the relatively small data sets that were at your fingertips. By turning to the computer’s cold, hard analysis of big data from heaps of sources, you benefit not only from a streamlined process but from one that eliminates much of the limitations of human-driven modeling.
Armed with predictive scores, you can move forward with pitching to your leads with confidence that they are truly qualified, as opposed to just relying on guesswork.
Concerned that your predictive analysis may become outdated? These systems include post-analysis tracking, so as to determine how effective the latest model is, and many also support manual adaptation, so you can make adjustments as you see fit. Be sure to update your model on occasion, and it will become an increasingly powerful investment over time.
By considering all of the business factors that may correlate with a lead worth pursuing, and then factoring in behavioral data that can have impact on whether a lead will convert, the app increases your savvy as you determine which leads are most sales-ready. By removing the guesswork from the analytics, you can be confident that you are approaching the best leads well.
Infer users boast 330% conversion rate growth and 230% growth per average sale size, and the Infer web app has a reputation for being among the most accessible and intuitive predictive platforms.
Moving Forward with Predictive Lead Scoring
By integrating with Infer, HubSpot takes your interests in growing your company seriously and is always invested in developing the tools that will advance and enhance your marketing. No wonder HubSpot has emerged in recent years as the go-to platform for marketers.
Predictive lead scoring helps you figure out which leads to prioritize when you move forward towards them. For that matter, predictive lead scoring can even help you retain your current customers, as it helps you identify the trends that might be impacting them as well.
It also can result in improved relations between your sales team and your marketing team. By having your data analyzed objectively, the course of action is not dependent on the whimsy of either department. Both teams can work more productively using the information that points to the best leads to pursue. Best of all, the results of these efforts involve sustained returns and put both teams in position to perform at their best.
A content and social media marketing specialist, Ben Jacobson joined the Lean Labs team in the summer of 2014. Ben has been active as a digital branding professional since the early days of social media, having overseen projects for brands including MTV, National Geographic, Zagat and Wix. His writing has appeared in Social Media Explorer, Search Engine Journal, Techwyse and the Mad Mimi Blog. Ben resides just south of the Carmel Mountain ridge in Israel with his dashing wife and two sprightly descendants.