Lead qualification is the process that marketing and sales teams use to screen prospects before sending them to sales reps for further action. In theory, qualified leads are the prospects who are most likely to become customers and who best fit the company’s ideal buyer profile. The goal is to maximize sales efficiency by ensuring that salespeople only devote time to leads they can win.
The reality? Lead qualification as it’s traditionally done is error-prone and haphazard. It’s based mostly on gut instincts, simple formulas and limited prospect data. It’s better than nothing, but far from the pinpoint guidance sales needs to cut through the uncertainty and focus on the best opportunities.
The problem isn’t a lack of information to go on. In today’s digital world, there’s a mountain of data on potential buyers, their interests and purchasing behavior – data that can reveal exactly what sales teams most want to know. The trouble is that no human brain (or brains) could process it all to extract actionable insights.
Fortunately, machine learning and AI have advanced to the point where there’s no longer any need to qualify leads using old-fashioned manual sorting and guesswork. A new generation of intelligent software, led by Infer, can analyze all of the data to evaluate prospects, gauge their sales potential and calculate precise lead scores. The process is fast, automated (no manual labor involved) and most of all, highly accurate. In fact, it can reliably predict the likelihood that any given prospect will make a purchase, and when. Hence the name predictive lead scoring.
Lead Qualification And Lead Scoring
Lead qualification has several key steps. First, marketing organizations sort through leads produced by demand gen programs, B2B lists and other sources, looking for prospects who’ve shown interest in the company’s offerings – for instance, by responding to an email drip campaign, or downloading some content from the company website. These prospects are deemed Marketing Qualified Leads (MQL) and are passed on to the sales team for further research and vetting.
The reality? Lead qualification as it’s traditionally done is error-prone and haphazard. It’s based mostly on gut instincts, simple formulas and limited prospect data. It’s better than nothing, but far from the pinpoint guidance sales needs to cut through the uncertainty and focus on the best opportunities.
The problem isn’t a lack of information to go on. In today’s digital world, there’s a mountain of data on potential buyers, their interests and purchasing behavior – data that can reveal exactly what sales teams most want to know. The trouble is that no human brain (or brains) could process it all to extract actionable insights.
Fortunately, machine learning and AI have advanced to the point where there’s no longer any need to qualify leads using old-fashioned manual sorting and guesswork. A new generation of intelligent software, led by Infer, can analyze all of the data to evaluate prospects, gauge their sales potential and calculate precise lead scores. The process is fast, automated (no manual labor involved) and most of all, highly accurate. In fact, it can reliably predict the likelihood that any given prospect will make a purchase, and when. Hence the name predictive lead scoring.
Lead Qualification And Lead Scoring
Lead qualification has several key steps. First, marketing organizations sort through leads produced by demand gen programs, B2B lists and other sources, looking for prospects who’ve shown interest in the company’s offerings – for instance, by responding to an email drip campaign, or downloading some content from the company website. These prospects are deemed Marketing Qualified Leads (MQL) and are passed on to the sales team for further research and vetting.
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