Selling Smarter With Predictive Lead Scoring Algorithms

Priya Sareen

Sales and selling skills have been favored subjects for numerous books. But the last few years have seen the emergence of a new weapon in the seller’s armory – predictive lead scoring.

As described by McKinsey in the article “Unlocking the power of data in sales,” forward-thinking companies are using the growth of data analytics and artificial intelligence to expand the frontier of value creation for sales and are generating remarkable results in lead generation/scoring, people management, cross-selling, and pricing. McKinsey surveyed over 1,000 sales organizations on the use of analytics. These firms were categorized as fast and slow growers, and the results were clear.

Predictive lead scoring’s brick & mortar

Predictive lead scoring is a fusion of old meets new. Traditional lead scoring tools were always available for marketers to score their leads. The same tools, when combined with powerful, modern predictive modeling algorithms, give us game-changing insights. This is surely the most relevant sales tool in the hands of teams in this age of Big Data. The fuel for this powerful tool is the enormous data generated every day. And with this data lies an untapped opportunity to dice it like never before. Using algorithms to pick patterns that would likely be missed by the human eye provides a razor-sharp edge to the sales team.

Modeling around sales has been marred by subjectivity. Sales teams have erred repeatedly in their judgment on what qualifies a good lead vs. a bad one. Bad judgments produce highly scored leads with poor intent to buy. The 80/20 Rule – 80% of a company’s revenue will come from 20% of the customer base – applies here. So, selling smarter means knowing which customers are right for an organization. To deliver the most value, an organization’s products must align with the customer’s key needs during every phase. Also, the costliest 20% of the customer base, those with high maintenance and low revenue, should be regularly trimmed.

Traditional lead scoring is largely left to the subjectivity of marketers to list factors they view as relevant to making a sale. These factors are then assigned weights, which are used to score customers. The broad result is a priority chart for sales executives to follow while seeking new business. While there’s no doubt it’s a beneficial process, it has its own bag of shortcomings.

First and foremost, identifying relevant factors is open to human error. And choosing the wrong factors will lead a company to chase the wrong set of customers. Not only will this hurt business, but it is also detrimental to the morale of sales executives. In my opinion, nothing is worse for the morale of a sales team than chasing the wrong set of customers.

Predictive lead scoring is a better way to deal with these potential errors. At its heart are algorithms that use statistical techniques. These algorithms dice huge sets of data and look separately at successful and unsuccessful leads.

The objective is simple: To look at patterns and identify factors that are relevant to making a sale.

Another major benefit of predictive lead scoring is that it helps check that the relevant factors identified by the algorithms align to the larger business strategy. Sometimes these patterns can uncover customers who were not considered in the ideal set when you began. It helps organizations take a fresh look at their products and make them appealing to potential customers.

Needless to say, predictive lead scoring is most relevant for firms that have the ability to generate and store enormous sales data. Even so, it’s an important tool for all organizations and has relevence to organizations that have crossed the initial hurdle of acquiring their first few customers.

Stats speak louder than words

Extrapolating from IDC’s AI/CRM Economic Impact Survey of 1,028 organizations worldwide and 20 years of economic impact modeling, IDC calculates that AI associated with CRM activities will boost global business revenue from the beginning of 2017 to the end of 2021 by $1.1 trillion. The survey also found that 66% of the 1,028 respondents were implementing or considering implementing predictive scoring technologies as part of their sales process. Of the 292 current AI adopters surveyed by IDC, 83% reported that they used or plan to use sales and marketing predictive lead scoring.

I think in this new age, where data is generated every day at exponential levels, AI remains essential for smart selling. And techniques such as algorithm-based lead generation will help marketers deliver high-quality leads to their sales team, who can then work with greater efficacy in keeping the revenue engine up and running.

All great journeys start with small footsteps…

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About Priya Sareen

Part of the Digital Transformation Office at SAP, Priya Sareen is responsible for creating thought leadership and value content for the front office of an organization, catering to the needs of the Customer. Her area of expertise is Customer Engagement and Commerce (Sales and Commerce LoBs)