Lead Scoring Grows Up: How AI Helps Marketers Predict Conversions with Accuracy

You’ve built a crackerjack marketing team, installed best-in-class marketing platforms, and designed super-creative campaigns with compelling messaging and offers. You hope.

In fact, you’re not sure, and you’re crossing your fingers that all the time and effort you’ve put into content developing and planning will pay off. From past experience, you know demand generation is hit or miss, and anticipating your audience’s reaction to a campaign or message is difficult to predict with accuracy.

Fortunately, advances in AI are changing all that. According to IDC’s AI/CRM Economic Impact Survey, AI associated with CRM activities will boost global business revenue from the beginning of 2017 to the end of 2021 by $1.1 trillion. Of the AI adopters surveyed, 83% are using or planning to use sales and marketing predictive lead scoring -- the practice of attributing a numerical score to leads based on certain pre-defined factors and behaviors.

Made possible by AI-powered propensity-to-buy models, predictive lead scoring is helping marketing teams understand which leads are most likely to buy specific products or respond to specific messages.

The Evolution of Traditional Lead Scoring

Marketers have been using lead scoring for a couple of decades. With lead scoring, marketers ascribe a certain number of points to a lead for clicking on two or more pages on your website, or downloading a marketing asset such as an ebook, or attending an event like a webcast. As the points add up for that lead, the lead is considered more qualified and likely to purchase a product or service than other leads. Teams can identify stages, such as Marketing-qualified or Sales-qualified, for instance, and when a lead reaches a certain agreed-upon threshold (e.g. 25 points), it progresses to the next stage in the marketing funnel, triggering additional nurturing activities.

Lead scoring helps marketing teams prioritize their time and effort to maximize the ROI of their activities and increase lead conversions by only focusing on leads that are likely to convert. But only in recent years has AI techniques like machine learning enabled predictive lead scoring with a higher level of accuracy.

Before AI-powered propensity modeling was possible, teams would manually assign a score to each lead based on a subjective evaluation of what a certain action might mean. For example, a marketer would decide that downloading an ebook was more telling than reading a blog post and ascribe a higher value to that action. They might also factor in other known data, such as age, gender, job title and past interactions.

Although traditional lead scoring methods enabled teams to weed out weak leads, it wasn’t great at identifying the best leads. Scoring was subjective and arbitrary, and not rooted in data science. It was also very time-consuming, involving a lot of research and spreadsheets.

CRM and marketing automation (MA) platforms later helped teams enrich the amount and quality of data about prospects and customers, and they also automated the scoring process. MA platforms such as Marketo and Eloqua made it possible to track people’s online behavior and collect certain types of implicit information, which could then be analyzed in concert with explicit data such as demographics and downloads.

The results were promising. MarketingSherpa wrote about one organization who used rudimentary lead scoring in 2012 and saw a 79% increase in lead conversion. However, without AI, marketers still needed to spend time researching leads before being able to eliminate ones less likely to convert.

AI Super-Charges Lead Scoring

Propensity-to-buy modeling enables marketers to predict with higher accuracy what actions prospects are most likely to take in response to marketing outreach.

Propensity-to-buy models leverage machine learning algorithms to spot trends and patterns in datasets that would be impossible to see with the human eye. They analyze data from CRM systems, MA platforms, social media commentary and other sources together, for richer, more complete insight into an individual’s buying behavior. And as the name implies, machine-learning algorithms will learn and adjust results based on new data as it enters the system, which enables predictions to become increasingly accurate over time.

Because they’re data-driven and statistical, propensity-to-buy models take into account both implicit and explicit information and remove the subjectivity from traditional lead scoring methods, enabling a broader set of predictions. They can identify shared traits among leads who already converted and those that haven’t yet, as well as shared traits among customers who purchased certain products or services, when they decided to purchase, and so on. They can even predict whether a customer will unsubscribe from an email list or stop using a product or service altogether.

Armed with knowledge about how likely someone is to take a certain action, marketing teams can focus resources on specific leads, and develop content and messaging that will make engaging with those leads much more meaningful and fruitful.

Score Leads with Precision with 2predict

Using an AI-powered lead scoring model can help you ensure your sales team is spending the majority of their time nurturing the prospects that are most likely to become customers, while eliminating time-consuming manual tasks and guesswork involved in developing targeted campaigns and content. But an off-the-shelf lead scoring or predictive analytics solution may fall short of your expectations. That’s because every marketing organization -- and every target audience -- is unique. A custom propensity-to-buy model can often yield more accurate results.