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How Predictive Lead Scoring Works in ABM?


Imagine if you could “predict” your next customer without qualifying rules and prioritizing leads? Predictive analytics is redefining sales and marketing by creating models that promise to find your next customer. Predictive lead scoring works by evaluating a set of indicators and building a model that predicts the likelihood of a lead to convert.

Predictive lead scoring is different from lead scoring. Lead scoring is a tool that marketers use to qualify leads based a defined criteria. The lead scoring model helps marketers to identify whether the lead makes sense for the business to pursue. A lead scoring model works by assigning values to each interaction in the buying cycle. Sales team usually pursues leads which are assigned higher scores using the model.

How Predictive Lead Scoring Model Works?

The predictive lead scoring model uses a deep learning algorithm to identify the likelihood of a lead to convert based on current and past data. The predictive model looks at what information your leads have in common and information your leads do not disclose in common. Based on its understanding data it will arrive at a model that buckets your leads helping you to identify the most qualified one.

The deep learning algorithm that powers predictive lead scoring model is also used for speech recognition, auto-tagged photos and also used in self-driving cars that use computer vision. To train the model, you need a large dataset which seemed impossible until recently.

Predictive Lead Scoring Works

Deep learning algorithms are trained using a sample dataset to identify patterns and arrive at a predictive model. The accuracy of the model depends on the amount of labeled dataset fed to the system. For instance, if you are training an algorithm to identify the image of a monkey it would require a large number of labeled images of monkeys with many different types of scenarios to arrive at a near accurate model. If you use incorrect labeled data, then you would arrive at an incorrect model. Google’s automated image labeling algorithm had its initial set of difficulties when it started labeling black people as ‘gorillas’ in its initial days.

Predictive lead scoring algorithm for account-based marketing would require uploading accounts that are labeled as “wins” or “losses.” The algorithm would look for future customers similar to how image labeling algorithm looks for similar images. Predictive model uses firmographic fit, company-level data, intent (using attribution data) and engagement to refine the model.

Why Predictive Lead Scoring Model Fails?

Predictive lead scoring model is a “black-box” without data. For a model to deliver a statistically accurate model, it needs at least 18 months of data. Also, the predictive scoring model is reactive because it only looks at the current and past data. As the world moves beyond CRM predictive lead scoring model might not be the best means to find opportunities in the future.

Also, the lack of data makes the predictive model rely heavily on firmographic data which offers insufficient context and provides a narrow view of your customer. Predictive analytics software providers tie-up with multiple third-party vendors to gather data, each using its proprietary model and assumptions to gather information.

Due to the shortfalls in the predictive model, marketers have now started evaluating prescriptive models. Prescriptive models can design nuanced, personalized rankings and recommendations. The prescriptive model moves beyond firmographic data and maps relationship between people and companies. The prescriptive model offers personalized recommendations for individual sales representatives and who they’re selling to. The model is proactive as it uses real-time data to prescribe your next customer.

These are early days of predictive lead scoring and companies with vast amounts of data are more likely to arrive at an accurate predictive lead scoring model. On another hand, you have start-ups like Node which are trying to make prescriptive a reality for marketers. Either way, it is important to understand the underlying principles behind both models to choose the right technology for your brand.

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