5. The future of
lead scoring

Can AI and machine learning more accurately predict buyer intent?

What role will AI play in lead scoring?

AI and machine learning are transforming a huge range of marketing tasks and workflows—and lead scoring is no exception.

Large pipeline and commercial data sets can be used to train intelligent models capable of accurately predicting outcomes and performance. Those models will take a huge amount of the guesswork and ambiguity out of lead scoring, and push organizations towards a truly objective and data-based view of prospect intent.

But, just like human lead scoring, those models need the right data and engagement insight to draw meaningful and reliable conclusions about individual prospects and their propensity to buy.

lead scoring, and push organizations towards a truly objective and data-based view of prospect intent.

But, just like human lead scoring, those models need the right data and engagement insight to draw meaningful and reliable conclusions about individual prospects and their propensity to buy.

What’s preventing you from applying AI or machine learning to learn more about your prospects?

A lack of reliable engagement data
Limited budget for innovation
Low leadership buy-in for AI in general

How will AI change the status quo?

In a word, accuracy.

Most lead scoring models are built on assumptions around engagement types and their relevance as an intent signal. However, assumptions can often be incorrect and lead to poor outcomes.

Mature marketing functions typically review the performance of their scoring model each quarter and look to optimize it over time. But with AI, they’re going to have a much better and more timely view of what’s working in real time.

Optimization can become an always-on process, enabling continuous tweaks to improve engagement and conversions.

That visibility will also help marketers focus on the indicators and actions that drive revenue, rather than pure MQL volume. A scoring model like that has the potential to bridge the gap between sales and marketing, increase alignment between the two, and restore trust in the quality of MQLs.