In our third installment of the five-part series "AI for Newcomers," we build on the topics of AI terminology and how to learn AI with a practical application that’s transforming Account-Based Marketing (ABM): Auto ML-driven lead scoring. In this example, we use HubSpot.
Challenges in Modern ABM Campaigns
Running ABM campaigns in HubSpot offers a wealth of data. However, traditional scoring methods often fail to identify valuable opportunities. Accurately predicting which leads are primed for a sales conversation requires more insight.
What’s the difference between lead scoring and engagement scoring?
Traditional engagement scoring tracks how leads interact with emails and content with open and click rates. This approach is superficial at best and outdated at worst because it only scratches the surface of a prospect’s potential interest.
The lead scoring model goes deeper. It blends engagement signals with richer insights like demographics and detailed website behaviors.
Understanding Lead Scoring Mechanics
Auto ML models utilize data to calculate a lead score by predicting the likelihood of each lead's conversion. Analyzing patterns in lead behavior is what makes this possible.
The lead score is a numerical representation, often scaled between 0 to 1, where a score closer to 1 indicates a higher probability of conversion. This scoring allows sales teams to prioritize leads effectively so that their time is spent focusing on engaging leads with the highest likelihood of becoming real deals.
Here is a code snippet to illustrate the scoring process:
Now let’s cover how to explore Auto ML
Coding Auto ML-Driven Lead Scoring
Data Layer
The first step is to extract comprehensive data from HubSpot. This includes engagement metrics such as open rates, click-through rates, and crucially, detailed website visit data. This rich dataset forms the foundation for our Auto ML model.
Here is a code snippet for extracting data from HubSpot:
Analysis Layer
Now that we have this data, we turn our attention to Auto ML to develop a lead scoring model capable of identifying leads most likely to advance in the sales funnel based on their engagement patterns.
Here is a code snippet for setting up the Auto ML Model:
User Layer
With the model trained, we apply it to new leads, enriching HubSpot's data with predictive scores to help your sales team prioritize leads with the highest potential for engagement. We integrate predictions into HubSpot to enhance lead profiles and tag leads with scores for follow-up by marketing or sales.
Here is a code snippet for updating leads in HubSpot:
Using Scores for Strategic Engagement
We integrate these predictive scores back into HubSpot so that you can segment leads for personalized outreach. High-scoring leads receive specific content and offers that match their demonstrated interests, which increases the probability of conversion significantly. Lower-scoring contacts require more nurturing.
Wrap Up
Incorporating Auto ML into a tool like HubSpot for ABM campaigns can transform how you work with leads. Marketing becomes more strategic. Sales does too. You are taking a big step towards realizing massive efficiency gains that AI makes possible.
“AI Ethics and Bias Mitigation” is our topic for next week. We will discuss ethical considerations in AI and how biases can inadvertently be introduced to AI models.
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