How AI Can Help Enhance Your Lead Scoring Model

As marketing professionals, we all understand the importance of lead scoring in identifying high-quality leads and focusing our efforts on the right prospects. However, the traditional approach of manual lead scoring can be time-consuming and error-prone. Thanks to AI, we can now leverage machine learning algorithms to enhance our lead scoring model and improve its accuracy. In this blog post, we will explore how AI can help interpret and enhance a lead scoring model, and we will share our insights and experiences on this topic. 

Let’s dive in!

Alright, so, I asked my AI buddy to help me out with this crazy complicated lead scoring model I’m trying to wrap my head around. Basically, this model takes into account a bunch of different factors, like a lead’s job title, function, seniority, industry, and engagement with specific websites and emails. Each factor gets a weight, and then the model spits out a score for the lead based on how well they match up with all of the criteria. The whole point is to figure out which leads are most likely to become customers, so the sales team can focus their efforts and close more deals. It’s like a crystal ball for sales, but without the swirly smoke and spooky music. 

How AI and machine learning can assist in interpreting and enhancing Lead Scoring models. 

Initially, I ensured that I gave ChatGPT a complete rundown of my Lead Scoring Model and requested a brief overview. Since I was unable to export the scoring model definition, I had to manually create a documentation of the model’s criteria. To do this, I made sure to record the profile and engagement criteria used by taking note of each criteria’s name, weight, and other relevant details.

Example Profile criteria: 

Example Engagement criteria:

I gotta say, I’m really impressed by how easily ChatGPT was able to decode that Lead Scoring Model. I’m no expert in machine learning, but it seemed like a pretty complex set of rules to me. Nevertheless, ChatGPT took it all in stride and was able to explain it all to me in a way that was easy to understand.

I proceeded to evaluate the AI’s ability to interpret the Lead Scoring Model by requesting an example lead that met the specified criteria, and it performed exceptionally well.

Prompt: Could you provide me with a sample contact record that meets the profile and engagement criteria for an A1 lead score, and briefly explain why this specific contact received an A1 score?

I carried out further testing and assessment of the Lead Scoring Model’s AI interpretation capability by requesting additional records with varying lead scores, and the outcomes were positive.

Prompt: Can you please fetch more records with varying Lead Scores (including also, C1 and D4 scores) but containing the same information as in the previous message?

I’m pretty sure AI has understood my Lead Scoring model accurately. Now, let’s get to the point.

• Can AI suggest any tweaks to the Lead Scoring model based on the current profile and engagement criteria?
• Can it flag any potential issues with the current setup or weaknesses in the model?
• Could the AI provide a detailed explanation for any recommendations it may have? 
• Should I be concerned about scoring contacts based on their Job Title, Job Function, Job Seniority, or Industry field values?

I’d appreciate any tips on improving the model.

ChatGPT: Based on the current profile and engagement criteria, here are some suggestions for updating the Lead Scoring model:

• Review the weight assigned to each activity: Review the weight assigned to each activity in the engagement scoring model to ensure that it reflects the most valuable actions taken by prospects. It is possible that some actions that were considered important in the past may no longer be relevant, or vice versa.
• Incorporate new engagement metrics: Consider including new engagement metrics, such as social media engagement or website behavior, that may provide valuable insight into a prospect’s level of interest and engagement.
• Refine the segmentation criteria: Review the segmentation criteria and ensure that they are aligned with the target audience. Consider refining the segmentation criteria to better target specific personas or industries.
• Improve data quality: Review the quality of data used to inform the Lead Scoring model. Poor data quality can lead to inaccurate scores, which can negatively impact the effectiveness of the model.
• Evaluate scoring models regularly: Regularly evaluate the effectiveness of the scoring models to ensure that they are still relevant and effective. This will help to identify any areas that may require adjustment or improvement.

As for potential issues with the current configuration, one possible issue is the reliance on demographic data, such as job title, function, and industry, as these data points may not always be accurate or up-to-date. Additionally, it is possible that certain data points may not be applicable or relevant to all prospects, which could result in a skewed score.

In terms of weaknesses, the model may not be able to capture all factors that influence a prospect’s behavior, such as external events or changes in the competitive landscape. The model also assumes that all prospects behave similarly and may not account for individual differences in behavior or preferences.

There are also risks associated with scoring contacts based on their job title, function, seniority, or industry field values. For example, a prospect’s job title or function may not accurately reflect their level of authority or decision-making power. Additionally, some industries may have unique characteristics or trends that are not reflected in the model, which could lead to inaccurate scores. – That’s the essential factor, that’s an insight that genuinely matters!

Relying solely on job title, function, seniority, or industry field values to score contacts carries a certain level of risk. These factors may not always provide an accurate depiction of a prospect’s decision-making authority or unique industry characteristics. As a result, the lead scores generated from this method may be inaccurate, leading to missed opportunities or wasted resources.

See the example below:

Prompt: Could you provide me with two contact records that both received an A1 score, but with different values for each record’s Job Title, Job Function, Job Seniority, or Industry field? I would like to demonstrate that solely relying on these values to score contacts may not always be reliable, as they may not accurately reflect a contact’s current interests.

Prompt: Could you give me a contact record that may have been scored inaccurately, according to the given profile and engagement criteria, and provide an explanation for why this might be the case?

That’s truly valuable advice, an insightful tip that holds significant importance! Why? John’s job title and function might not tell the whole story about his interests and responsibilities. As an administrative assistant, he could be a secret purchasing ninja or a decision-making wizard. And while he’s into education, who knows, maybe he’s currently daydreaming about becoming a professional skydiver instead.

Ok, let’s find out if AI could provide some additional suggestions on what I could search for to enhance this Lead Scoring Model:

ChatGPT: Here are some additional suggestions to enhance your Lead Scoring Model configuration:

1. Behavioral data: Consider incorporating behavioral data such as website interactions, email opens, clicks, and downloads, and incorporate them into the scoring model. This data can provide insight into a contact’s level of interest and engagement with your content.
2. Demographic data: While demographic data can be a valuable indicator of a contact’s potential interest in your product or service, it should not be relied on solely for lead scoring. Consider supplementing demographic data with other factors such as behavioral data or firmographic data to provide a more accurate picture of a contact’s level of engagement.
3. Account-based data: If you are using an account-based marketing (ABM) approach, consider incorporating account-based data such as account size, revenue, and industry into your scoring model. This data can help you prioritize high-value accounts and contacts that are more likely to result in a successful sale.
4. Lead source data: Consider incorporating lead source data into your scoring model to identify the sources that generate the highest quality leads. This data can help you optimize your marketing efforts to focus on the most effective lead generation channels.
5. Scoring calibration: Regularly review and recalibrate your scoring model to ensure it accurately reflects your target audience and lead generation goals. This can help you identify any biases or inconsistencies in the model and make necessary adjustments to improve its accuracy.

Conclusion

In conclusion, AI is a powerful tool that can help marketers interpret and optimize their lead scoring models, leading to improved accuracy and efficiency. AI can be particularly useful for Quality Assurance (QA) testing during the setup process, detecting inconsistencies and ensuring all relevant criteria are included. By analyzing large amounts of data, AI algorithms can identify critical data points and help adjust the lead scoring model accordingly. Additionally, by analyzing historical data and identifying patterns and trends, marketers can fine-tune their lead scoring models to better reflect changing market conditions and lead behavior.

However, it’s essential to be aware of the potential risks associated with relying solely on AI to evaluate leads. Therefore, we recommend that marketing professionals use AI in conjunction with human expertise and judgment to ensure the most accurate and effective lead scoring model possible. With this approach, we can leverage the power of AI to enhance our lead scoring model and make better decisions when evaluating and ranking our leads.

We’d love to hear your thoughts on this post and the topics we covered. Did you find ChatGPT’s capabilities impressive? Have you had any experience using ChatGPT or other AI tools in marketing automation? 

If you’re interested in exploring this topic more deeply, be sure to check out our upcoming blog posts on AI and its capabilities for the Marketing Automation industry. In our next posts, we’ll continue the interrogation of ChatGPT in various areas of marketing automation. 

Stay tuned for more content from us!