How AI Can Help Enhance Your Lead Scoring Model

Right, so I asked my AI colleague to help me with this complex lead scoring model I’ve been trying to understand.
Basically, this model looks at a bunch of different factors, like the lead’s job title, function, seniority, industry,
and engagement with specific websites or emails. Each factor has a weight assigned to it. This allows the model
to come up with a score that shows how well they match up with all of the criteria. The idea is to identify which
leads are most likely to become customers, so the sales team can focus their efforts and close more deals.
You could say it’s like a crystal ball for sales, but without the swirly smoke and spooky music.

Initially, I made sure that I gave Chat GPT 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 create documentation of the model’s criteria manually. To make it happen, I recorded the profile and engagement criteria used by taking notes of each criterion’s name, weight, and other relevant details.

Example Profile criteria: 

Example Engagement criteria:

I’ve got to say, I’m really impressed by how easily ChatGPT was able to decode that Lead Scoring Model.
I am 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 tome in a way that was easy to understand.

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 and ask some serious questions on the scope of AI’s support:

• 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?

Prompt: 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 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. AI 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 an essential factor and 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 an example below:

Prompt: Could you provide me with two contact records that both received an A1 score, but have different values for their
Job Title, Job Function, Job Seniority, or Industry fields? 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.

That’s truly a valuable and insightful tip that holds significant importance! Why? John’s job title and function might
not tell us 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 in order to enhance this
Lead Scoring Model:

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

1. Behavioral data: Consider gathering 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.