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The Black Box of HubSpot's Predictive Lead Scoring

The Black Box of HubSpot's Predictive Lead Scoring The Black Box of HubSpot's Predictive Lead Scoring

New mobile apps to keep an eye on

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What new social media mobile apps are available in 2023?

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Use new social media apps as marketing funnels

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Try out Twitter Spaces or Clubhouse on iPhone

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What app are you currently experimenting on?

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HubSpot touts their predictive lead scoring as a key differentiator of their marketing platform. 

By applying machine learning algorithms to your HubSpot data, the company claims it can accurately predict which leads are most likely to convert into customers. 

However, there is one big issue with their model - it's a total black box.

Black Box Machine Learning Explained

What this means is that while HubSpot shows you the scores and predictions, they don't reveal anything about the weighting applied to each variable included in the predictive model. 

As they state in their documentation:

"HubSpot uses blackbox machine learning to provide predictions. With blackbox machine learning, the input and outputs of the model are known, but it is unknown how the input is transformed into the output. For lead scoring, this means it's not possible to know exactly how each input contributes to a contact's score."

This lack of transparency into the underlying model can be highly problematic for a few reasons:

1. Inability to Validate or Refine the Model

Without seeing the key variables and their respective weights, you have no way to truly understand if the model aligns with your business context or identify potential areas of improvement.

Essentially you need to take HubSpot's word for it that their one-size-fits-all model will work for your business.

2. Lack of Customization

You don't have any ability to specify the inputs that matter most to your sales process or business model.

Furthermore, you can't remove factors that may not be applicable or useful in predicting your customer conversions. The model is a fixed black box.

3. No Cross-Cloud Data Considerations

The model is limited to data within HubSpot only. 

Many businesses are joining together data from various sources (product analytics, 3rd party intent data, etc..) and would benefit enormously from having this cross-cloud data incorporated into lead scoring predictions. 

But HubSpot's walled garden approach prohibits this.

4. No Compounding Intelligence 

While machine learning should mean that predictive accuracy improves continuously over time, it's unclear whether HubSpot's model is really self-learning. 

There is no visibility into whether past predictions and outcomes are used to refine the algorithm.

Clearly other predictive scoring solutions on the market have advantages over the black box limitations of HubSpot's approach:

  1. Transparency into the significant variables and weights driving predictions
  2. Flexibility to add or remove factors to align with sales priorities  
  3. Incorporation of cross-cloud data for richer predictions
  4. Confirmation that the model compounds intelligence over time to increase accuracy

So while lead scoring is an important innovation, buyers should be aware of HubSpot's limitations compared to alternative solutions. 

No one wants their success metrics and forecasts to rely on a mysterious black box algorithm they can't control or even understand.

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