What is a propensity model, and why should marketers care?

What is a propensity model?

What makes a successful propensity model?

  1. Tailored to your business needs. Most canned, out-of-the-box models are trained with standard data that may not be applicable to your business needs. Even when trained using your data, canned models usually include only the data available in the vendor system and exclude other data sources you might have. In addition, many canned models are based on an outcome that may not apply to you, such as whether an account will lead to a created opportunity regardless of its chances of closing. If you care about closing deals, your propensity model should let you pick the relevant outcome.
  2. Transparency. You know how it’s built, and the results are readily accessible to the user. Often when a propensity score is used, sales and marketing executives are reluctant to commit resources because they don’t know how the propensity score was computed, the model is effectively a black box. When the training and workings of your propensity model are transparent, you can validate the model and get buy-in much more effectively.
  3. Outcome-focused, so it focuses on the outcome that applies to you. Many canned models will be based on an outcome that may or may not apply to you such as whether an account will lead to a created opportunity regardless of its chances of closing. What if what you care about is closing deals (as you should)? Your propensity model should let you pick the relevant outcome.
  4. Based on a modern tech stack. This allows your model to scale with your data and mesh well with your CRM system. A one-off propensity model developed in isolation will be useless unless you can put it in production and integrate it with your CRM system, providing timely propensity scores at scale in the system your sales reps use every day.

How is propensity different from intent?

How can we help?

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Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team

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Analyzr: Insights, Achieved

Analyzr: Insights, Achieved

Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team