Propensity Models

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Unlock the power of data with propensity models, for more informed decision-making!

Propensity models can forecast customer behavior, enabling you to make data-driven marketing, sales, and product development.

Propensity models are statistical models that predict the likelihood of a customer taking a certain action, such as making a purchase, responding positively to a campaign or churning. These models can be used to improve business performance in a number of ways, including:

  • Acquiring new customers: By identifying customers who are more likely to convert, businesses can focus their marketing efforts on the right people.
  • Retaining existing customers: By understanding what factors lead customers to churn and turn to competitors, businesses can take steps to reduce churn rates (attrition or churn models)
  • Cross-selling and upselling: Propensity models can be used to identify customers who are likely to be interested in additional products or services (conversion models)
  • Customer segmentation: Propensity models can be used to segment customers into groups based on their likelihood of taking certain actions. This information can be used to create targeted marketing campaigns.
  • Personalizing the customer experience: By using propensity models to segment customers, businesses can create more tailored and relevant offers and promotions.
  • Risk assessment: Propensity models can be also used as inputs to risk scores (e.g. credit risk, etc.).
  • Cost Efficiency: Maximize your marketing budget by targeting only those prospects with a high likelihood of responding positively. This leads to cost savings and a more efficient allocation of resources.
  • Competitive Advantage: Stay ahead of the competition by leveraging data-driven insights that your rivals may not be utilizing to the same extent.

Our approach is a unique blend of advanced analytics, machine learning, and deep expertise in implementable solutions, tailored to your specific needs, as it includes:

  • Methodology design based on review of requirements, processes, data definition, data availability and implementation environment.
  • Data review, management and preparation
  • Creation of quality definition and development sample
  • Examine all the potential variables that will be considered under the development process (systemic variables and derived/transformed ones, dynamic vs. static, etc.)
  • Model development (use of Machine Learning algorithms or more conventional approaches)
  • Model validation in independent sample – as part of the development (if possible)
  • Full Documentation on model development, including probability tables (strategy tables)
  • Support in model implementation


With StatDec’s propensity models, you will improve your customer targeting, and be able to optimize your marketing and sales strategies.