How & Why to Use Propensity Models to Predict Customer Behavior?

To accurately predict customer behavior, data-driven organizations are increasingly turning to propensity models that help them identify which customers are most likely to take the desired action, such as making a purchase or signing up for a service.

While there are a variety of factors that can be used to build a propensity model, some of the most important include customer demographics, past behavior, and current circumstances. How to use propensity models to make better-informed decisions about marketing, sales, and other customer-facing activities?

What is propensity modeling?

Propensity modeling is a method of statistical analysis that is used to predict the likelihood of certain outcomes. It’s often applied in marketing and advertising to estimate the probability of a customer performing specific activities such as purchasing a product, signing up for a newsletter, and more.

Why is it important?

Creating propensity models allows organizations of all sizes to:

• predict the customer interest and readiness to buy – this makes it possible to allocate marketing resources more efficiently by targeting those who are more likely to make a purchase.
• increase qualified leads – by targeting those who are more likely to take the desired action, marketers can increase the number of leads that are qualified and likely to convert.
• shorten sell cycles and boost close rates – thanks to understanding customers, organizations can prioritize and focus their selling efforts on those who are likely to finalize transactions. This results in shorter sell cycles and higher close rates.
• focus marketing resources on active buyers – creating propensity models is an easy way to identify which customers are in the market for a product or service. Thanks to it, organizations can precisely target their marketing activities, instead of wasting time and money on efforts that aren’t likely to bring the desired results.
• reduce churn and boost cross-selling – by analyzing which customers are at risk of churning, organizations can work to retain them before they cancel their subscriptions, stop using services or buy products. Moreover, propensity models can be used to identify who is potentially interested in additional products or services, making it possible to boost cross-selling efforts.

How to use propensity models to get customer signals?

Customer signals are behaviors that indicate that a person is interested in purchasing a product or service.

A properly performed customer propensity analysis is a great way to get customer signals by:

• identifying the factors that influence customer behaviors,
• measuring the strength of those influences,
• predicting how likely a customer is to exhibit a certain behavior, and creating targeted marketing campaigns that encourage people to finalize transactions.

The takeaway

Organizations that want to accurately predict customer behavior and make better-informed decisions about marketing and sales should consider using propensity modeling.

When created correctly, propensity models can help businesses focus their resources on active buyers, reduce churn, and boost cross-selling.