What is Propensity Model? A Complete Guide

Understanding your customers is no longer a luxury; it is a necessity. Businesses always look for ways to gain an edge, and one powerful tool is the propensity model. These predictive models go beyond guessing, using machine learning to study large amounts of past data and predict future actions.

What is a Propensity Model?

A propensity model is a type of predictive model that uses a machine learning algorithm to calculate the likelihood of a specific event occurring. In business, this event is almost always related to customer behavior.

The "propensity" refers to the inclination or tendency of an individual to take a certain action, such as making a purchase, churning (leaving a service), or clicking on an ad. The model works by analyzing a wide range of data input, including past transactions, browsing history, demographic information, and more. 

How Does a Propensity Model Work?

Creating a propensity model is a multi-step process that transforms raw data into a powerful predictive tool. It's not just a single action but a carefully executed sequence of steps that ensures accuracy and effectiveness.

1. Data Collection and Preparation

The journey starts with data collection. A model is only as good as the data it uses. Businesses must gather relevant historical data from different sources, such as CRM systems, website analytics (like Google Analytics), and transaction records. The data should be complete, covering details like past purchases, browsing history, and demographic information.

With Leat.com, all this data is unified through our 40+ integrations, including POS systems, so you can see all your customer information in one central dashboard. By unifying and preparing your data with Leat, you ensure it is reliable and ready for the next steps, laying a solid foundation for a successful model.

2. Feature Selection and Engineering

After the data is clean, the next step is feature selection and engineering. "Features" are the specific variables or details the model uses to make predictions. For a propensity-to-purchase model, features could include the number of website visits, time spent on a product page, or the value of past purchases.

Feature selection means picking the most important variables that affect the outcome. Feature engineering is about creating new variables from existing data. For example, you could combine a customer's age and location to make a new "customer segment" feature. This helps the machine learning algorithm see patterns and relationships in the data more clearly, improving the model's accuracy.

3. Choosing the Right Algorithm

Choosing the right machine learning algorithm is an important decision. The choice depends on the problem and the type of data. A common option for a propensity model is logistic regression, which works well for predicting a yes-or-no outcome (buy or not buy).

Other options include gradient boosting machines or random forests, which can handle more complex patterns. The right algorithm will use the data efficiently and provide the most accurate predictions.

4. Model Training and Validation

With the data and algorithm ready, the next step is model training and validation. The cleaned data is split into two sets: a training set and a validation set. The model "learns" from the training set, finding patterns and connections between the features and the outcome.

After training, the model is tested on the validation set. This data was not used in training, so it gives an unbiased measure of the model’s accuracy. This step is crucial to ensure the model is not "overfitted" to the training data. A strong validation process ensures the model works well on new, unseen data, giving businesses confidence in its predictions.

5. Deployment and Monitoring

The final stage is deployment and monitoring. Once validated, the predictive models are added to business systems. This allows for real-time predictions. For example, a marketing platform can use the model to decide which customers to target in a campaign.

But the work does not stop there. Customer behavior and market trends change over time. The model’s performance must be watched continuously. Regularly updating the model with new data keeps it accurate and relevant. This ongoing monitoring is key to keeping marketing campaigns effective and improving customer retention over the long term.

Types of Propensity Models

Propensity models are highly versatile and can be tailored to predict a wide range of customer behavior. Businesses use different types of these models to address specific challenges and capitalize on distinct opportunities. Each model is designed to answer a unique, critical business question, helping to optimize various marketing efforts and operational strategies.

1. Propensity to Buy Models

This is one of the most common types of propensity models. A propensity to purchase model predicts the likelihood that a customer will buy a specific product or service. These models analyze factors like a customer's past purchases, browsing history, and engagement with marketing campaigns.

By identifying customers with a high propensity to buy, businesses can create highly targeted promotions, product recommendations, and personalized offers. This approach directly boosts conversion rates and sales, ensuring marketing budgets are spent on the most promising leads.

2. Propensity to Churn Models

Improving customer retention is a top priority for most businesses. Propensity to churn models are designed to identify customers who are at risk of leaving or canceling a service. These models analyze data such as a decrease in activity, customer service interactions, or changes in subscription usage.

By flagging high-risk customers, companies can proactively intervene with targeted retention offers or personalized support. This not only reduces customer churn but also enhances the overall customer experience and long-term loyalty.

3. Propensity to Upgrade Models

For subscription-based businesses, upselling is a key growth driver. Propensity to upgrade models predict which customers are most likely to move to a higher-tier plan or purchase an add-on. The model considers factors like current usage patterns, features utilized, and time since the last upgrade.

This allows businesses to present the right upgrade offer to the right customer at the right time, maximizing revenue from their existing customer base. It's about smart, timely upselling that feels helpful rather than pushy.

4. Propensity to Respond Models

In direct marketing, knowing who will engage with a message is vital. Propensity to respond models forecast the likelihood of a customer responding to a specific call to action, such as opening an email, clicking a link, or filling out a form.

These models help optimize email lists and direct mail campaigns, saving money and improving response rates. By predicting who will respond, businesses can avoid wasting resources on uninterested individuals and focus on those most likely to take action. This approach makes marketing efforts more efficient and effective.

5. Custom Propensity Models

Beyond these standard types, businesses can build custom propensity models for virtually any objective. For example, a company might create a model to predict the likelihood of a customer leaving a positive review, or a healthcare provider might use one to predict patient adherence to a treatment plan.

The flexibility of these machine learning models means they can be adapted to solve a wide array of unique business challenges, providing a competitive edge by allowing businesses to predict customer behavior for any desired outcome.

Benefits of Propensity Models

Integrating propensity models into your business strategy offers a wide range of advantages. These models provide the intelligence needed to make data-driven decisions, leading to significant improvements in efficiency, profitability, and the overall customer experience.

1. Improved Customer Targeting

Propensity models allow for a fundamental shift in how businesses approach their audience. Instead of using broad demographics, you can now identify and target customers based on their predicted behavior. For example, a business can use a propensity to purchase model to target only those individuals who are most likely to buy a new product.

This precision targeting ensures that marketing campaigns are not wasted on uninterested customers. By focusing on the most promising segments, you can achieve higher conversion rates and a stronger return on your marketing investment. This level of granular targeting is a game-changer for businesses of all sizes. With Leat you can filters your customers based on multiple things like inactive for the last 60 days to retarget them.

2. Increased Marketing Efficiency

With traditional marketing, a significant portion of the budget can be wasted on ineffective campaigns. Propensity models dramatically increase marketing efficiency by providing actionable insights. By knowing which customers are most likely to respond (propensity to respond), you can optimize your spending and allocate resources more effectively.

A 2021 study found that companies using predictive analytics for marketing saw a 20% increase in marketing ROI. This is because every dollar is spent on a more impactful effort, leading to more sales and better results without necessarily increasing the total budget.

3. Reduced Customer Churn

One of the most powerful applications of these models is in improving customer retention. A propensity to churn model helps businesses proactively identify and engage with at-risk customers before they leave. By analyzing subtle shifts in customer behavior, such as decreased usage or support inquiries, the model can alert you to potential problems.

This early warning system allows you to launch targeted retention campaigns, offering special discounts, personalized support, or other incentives to enhance customer loyalty. Reducing churn is often more cost-effective than acquiring new customers, making this a critical benefit for sustainable growth.

4. Better Resource Allocation

Propensity models help businesses allocate their resources more intelligently across the board. Whether it’s sales teams, customer service representatives, or marketing staff, these tools provide clear direction.

For instance, a B2B sales team can prioritize leads with a high propensity to purchase score, focusing their time and energy on accounts that are most likely to close. Customer service teams can be alerted to high-value customers who have a high propensity to churn. This strategic allocation of resources leads to better outcomes and a more streamlined operation.

5. Enhanced Personalization

Modern consumers expect personalized experiences. Propensity models are the engine behind true personalization. By predicting what a customer is most likely to do next, businesses can deliver highly relevant product recommendations, tailored offers, and personalized content.

This creates a seamless and intuitive customer journey. When customers feel that a brand understands their needs, it strengthens their relationship and builds long-term loyalty. This level of personalization is difficult to achieve without the predictive power of AI-powered models.

Conclusion

Propensity models are no longer a niche tool; they are a fundamental component of a modern, data-driven business strategy. By using machine learning models to predict customer behavior, companies can move beyond guesswork and make informed decisions that directly impact their bottom line. From improving customer retention to boosting conversion rates, the benefits are tangible and far-reaching.

By leveraging these AI-powered solutions, businesses can deliver a more personalized customer experience, optimize their marketing efforts, and ensure a competitive edge in a crowded marketplace. Embracing propensity models is about unlocking a new level of growth and efficiency, all powered by the intelligent use of data.