Leaders investing in business rarely struggle with the idea — they struggle with execution. This analysis of propensity model focuses on practical trade-offs: what to build first, what to integrate early, and where teams lose momentum after the first release.
Businesses try to forecast their customers' behavior to understand whether customers will buy, share their contact details, etc. Usually, entrepreneurs use their professional experience to build a propensity model. But, machines support strengthen the job significantly.
For instance, Scandinavian Airlines (SAS) uses machine learning to analyze clients' behavior. Thanks to a large amount of data, they offer customized offers to every client separately. It supports the company make a lot of sales, keeping their marketing budget safe.
Thanks to machine learning and artificial intelligence, machines can analyze large amounts of data. Moreover, the technologies help simulate particular events and build accurate predictions.
Down in the post, you will learn more about propensity modeling. Also, you will discover how to craft your propensity to buy models to forecast customers' behavior.
A Propensity Model? in practice
To start, we have to define propensity. This is the calculated predictions of how a customer will behave. It supports marketers understand if people respond to particular offers without launching promotional campaigns. This approach reduces the need for costly A/B testing, as machine learning models powered by propensity AI provide insights into the expected outcomes of offering a particular product.
Investigating further, there's another term worthy of mentioning. Risk propensity definition refers to the degree to which a customer likes to take or avoid risks. It measures how comfortable a person is with uncertainty or potential adverse outcomes when making decisions. Understanding it helps businesses assess how likely a customer is to purchase a new, unfamiliar product or invest in a premium service.
In practice, results of forecasting customers' behaviors are called propensity scores. These scores help businesses predict if potential leads—people who have shown some interest but are not yet ready to make a purchase—are likely to convert into paying customers. It's figured out based on what we know about the customer's traits and behaviors. According to Wikipedia, a lead is a particular form of customer in the awareness stage.
For a long time, experts manually created forecasts that businesses relied on. However, the development of computers brought propensity modeling to a new level. Today, there are various types of propensity models aimed at predicting whether specific groups of people are likely to take certain actions.
In practice, days, machine learning algorithms support entrepreneurs quickly create detailed pictures of their customers' behaviors. A top-notch propensity model should be designed with flexibility to adapt to recent trends and new data. For example, if new data appears, a model should easily accept and analyze it to keep up with recent trends. Let's dive deeper and discover how a good propensity model should look.
Factors of A Great Customer Propensity Model
By now, you might have a good understanding of propensity data modeling. However, Teams should know there is no one-size-fits-all propensity to pay model, as a good model should have particular qualities. Below, we will look at the factors that define a top-tier customer propensity model that can support create accurate forecasts.
- Since the world evolves constantly, the market also updates quickly. Experts need to invest a lot of time and effort in the development of a model that delivers effective propensity scores. Since we live in a data-rich age, new information is mined constantly. to stay current, a good propensity model should be dynamic so that managers will be able to update it quickly.
- High performance. A top-tier propensity-to-pay model should be capable of quickly analyzing large amounts of data. If it can deliver impactful propensity scores in time, a company will likely convert leads into new clients who explore a product. Results delivered fast help marketers make the best offer to engage a user.
- Growth-ready. To not build new models from scratch, they need to have a scalable propensity to buy models. Having a growth-ready model, a company can save a lot of resources by enhancing the current one.
- Demonstrates ROI. Indeed, a simple propensity to pay model can help predict the actions of potential customers, which is a source of helpful insights. That said, a top-quality model should estimate the amount of money needed to attract a client and make a sale. It can help calculate the ROI to understand if it will meet particular propensity model marketing goals.
Propensity Modeling Benefits
Insights and customer behavior forecasting are the main benefits of a propensity model to businesses. However, a simplified explanation helps answer the question, "What is a propensity to buy model?"
- Enhanced prediction. When launching promotional campaigns, marketing managers rely on their personal expertise and experience. That said, when they develop prediction models, the efficiency of forecasts increases. It happens because all the outcomes are based on mined data. So, the accuracy of predictions increases significantly.
- Increased ROI. Businesses need to conduct many tests to find the best business models that bring the highest revenue. A good model powered by machine learning can analyze many customer attraction tactics and provide information about the most impactful ones. Consequently, businesses can get the best model for the highest ROI.
- Saved time and budget. All companies strive to use their budgets efficiently. Propensity modeling can develop many different tactics and pick the most lucrative one's lightning fast. Real-world scenario testing may need to invest a lot of time and money. That said, machines can do a lot of calculations fast.
- Supports avoid restrictions. Sometimes, businesses may face many issues because it's forbidden to conduct particular experiments. For example, some tests are banned because they are considered unethical. In such a case, machine learning can analyze people's behavior based on provided data and build predictive models.
- Churn rate analysis. All businesses pay close attention to the churn rates to avoid revenue losses. When analyzing the predictive behavior of people, businesses get helpful insights. They can define what can boost the churn rate and force people to leave a website without any action. Managers can avoid tactics that may negatively impact them when having a forecast.
Explore how predictive analytics software empowers your business to turn data into future-ready insights.
Types of Propensity Models
A propensity model is used to predict the behavior. However, it isn't a one-size-fits-all technique that can be applied to any business. There are different types of propensity models that businesses use to create forecasts. The main differences are caused by the different types of audiences that can be targeted or problems that should be solved. Here are the main types of propensity models:
Propensity to buy. It is the main type of predictive model used by many businesses. They are used to determine the leads who are likely to convert into real clients. A good customer propensity model can help discover the best ways to attract leads and force them to make a purchase. In practice, this is very helpful in developing customized offers. They can help enhance marketing techniques and develop the most effective lead magnets.
Churn rate forecasting. Unfortunately, there is no option to convert 100% of attracted leads into customers. This propensity score can support forecast the number of leads who won't convert. Machine learning can help estimate the worst tactics with high churn rates to avoid.
Propensity to engage. Usually, managers have to conduct many A/B tests to discover which elements will draw users' attention and drive them to take action. Propensity modeling can support save a lot of time by forecasting the best tactics for engaging users. For example, managers can simply define the best lead magnets, having plenty of data to analyze.
Customer lifetime value. Estimating the maximum amount of money an average customer can bring to build a successful business model is vital. These days, organizations need to invest a lot of money into attracting one client, so the customer lifetime value is extremely critical. This predictive model can help build a lucrative business model, especially if it is focused on recurring customers.
Propensity Modeling Techniques To Predicts Customers' Behaviors
What are propensity models in machine learning? Several different machine learning models help get accurate propensity scores. Each model uses particular data analysis tactics to predict customers' behavior. Moreover, they have specific pros and cons that should be considered before developing your one.
Logistic Regression
In practice, this is a basic model used to build predictions and forecast particular events. It has only two variables, 0s and 1s, which present information on whether an event will happen. It teams commonly use to predict binary outcomes, such as whether a customer will make a purchase based on their historical activity. The model uses the dependent and independent variables to estimate the possibility of an event occurrence.
- Fast data analysis. Using linear algebra, computers can simultaneously apply particular formulas and get results.
- Accuracy. If simple data is used, this type of propensity modeling can produce accurate results that aren't tested in the real world.
- Interpretable. Every particular feature connects with outcomes mathematically. Also, managers can set the significance of features and their effect on outcomes.
- Simple data is required. This technique works better with simple and structured data only. Otherwise, the accuracy of results may be reduced.
- Results need to be explained. Since mathematical expressions are used to analyze data, the assistance of experienced mathematicians may be required to describe the results.
Decision Tree
Decision trees are visual, intuitive models. In essence, they build a structure, narrowing down the customer base by applying Yes/No answers to particular questions. For example, they can support find an answer to the question of whether a customer of a specific age, status, and nationality is likely to renew a purchase. Then, the model divides it by features using nodes, providing Yes/No answers.
In essence, it builds a structure, narrowing down the customer base by applying Yes/No answers to particular questions. For example, it can help find an answer to the question if a customer of a specific age, status, and nationality is likely to renew a purchase.
- Fewer efforts are required. It is one of the most hassle-free methods that enables the creation of models with ease. It has an intuitive structure that doesn't need to be explained in more detail to the majority of customers.
- Any data can be used. There is no need to normalize or scale data to create a decision tree. Also, missing values do not affect the decision tree development.
- Serious structure changes. Since a decision tree has a hierarchical structure, lower layers depend on higher ones. As a result, small changes can affect the entire structure and propensity scores significantly.
- Time-consuming. Managers must invest a sustainable amount of time in training the model.
Random Forest It is a complicated propensity modeling technique that uses regression and classification to forecast customers' behaviors. It uses the combination of logistic regression and decision tree techniques to build predictive models. That said, the model uses many trees to apply different conditions.
Every tree provides a particular propensity to buy results. The more trees there are, the more results can be obtained. Then, a formula is applied to the received results. It helps increase the accuracy of propensity scores by analyzing different results and picking the most popular ones.
- All the information processes do not have linear relationships. Therefore, minor changes don't affect the final propensity scores.
- Adaptable. Since decision trees don't depend on particular data types, the input information can be easily updated. The model will be able to compute new details.
- Cost-intensive. The random forest model needs to compute a ton of information. Since the number of trees can be huge, it may be costly to forecast using this model.
- Low control. Users can only control the number of trees and their depth. Other information is generated randomly. Therefore, users have limited control over their customers' behavior forecasting models.
Neural Networks
In practice, this is the most advanced type of propensity modeling, inspired by people's neural systems. Neural networks have a layered structure; in essence, the input and output nodes are the primary layers in the structure. Between them, an undefined number of hidden layers interconnect with each other.
Neural networks are handy for working with complex, unstructured data, such as customer activity on a website or patterns in large datasets. Once trained, they can make predictions that outperform traditional models, though they require substantial computational power and monitoring during development.
- Great performance. Once a neural network is trained, it can produce a lot of different computations. It's a great pick for large organizations that need to analyze and forecast their clients' behaviors constantly.
- Lack of control. Hidden layers in neural networks cannot be accessed. Their impact on outcomes cannot be estimated as well. Therefore, users have no clue what is happening between the input and output layers.
- A lot of resources are required. Even though training data is provided, managers need to monitor the outputs brought by neural networks. Also, neural networks need a lot of computing power when training.
How To Build a Propensity Model How do you make a propensity model? If you want to build your propensity to pay model, you need to carry out five major steps. By following them, you will create a forecast which will help you minimize risks and strengthen your marketing success by analyzing the scope of data only.
- Create a Dataset
First, you need to define the features to describe your customers. This includes customer characteristics (e.g., age, marital status) and the outcome you're trying to predict (e.g., whether they responded to a marketing campaign). Features are also known as independent variables. You'll likely spend some time compiling this dataset from different sources. If you're using a tool like Jupyter, this is where you upload the dataset to start working with it.
- Create a Model
To analyze your dataset, you will need to create a model. Using coding platforms like Jupyter, you will import libraries such as scikit-learn to assist with this process.
- Explore Your Dataset
Before you build a model, taking time and knowing your data is critical. This includes making sure that your data sample is large enough, considering essential variables (like marital status or education in forecasting response rates), and checking whether the data itself isn't outdated or incomplete. The aim of this step is to maintain that your model is processed based on proper information.
- Configure and Train Your Model
When setting up a model, start by choosing the outcome you want to predict (the "dependent variable") and selecting the factors that might influence that outcome (the "independent variables"). For example, the outcome could be whether a customer responds to a marketing campaign and factors like age, income, and education level.
Once you've chosen the right variables, you'll need to pick an algorithm (a type of model) to train it. Feel free to examine their perks, advantages, and drawbacks listed in the post above. Analyze your needs and find the best one that matches your requirements.
- Make Predictions
And now it's time to use it! Apply the model to new customer data to predict their behavior.
But it's only the beginning. It is rather important to monitor its accuracy and update it if necessary to avoid "model drift," which happens when the predictions become less reliable due to changes in customer behavior.
Customer Propensity Model – Wrapping Up
Propensity modeling is essential for large organizations that operate in highly-competitive markets. By predicting customers' behavior, they manage to build effective marketing strategies. In essence, they manage to spend less money on attracting leads and converting them into customers.
In case you also want to get an advanced system to get propensity scores, feel free to reach out to our specialists. We will help you tackle any possible issues related to propensity modeling.
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