How to Use Propensity Modeling to Predict Customer Behavior?
What is Propensity Modeling?
Propensity Modeling is a statistical technique used to predict the chances of certain events happening in the future. With the increasing use of machine learning, companies can build robust propensity models and make accurate forecasts. In marketing, for example, propensity models are used to predict customer behavior.
Then, it could be as basic as finding out whether a customer was likely to respond to a particular offer, or purchase a product, given a certain set of circumstances. Understanding the behavior of a customer helps businesses fine-tune their marketing efforts, and accordingly allocate resources.
Table Of Contents:
- What Is Propensity Modeling
- Various Types Of Models
- How To Use
- How To Implement
Importance of Propensity Modeling
As a general term, “propensity model” refers to different types of statistical models designed to predict binary outcomes; that is, either something will happen or it won’t.
It is a statistical technique used to predict the likelihood of a certain event occurring.
Propensity modeling is a powerful tool that can be used to improve marketing campaigns, target customers more effectively, make better business decisions, and to even predict customer churn.
While propensity modeling as a technique goes back to the early ’30s, today, machine learning is being deployed to develop these models.
There are some preparatory steps before you can begin making these models. An enterprise needs to first collect data on customer behavior. This data can be collected through surveys, focus groups, or customer transaction history.
Once this data is collected, it can be used to create a statistical model that can be used to predict customer behavior.
There are three basic sources of data. One of them is the demography of the customer, which will tell you who your customer is.
In order to understand what the customer has done, or what action he/she has taken, you need their transactional data, i.e. purchase history.
To understand why the customer completed that particular action, you need his/her opinions and comments posted in the “Comments” section or on social media.
What Are The Various Propensity Models?
Propensity models are typically used by businesses to target customers with specific marketing campaigns or identify which customers are most likely to respond to a particular offer or measure customer churn.
There are several propensity models that can be used. The most common and oft-used ones are probit (a type of regression model) and logit (logistic regression) models.
Simply put: Probit models are regression models where the dependent variant can only take two values, and determine the likelihood that an item or event will fall into one of a range of categories.
It is used to predict the likelihood of an event occurring, while logit models are used to predict the odds of the success of a certain event.
Probit models and logit models are similar, but they are based on different functions. Probit models use probits to determine the likelihood of an item or event falling into a certain category, like married or unmarried, while logit models use logistic functions.
One common use of propensity modeling is to predict customer purchase behavior.
Logistic regression: Logit models are commonly used in classification and predictive analytics.
Based on a dataset of independent variables, logistic regression estimates the probability of an event occurring, such as voting or not voting. In this case, the dependent variable has a range of 0 to 1.
In addition to making predictions about categorical variables, logistic regression can also be used to estimate relationships between dependent variables and independent variables.
Random Forest: In classification and regression problems, Random Forest is often used as a supervised machine learning algorithm.
For classification and regression, it uses the majority vote of the decision trees created on different samples.
What is more, the Random Forest Algorithm can handle both continuous and categorical variables, which is why it can be used for regression and classification. As a result, it produces better results when dealing with classification problems.
How to Use Propensity Models to Predict Customer Behavior Using Machine Learning
As we’ve said before, propensity modeling is a statistical technique that can be used to predict the likelihood of a certain event occurring.
For example, a retailer might use it to predict whether a customer is likely to make a purchase.
By understanding the factors that influence customer behavior, businesses can use propensity models to target their marketing and sales efforts more effectively.
A propensity score evaluates the probability that your customers will take one or more of those actions. By using these scores, you can also predict in real-time the value each customer brings.
Because of machine learning, today, companies can build robust propensity models and make accurate forecasts, using the right tools and data science teams.
Also, propensity models have to be dynamic, scalable, and adaptive.
By analyzing data from past customer behavior, machines learn how to anticipate what actions customers are likely to take next.
Propensity models can be considered binary classifiers in terms of machine learning, which means they can predict whether certain events, actions or behaviors will take place or not.
The following steps are typically involved in bringing propensity modeling to life:
- Mapping a strategy
- collecting relevant data
- preparing data for modeling
- creating and testing a model and
- deploying a model
Benefits of Propensity Modeling
Propensity modeling is based on the idea that past behavior is a good predictor of future conduct. By analyzing past data, businesses can make models that can accurately talk of the chances of events happening.
This information can then be used to decide on marketing, product development, and other areas of the business.
There are a number of benefits to using propensity modeling in your business:
- Such models can be used to predict customer behavior. By understanding the factors that influence customer behavior, businesses can use them to target their marketing and sales efforts in an effective manner.
- Propensity modelling can help you make smarter decisions by throwing up insights that would otherwise not be available to an enterprise.
- They can also be used to predict the value each customer brings in real time. By understanding which customers are most likely to make a purchase, businesses can gauge the value of those customers and allocate resources accordingly.
- Such models can be used to optimize customer acquisition strategies. By figuring out which customers are most likely to become regulars, businesses can identify which campaigns are resonating.
- These models can be used to optimize customer retention strategies. By knowing in advance which customers are most likely to churn, businesses can identify the measures that can then stop them from leaving.
- Propensity models can be used to predict the profitability of a given customer segment. By understanding which customers are most likely to generate revenue, businesses can optimize their marketing and sales efforts accordingly.
- They can also be used to improve customer service and improve the levels of customer satisfaction.
The Limitations of Propensity Modeling
For one, it is based on past data. Also, propensity models can be biased if the data used to create them is not representative of the population as a whole.
What’s more, propensity models are only as good as the assumptions made around customer behavior.
If these assumptions are inaccurate, the predictions will be as well. But despite such limitations, propensity modeling can be a valuable tool for businesses looking to better understand and predict customer behavior.
How to Implement Propensity Modeling
Propensity modeling is a statistical technique used to forecast the likelihood of a certain event occurring.
There are many ways to implement propensity modeling. The most common technique is to use historical data to train a machine learning algorithm.
The algorithm looks for patterns in the data that indicate a high or low likelihood of an event occurring. Once the algorithm is trained, it can be used to make predictions on new data.
Another approach is to use surveys or experiments to collect data on people’s likelihood of taking a certain action. This data can then be used to build a statistical model that can be used to make predictions.
To ensure that a data scientist’s models are fit for the goals of a business, it is imperative to choose model performance metrics for comparison that are in sync with those objectives.
Archetypical metrics include accuracy, specificity, and receiver operating characteristic metrics. But by using more targeted metrics, models can be selected and optimized more effectively to achieve the desired business outcomes.
In conclusion: Propensity modeling can be a powerful tool for predicting customer behavior. However, it is important to remember that all predictions are based on past data and may not necessarily be accurate for future events.
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