We are all in the data game, a game of understanding our audiences and customers deeply.
As C.F. Kettering said, “My interest lies in the future because I will spend the rest of my life there.”
When we look at data and analytics, we see that most marketers focus on the past. How did we perform last quarter? Which products were popular last quarter? What is our revenue share for 2023 compared to last year? The list goes on.
We do use data to look into the past, but data becomes more valuable when we use it to predict the future. Propensity modeling is a valuable tool in this situation. Let us tell you more about it in the following section.
What Is Propensity Modeling?
In simple terms, propensity modeling is a statistical approach that attempts to predict the probability of leads, visitors, and customers to perform certain actions. We can say that propensity modeling is a statistical method that identifies and controls factors that influence customer behavior.
For example, using the approach of propensity modeling, your marketing team can predict the likelihood of a lead converting into a customer using AI and machine learning.
The propensity score is the probability of the prospect taking a certain action. Propensity modeling removes the guesswork from the marketing equation. Combining data and audience engines can indicate precisely what works for which group of people, and who is likely to perform the desired action.
However, implementing propensity modeling and audience engines in your organization is not an easy task. To be good at customer predictions, propensity modeling must be dynamic, scalable, and adaptive. Here are the characteristics in detail:
Dynamism means that the model is built in a way to keep up with trends, adapt, and learn as new data becomes available.
Scalability means that the model is built for the entire team and can accommodate more team members in the future.
Adaptability means that the model should have a proper data pipeline for injecting, validating, and deploying data in a timely manner to adjust to any changes.
When implemented in the right way, propensity modeling can help you avoid revenue loss and build hyper-targeted marketing campaigns.
Read More: SalesTechStar Interview with George Kanuck, Chief Sales Officer at Monetate
Best Practices for Implementing Propensity Modeling
Regardless of the type of data model you develop and implement in your organization, the implementation process plays a pivotal role in its success, and propensity modeling is no different. Know that such models rely on machine learning algorithms, here are a few steps that will help you bring propensity modeling to life in your organization:
Define the Goal of the Model
The first stage in creating a propensity model is to map out a strategy. You can achieve this by aligning your business objectives and deciding what insights will help you increase your chances of audience conversion.
Gather Data
During this stage, you must collect relevant data about your active and potential customers. With the demise of third-party cookies, your focus should be on collecting first-party data, but third-party data can also be helpful. A strategic combination of both data can help you extract better insights.
Clean and Prepare the Data
In the next step, you must prepare the data selected for propensity modeling. The data should be accurate, consistent, and complete. Your team can use a variety of data preparation steps to make it happen.
Choose a Model
Now that you have the data ready, it is time to build and train propensity models using various algorithm approaches. Talk to your ML specialists, and they will help you choose from a wide range of models such as decision trees, logistic regression, random forests, and more. Create an understanding for each one of them and choose the most suitable for you.
Train and Deploy the Model
The final stage of implementing a propensity model is to deploy the model into action. As you are deploying new models, your data scientists should keep on maintaining the already implemented models for consistent accuracy over time.
Having a propensity model trained to work on historical customer data is only half a battle won. No amount of artificial intelligence can replace human creativity and critical thinking. It is up to your team to decide what to do with the outputs and how a new strategy can be built around that.
Read More: Why Apple’s Headset Could Change The Retail Game
Companies that have the access to this powerful tool called propensity modeling should use it to strategically bring valuable insights into the future behavior of customers. However, the accuracy of predictions will depend on the quality of the models.