Propensity Models in Sales: What You Need to Know

In sales, several factors play a crucial role in taking a particular lead to a closed-won. These factors include the right time to reach out to a prospect, predicting customer behavior, and understanding their pain points, priorities, challenges, etc. Today, sales reps largely rely on their own gut feel and preliminary data points to determine which leads should they pursue.

Solely relying on guesswork without any scientific validation doesn't result in the best outcomes and throws predictability out the window. For example, when you have millions of leads but limited time and resources, how will you decide which leads to prioritize?

What if your sales team had the know-how to decipher prospects' wants, their preferences, and how to nudge them to make a purchase? Wouldn't this be the dream? Well, that's what sales propensity models can help you achieve.

What Exactly Are Sales Propensity Models, and How Do They Work?

Fundamentally, a propensity model is a statistical, machine-learning algorithm created to forecast binary outcomes. These models predict whether an event will occur or not. They also help indicate the likelihood of an individual performing a particular action.

In sales, this action could include placing an order, checking out the pricing page, interacting with an advertisement, or similar touchpoints. By examining past customer data, these propensity models detect recurring patterns and trends, consequently aiding organizations in understanding user behavior more effectively. It also helps them create better user experiences and enhance their lead-targeting strategies.

After analyzing the relevant data points, the propensity model evaluates each user and determines a score or probability indicating their likelihood of taking the desired action. If a user has a high propensity score for making a purchase, the sales team can identify them as a hot lead and prioritize their efforts toward them.

Types of Propensity Models in Sales

There are multiple variations of propensity models, each designed to meet specific sales requirements. Some commonly used models are:

1. Churn Propensity Models

Churn propensity models are used by businesses to pinpoint leads who may be on the verge of terminating their subscriptions. This information helps the sales and marketing teams create proactive retention strategies, such as customized offers, discounts, or focused messaging. All this helps in re-engaging users before they churn.

2. Logistic Regression Models

These models are usually employed in classification and predictive analytics. Using a dataset of independent variables, logistic regression models can calculate the likelihood of an event happening, like purchasing or not purchasing.

The model uses historical user data to determine a prospect's propensity to buy, using a sigmoid function to calculate the probability. The resulting output ranges from 0 to 1, with values closer to 1 indicating a higher chance of a purchase. With this information, sales teams can target leads with higher purchase probabilities, leading to more effective sales strategies and improved resource allocation for increased revenue.

3. Response/Engagement Propensity Models

When businesses design and run marketing campaigns, they can leverage response propensity models to predict which users are likely to respond positively to those campaigns. They help you know which users have a high propensity to click on your ads. With this information, they can boost their marketing efforts and target the right prospects with the right message, maximizing conversion rates.

How to Implement Propensity Modeling With Machine Learning: Best Practices and Tips

Machine learning is a crucial method used in developing propensity models. These predictive models learn from data and continuously enhance their predictive abilities. Here's how you can implement an effective propensity model with machine learning:

1. Build a Mapping Out Strategy

Before starting the practical implementation of propensity modeling using machine learning techniques, it is essential to map out a strategy. You can do this by clearly defining the specific goals you want to accomplish using propensity modeling. Identify the binary outcomes you wish to forecast, such as the user's past purchases, service subscriptions, or ad interactions. Establishing a clear objective will ensure your model is customized to address your business requirements effectively.

2. Collect the Necessary Data

Collecting data from various sources is essential in creating an effective propensity model. To achieve your objectives, consider gathering data from user interactions, transaction histories, website activity, social media engagement, and demographic information.

There are two major types of data that you can use in your propensity model:

• First-Party Data: It is the data that your company owns. It is present on your website, CRM, social media, etc.
• Third-Party Data: It is the data you can gather from third-party platforms like business intelligence tools (Zoominfo, Clearbit, Apollo, etc.) This data is generated and collected by external sources and can offer valuable insights into specific audiences.

3. Prepare Your Data for Modeling

Once the data has been gathered, it is vital to prepare it for modeling. This crucial step includes data cleaning, managing missing values, encoding categorical variables, and scaling numerical features. Doing so helps ensure your propensity model generates accurate, consistent, dependable predictions.

4. Select the Right Features as Input for Your Model

Selecting the right mix of features is a critical factor in determining the success of your propensity model. Features are the variables that directly impact user behaviors, and accurately choosing the most relevant ones is crucial for making accurate predictions.

To get a complete understanding of your target users, consider using the following metrics:

• Demographic Information: Demographic data offers significant insights regarding the characteristics of your users. This includes essential features like age, gender, education level, and location.
• Psychographic Information: Psychographic data explores the characteristics, perspectives, preferences, and dislikes of users.
• Data Related to Transactions and User Behavior: It includes features like the total purchases made, the specific products or services acquired, and the average value of orders. You can also get details about the duration between an offer and a successful conversion.
• User Engagement Data: It includes features such as webpage dwell time, the number of emails opened and clicked, mobile app searches, and social media interactions.

5. Create and Test Your Model

Once you have collected the necessary data and features, it is time to construct your propensity model using machine learning algorithms. The choice of model depends on the intricacy of your data and your specific goals. You can select from a range of models, such as logistic regression, decision trees, random forests, or neural networks. Let's have a quick look at each of them:

• Logistic Regression: Logistic regression is a straightforward algorithm used for binary classification (0 or 1). It estimates the probability of something happening by considering the relationship between dependent and independent input variables. This model is generally used for generating propensity scores.

• Decision Trees: Decision trees are models that are both intuitive and non-linear. They divide the data into branches based on different features to predict outcomes in the form of YES/ No. These models can capture intricate patterns.

• Random Forests: Random Forests are machine learning algorithms that combine multiple decision trees into a forest. They are suitable for handling large datasets and performing classification and regression tasks.

• Neural Networks: Neural networks are highly effective models that draw inspiration from the brain. They contain neurons or nodes present in different interconnected layers of the model. Neural Networks can learn from vast amounts of unstructured data and have the ability to detect intricate patterns. They do, however, demand substantial amounts of data and computational resources.

6. Deploy Your Model

Once the model has been created and tested, the next step is to deploy it within your sales process. This involves integrating the model into your sales team's workflow, ensuring smooth interaction with their current tools and systems. Ensure your sales representatives receive adequate training on interpreting and using them. Keep track of the model's performance in real-world scenarios and collect feedback from your sales team to pinpoint any potential issues or areas that can be improved.

Use Propensity Models in Your Sales Strategy Today

The emergence of propensity models has undoubtedly revolutionized the sales industry. By using these models, sales teams can better understand their prospects and deliver personalized experiences that foster long-lasting business relationships. As these models advance and become more sophisticated, the sales landscape is poised for an unprecedented transformation.

By following the best practices mentioned above, you can effectively incorporate propensity modeling into your sales processes. This will allow you to harness its full potential and design more data-based sales strategies.