What is predictive modeling in marketing? Predictive modeling is a subset of data mining that uses historical data to make predictions about future events. It can be used to predict things like customer behavior, what products people are likely to buy, and even how much revenue a company can expect to generate in the future. Predictive modeling is becoming an increasingly important tool for marketers, and those who understand how to use it effectively can gain a significant competitive advantage. In this article, we will discuss what predictive modeling is, how it works, and some of its applications in marketing. We will also explore some of the challenges involved in using predictive modeling and offer some tips for getting started. So let's get started!

Predictive Analytics

Predictive analytics combine historical data and statistical techniques to pin down how likely a future event is. Some of the statistical strategies used in predictive analytics include:

• Data mining 
• Predictive modeling 
• Machine learning 

For example, Lenders use predictive analytics to determine the likelihood of a person making on-time payments in the future. Credit scoring is the historical data they use. Calculating a score based on past data about one’s credit history, banks, previous payments, etc. provides them with a score. 

One very interesting example is how Netflix used big data and predictive analytics when developing its hit series “House of Cards’. Based on historical data from customers, Netflix determined that the following combination would yield a successful show:

• Leading actor Kevin Spacey 
• The House of Cards concept, which was first used in the U.K.
• Director David Fincher 

Predictable Modeling vs Predictive Analytics

“Predictive analytics” and “predictive modeling” have unique meanings that are helpful to differentiate. 

Both of these concepts are about identifying data patterns. With predictive modeling, the pattern is presumed to remain constant as individual variables change. Therefore, you can use it to predict effects in different data sets. Consider the example where when prices decrease, sales increase. Marketing teams could use this model to predict how much revenue they would generate or how many conversions they would achieve. 

How Predictive Modeling is Used in Marketing

Predictive modeling is incredibly useful for marketing activities. It can help marketing teams assess the likelihood of customer behaviors. Data scientists leverage this tool to pin down the chances of a certain event occurring. The more data points they have on the different factors influencing the event, the more accurately they can predict the end result. 

With a lot of high-quality data, you can make personalized, accurate predictions. Marketers may use predictive modeling to evaluate how a certain customer segment will respond to an ad campaign or to determine the best price point for in-app purchases. 

In addition to very specific predictive analytics, marketers also use predictive models on broader scales.

• Conversion estimation 
• ROI
• Customer relationship management
• Promotions 

Marketing Use Cases for Predictive Modeling

Predictive modeling is certainly a sales tool, but it can be used for much more. There are many key marketing and customer success use cases for predictive modeling such as:

• Understanding behavior: Based on previous interactions, you gain insight into consumer behavior. This provides useful information for segmenting your audience and delivering targeted messages. 
• Assessing churn: While churn is a part of every business, it’s something you can drastically reduce with actionable insights. Using predictive modeling, assess the likelihood of churn. Using current sales trends, you can predict churn rates and take intentional steps to prevent it. 
• Upselling and cross-selling: Which customers are most likely to purchase related items at the time of purchase? Or to upgrade a product/service later on? Using predictive models, you can determine the most effective ways to engage customers for upselling and cross-selling. The most well-known eCommerce businesses use collaborative filtering to provide recommendations for products/ services. Collaborative filtering is about using past behavior to provide recommendations. 
• Lifetime Value: What monetary value will a customer provide to your business over the course of the business relationship? Based on their demographic information, buying habits, and more, predictive models can calculate the average lifetime value. 
• Lead scoring: Who is likely to become a customer based on previous data? Use your customer data to figure rank your prospects based on their chance of converting. You can then leverage this data to trigger marketing campaigns or prioritize sales outreach efforts to those who pass a set threshold of lead scoring. 

Using Predictive Models in Your Marketing Strategy

To take advantage of predictive models, marketers must collect, analyze, and implement data. 

Define Your Question

What question are you seeking to answer with predictive modeling? Before jumping into the data, you should set your direction. 

The basic formula is “based on what happened before, what is likely to happen in the future?”

• Which customers are likely to churn within the next month?
• Which leads are most likely to convert within the next month?
• Which marketing pieces should I deliver to people whose free trial just ended and I’m looking to convert?
• Which audience segment should I target in my next Instagram campaign?

Unified Marketing Measurement

A lot of historical data is required to precisely predict future trends. Predictive analysis requires that you collect data on:

• Consumers’ interactions with campaigns 
• Engagement from customers 
• Historical data
• Demographic information 

Then, synchronize the data to develop consumer identifies. Unified marketing measurement involves data collection from consumer behavior, marketing trends, and online engagements in a central location. 

Integrate Measurement Tools

To handle the massive amount of data, many marketing teams rely on marketing analytics software. You must convert all of the data into comprehensible patterns and then actionable insights. 

Artificial Intelligence 

Many marketers experience the benefit of artificial intelligence and machine learning when it comes to predictive modeling. Viewing and acting on real-time insights helps marketers with omnichannel efforts. Combining predictive analytics, AI, and machine learning 

Predictive Analytics Models

• Classification Model: This is the most basic model. It organizes data for direct, easy query response. 
• Cluster Model: This model clusters data based on common attributes. This type of modeling can help you figure out which segmentation makes the most sense for your audience. You can experiment with multiple clutter models to review which audience segments are the most logical for your goals. The cluster model segments people with shared behaviors or demographic characteristics, allowing you to craft customized strategies on a larger scale. 
• Identification modeling: Beyond initial segmenting, you can form identification models. These help you to identify prospects that most closely match your current customers in some relevant manner. This can help you determine who to focus on or how to best target engagement. 
• Outliers Model: The data points in this model are abnormal. Several methods are used to detect outliers depending on the center of the database at hand. 
• Time Series Model: Use this model for a better assessment of what has happened over the course of a set time frame. Time series data analysis assesses data points based on the time you determine. This model then forms predictions based on the patterns in the time-series data. 

Unlock Predictive Models with Your Data Warehouse

A wealth of data provides the foundation for predictive modeling. One challenge that you may face is collecting and combining data from multiple channels. While this is essential, it can provide a major hurdle for predictive analytics marketers. 

The best solution is to use a Data Warehouse as the single source of truth for data. The data warehouse serves as one powerful location to store, analyze, and activate customer data from all of your channels. 

You can create a core customer data model right in your data warehouse, and analyze it using Python, SQL, or data modeling tools. 

The Data Warehouse provides you with a single view of your customers. You’ll be able to model customer behavior and generate key analytics for your marketing efforts.

Making the Most from Predictive Modeling

To make the most of your predictive modeling efforts, you’ll need a way to activate your Data Warehouse to your key marketing channels. 

GrowthLoop is your solution for activating your data cloud on over 30 destinations. Run our customer segmentation platform directly on your data warehouse to unlock a huge potential for experimentation and predict marketing. 

With our no-code data activation solution, you can quickly and easily activate your customer data and drive revenue. Segment your customers without SQL and leverage these groups for your marketing efforts on all channels. 

Learn more about how our software can help your business drive revenue. Contact our software experts today!