Why you should add predictive modeling to your marketing mix

Predictive analytics provides a solid foundation for data-driven decision-making in marketing campaigns. But while most marketing leaders are already using some form of predictive analytics, specifically predictive modeling, many still struggle to fully incorporate it into their decision-making.

Concerns about data quality and appropriate data use are hindering widespread adoption of predictive analytics. Similarly, some marketers believe that predictive analytics is too complex and requires a thorough understanding of data science or the use of advanced AI tools. Advanced analytics and AI can enhance predictive modeling, but even without these capabilities, predictive modeling can be used to improve decision-making.

Let’s take a look at how predictive modeling can enhance your marketing to increase your brand’s sales next year. Learn about its current usage in the advertising industry, start approaching it in 2024, and discover real-world examples of its impactful results.

Common use cases for predictive modeling

Predictive modeling uses large datasets to make data-driven decisions and replace intuition with insight. By discovering patterns in your data, you can better predict consumer behavior and optimize your advertising strategy. Here are some common ways to use predictive modeling in advertising.

Accurate audience segmentation

Using data such as customer demographics, online behavior, and purchase history, you can segment your audience and create campaigns tailored to each target segment’s individual preferences and needs.

Up to 71% of customers expect branded personalization, so segmentation is key to meeting this demand.

Ideal placement and timing of advertising campaigns

Historical data analysis can generate predictive models that tell you which channels or platforms are most effective and when it’s best to advertise.

Advertisers can use this analysis to develop better media planning strategies to ensure their ads are delivered to the right audience at the time when they are most likely to engage with or purchase from their brand.

Maximize customer lifetime value estimates

Customer lifetime value (LTV) is the expected return a brand can expect over the entire relationship with a customer. By using predictive modeling to predict your customers’ LTV, you can make data-driven investment decisions and retain existing high-value customers across media channels.

You can also use it to identify leads that are most likely to be valuable to your brand in the long term.

Learn more: The power of predictive analytics: Is the future now?

Predictive modeling helps target audiences, optimize advertising campaigns, and prioritize high-value customers. It can also be utilized for a variety of applications, including trend analysis and ROI prediction for resource allocation to respond to industry changes.

Many marketers are upgrading their predictive modeling with generative AI and machine learning, reflecting industry trends. However, predictive modeling is still available without investing in advanced technology.

For example, predictive modeling based on statistical principles does not require machine learning. Use cases include:

• regression analysis. Helps optimize your outreach by comparing the impact of campaign variables (such as channels and messaging).
• Time series analysis is another type of statistical modeling that helps marketers understand trends over time and create sales forecasts.

Learn more: 4 AI Categories Impacting Marketing: Predictive Analytics

Get started with predictive modeling

Regardless of the approach, with or without AI, there is a strong business case for implementing predictive modeling in 2024, especially as consumer behavior evolves and media habits change.

The ability to hyper-personalize campaigns alone is quickly becoming a critical element for brands, and predictive modeling provides that capability along with many other important insights. So how do you get started?

If you don’t have much experience with predictive modeling, partnering with an agency is an ideal place to start. By leveraging the agency’s expertise across many brands, you can develop your skills and get guidance in integrating predictive modeling into your overall marketing strategy. Collaboration with government data scientists and analysts may also be helpful.

Here are other self-directed ways for you and your marketing team to learn more about predictive modeling.

Take advantage of training

It’s good for your entire marketing team to understand the basic principles of predictive modeling and analytics. Fortunately, there are many resources available to help your team understand the basics.

These include online courses, tutorials, and other resources. Learning about this area will enhance collaboration, allow you to work more effectively with external resources, and contribute to better decision-making.

Define priorities

Once you have a deeper understanding of the capabilities of predictive modeling, the next step is to define the initial and long-term goals of your strategy. These may include priorities such as:

• Improve the way you target your customers.
• Predict customer behavior more accurately or measurably.
• Improve campaign performance.
• Maximize customer acquisition efficiency and LTV.

Setting priorities will help you plan your implementation and identify specific success metrics.

Start small and scale up

Once you set your priorities, choose a single project to start rather than trying to implement predictive modeling across many campaigns at once.

That way, you can gain experience without getting nervous. It also gives you space to recognize and apply lessons learned along the way.

Once you are confident in the impact of your results, you can expand your predictive modeling implementation more broadly.

Continually reevaluate and adjust

Another best practice is to frequently evaluate the performance of your predictive modeling against your goals using the metrics you identified when you defined your priorities for different time periods and changes in your marketing campaign.

As you evaluate your progress and analyze your results, be prepared to iterate on your approach so that you can continue to improve your predictive modeling techniques on a regular basis.

Incorporate predictive modeling into your 2024 strategy

When used properly, predictive modeling can help transform your campaigns. In a real-world use case, we were able to achieve year-over-year subscription growth in a highly competitive industry.

Rapid data-driven optimization systems and reliable predictive performance models have significantly increased market share and exceeded customer expectations.

Stay up to date on advances in technology and analytics as you integrate predictive modeling into your marketing strategy this year.

Whether you partner with an agency, start with an AI tool, or gradually increase your technology capabilities, incorporating predictive modeling will power your decision-making for great outcomes in 2024.

Learn more: What do marketing attribution and predictive analytics tools do?