Learn how predictive modeling can optimize your marketing campaigns through accurate audience segmentation, strategic ad placement and more.
Predictive analytics provides a solid foundation for data-driven decision-making for marketing campaigns. But while most marketing leaders already use some form of predictive analytics, specifically predictive modeling, many still struggle to fully integrate it into their decisions.
Concerns about data quality and using the right data hinder the wider adoption of predictive analytics. Likewise, some marketers think predictive analytics is too complex, assuming they must fully grasp data science or employ advanced AI tools to benefit from it. While advanced analytics and AI can enhance predictive modeling, you can still improve decisions using predictive modeling without these capabilities.
To boost your brand’s sales in the upcoming year, let’s explore how predictive modeling can enhance your marketing. Learn about its current use in the advertising industry, kickstart your approach in 2024 and discover real-world examples of its impactful results.
Popular use cases for predictive modeling
Predictive modeling uses large datasets to inform data-driven decisions, replacing intuition with insights. By detecting patterns in the data, you can better predict consumer behavior and optimize ad strategies. Here are a few popular ways to use predictive modeling in advertising:
Accurate audience segmentation
With data on customer demographics, online behavior, purchase history, etc., you can segment your audience and create customized campaigns for each target segment’s distinct preferences and needs.
Up to 71% of customers expect personalization from brands, so segmentation is important to help meet this demand.
Ideal placement and timing for advertising campaigns
Historical data analysis can produce predictive models that indicate which channels or platforms are likely to be most effective and when it’s optimal to place ads.
Advertisers can use this analysis to develop better media planning strategies, assuring the right audiences are served ads when they are most primed to engage with a brand or make a purchase.
Maximize customer lifetime value estimates
Customer lifetime value (LTV) is the predicted profit a brand can expect to make throughout the customer relationship. By using predictive modeling to project customer LTV, you can make data-driven investment decisions to retain existing high-value customers across media channels.
You can also use this to identify prospective customers who will most likely be valuable to the brand over time.
Predictive modeling aids in audience targeting, ad campaign optimization and prioritizing high-value customers. It also has various applications, such as trend analysis for responding to industry shifts and ROI projections for resource allocation.
Many marketers are upgrading predictive modeling with generative AI and machine learning, reflecting the industry’s trend. However, predictive modeling remains accessible even without investing in advanced technology.
For example, predictive modeling based on statistical principles don’t require machine learning. Use cases include:
- Regression analysis, which helps compare the impact of campaign variables (i.e., channels or messaging) to optimize outreach.
- Time series analysis is another type of statistical modeling that can help marketers understand trends over time to create sales forecasts.
Getting started with predictive modeling
Regardless of approach — with or without AI — there’s a strong business case for deploying predictive modeling in 2024, especially with evolving consumer behavior and changing media habits.
The ability to hyper-personalize campaigns alone is quickly becoming table stakes for brands, and predictive modeling delivers that capability along with many other essential insights. So, how can you get started?
Partnering with an agency is ideal if you don’t have much experience with predictive modeling to make a start. By tapping into agency expertise across many brands, you can develop your skills and get guidance as you integrate predictive modeling into your overall marketing strategy. A collaboration with agency data scientists and analysts can also be helpful.
Here are other self-driven ways for you and your marketing teams to learn more about predictive modeling.
Take advantage of training
It’s a good idea for the entire marketing team to understand the basic principles of predictive modeling and analytics. Fortunately, many resources can help your team understand the fundamentals.
These include online courses, tutorials and other resources. Learning about the field enhances collaboration, helps you work with outside resources more effectively and contributes to better decision-making.
Define your priorities
After gaining a deeper understanding of predictive modeling’s capabilities, the next step is to define initial and long-term objectives for the strategy. These might include priorities such as:
- Improving how you target customers.
- Predicting customer behavior more accurately or measurably.
- Improving campaign performance.
- Maximizing customer acquisition efficiency and LTV.
Planning implementation and identifying specific success metrics will be easier when you set your priorities.
Start small and scale up
Once your priorities are set, choose a single project to make a start instead of trying to implement predictive modeling across numerous campaigns all at once.
This will allow you to gain experience without getting overwhelmed. It will also give you the space to recognize and apply lessons you learn along the way.
Once you feel confident about the impact of your results, you can ramp up predictive modeling deployment more broadly.
Reassess and adjust continuously
Another best practice is to frequently evaluate predictive modeling performance against objectives, using the metrics you identified when you defined priorities over varying periods or changes in marketing campaigns.
As you assess progress and analyze results, be prepared to iterate your approach so you can continuously improve your predictive modeling techniques regularly.
Incorporate predictive modeling into your strategy in 2024
Using predictive modeling right can help transform campaigns. In one real-world use case, it helped deliver YoY subscription growth in a fiercely competitive vertical.
The data-driven rapid optimization system and the high-confidence predictive performance model significantly increased market share, exceeding client expectations.
Stay updated on technology and analytics advancements as you integrate predictive modeling into your marketing strategy this year.
Whether you’re partnering with an agency, starting with AI tools or gradually enhancing tech capabilities, incorporating predictive modeling will enhance your decision-making for impressive 2024 results.
The post Why you should add predictive modeling to your marketing mix appeared first on MarTech.
MarTechAbout the author
As a leader in the marketing space, Jessica is a written contributor to various industry publications offering insights on key industry trends. In addition, Hawthorne-Castro has been recognized from the broader professional community with a long list of accolades for her career accomplishments, including: semifinalist in the Ernst and Young “Entrepreneur of the Year” in the Greater Los Angeles area, “Women to Watch” recognition for the “Marketing Hall of Femme” Direct Marketing News, “Woman of Influence” by L.A. Biz and Biz Women, “Female CEO of the Year in Advertising, Marketing and Public Relations”, presented by the CEO World Awards organization, Marketing EDGE’s “Rising Star Award”, and “Top 40 Under 40” by Direct Marketing News.
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