Nice hotels often stock rooms with amenities like ironing boards, spare toothbrushes, bottled water, and cozy robes. These items do more than provide comfort—they anticipate customer needs. If you forgot your toothbrush or wake up thirsty in the middle of the night, the solution is at hand. This attention to detail helps reduce travel frustration and shape a positive customer experience.
Predicting the future is impossible, but educated guessing can be a smart marketing practice. With predictive marketing, businesses can use what they know about consumers to predict future behaviors—and meet customers with exactly what they want, when they want it.
Learn how businesses can use analytics and predictive marketing to make data-informed decisions.
What is predictive marketing?
Predictive marketing is the practice of using predictive customer analytics to inform your marketing strategies. Predictive analytics employs machine learning algorithms and statistical models to identify patterns in historical customer data that can forecast future customer behaviors. These models consider multiple variables simultaneously to generate probability-based forecasts. Good data analytics tools can help you anticipate needs that enhance your marketing personalization efforts, improve your churn rate, and boost sales.
How to use predictive marketing
- Gather customer data
- Apply predictive models
- Employ customer segmentation
- Target with personalized content
- Test, measure, and optimize
The predictive analytics marketing process is how businesses use predictive analytics insights to inform strategic marketing decisions and improve future outcomes. Here’s how it works:
Gather customer data
Predictive analytics tools use multiple types of customer data to make predictions. Depending on the tools in your marketing tech stack, gathering this information may require pulling information from multiple sources, such as a customer relationship management (CRM) platform, a point-of-sale (POS) system, and a web analytics platform.
When combining data from multiple sources, data unification is key. Platforms may organize data in slightly different ways, and some tools might overlap and hold copies of the same information. A POS and CRM, for example, might both contain sales data. Many businesses use a centralized data management tool, like a customer data platform or data warehouse, to collect and unify different types of customer data. Syncing your predictive analytics platform with a single, centralized data source eliminates the risk of duplicate or disorganized data from multiple platforms.
Relevant data points include:
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Purchase history
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Browsing data
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Demographics
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Search history
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Web, email, and social media engagements
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Customer service interactions
Apply predictive models
A predictive model is a tool’s algorithmic approach to interpreting data and generating predictions. Many predictive marketing tools include several pre-built models designed to yield different types of insights. Businesses select and apply predictive models based on their goals and applications.
Examples of common predictive models include:
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Classification. Classification models sort customers into categories, such as “most likely to convert” and “least likely to convert,” based on behavioral patterns.
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Regression. Regression models forecast numerical values. These models can be used to predict concrete figures like annual sales revenue or customer lifetime value.
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Recommendation. Recommendation models analyze customer preferences and predict additional products or services that might appeal to them.
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Clustering. Clustering models identify patterns in customer behavior and group similar users together.
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Time series. These models use time-stamped data to predict operational changes over time. Time series models can predict temporal trends like seasonal demand.
Employ customer segmentation
With predictive marketing, businesses sort customers into distinct audience segments based on anticipated behaviors, such as conversion likelihood or churn risk. Many predictive analytics tools can automatically create segments based on behavioral patterns they identify.
As an alternative, marketing teams can use guided customer segmentation to create custom groupings. With guided segmentation, marketing professionals suggest criteria, such as “most likely to convert,” and the analytics tool uses data to populate the segment.
Target with personalized content
To leverage predictive insights, marketing teams target customer segments with tailored outreach. Your targeting strategies will vary depending on your marketing campaign goals and the predictive model you use.
For example, if you’re using a time series predictive model to increase sales revenue, and data indicates that sales spike on Sunday afternoons, you could allocate more advertising budget to targeting high-value customers during this time frame.
Test, measure, and optimize
Testing content and measuring marketing campaign performance helps teams evaluate how much value they’re gaining from applying predictive analytics. This often involves using A/B testing techniques to compare customer segments derived from predictive analytics to randomized groups. If the predictive segment doesn’t outperform the control group, it might indicate a problem with the data.
Adding new or additional data to your predictive marketing tools will help maintain and improve accuracy, optimizing performance over time. Keeping platforms up to date is an essential part of an effective predictive marketing strategy.
Predictive marketing use cases
- Dynamic pricing
- Predictive segmentation
- Predictive product suggestions
- Social media suggestions
- Churn prevention tools
- Leveraging customer lifetime value
Predictive analytics insights can help your team make data-informed decisions and improve future marketing efforts. These are some of the most common applications of data in predictive marketing:
Dynamic pricing
Dynamic pricing is the practice of adjusting your product prices in response to shifting market conditions. This can involve raising or lowering prices, depending on your marketing strategy. For example, a caramel popcorn company might plan to run a sale ahead of a predicted fall demand spike. This approach could help their product edge out competitors.
Predictive analytics software platforms can support dynamic pricing by forecasting temporal demand fluctuations, monitoring competitor prices, and segmenting customers by willingness to spend.
Predictive segmentation
With predictive segmentation, predictive analytics tools automatically place customers into groups based on expected behaviors. This feature may help your team identify consumer trends and capitalize on them with tailored marketing messages. For example, if segmentation revealed that customers who purchase toddler clothing usually shop in the evening, the marketing team could run targeted promotions for this demographic after 7 p.m. This technology can also save time by reducing the effort of manual segmentation.
Predictive product suggestions
Many ecommerce websites use predictive product suggestions to display a list of “for you” recommendations. Predictive product tools use past browsing and purchasing behavior. Sephora’s homepage, for example, displays a personalized list of products labeled “chosen for you.”
For businesses promoting products on an ecommerce platform, creating robust product listings can increase your chances of being featured. Including accurate item category information and keywords helps the algorithm classify products and serve them to relevant users.
Social media suggestions
Predictive marketing technology can provide suggestions to support your social media marketing efforts. By analyzing past content performance, predictive social media tools are able to recommend posting times, content themes, and image styles likely to resonate with your audience. Marketing and social media teams can use these tools to choose between several photos for an Instagram post and pick the best time to share content.
Churn prevention tools
Churn, also known as attrition or drop-off, occurs when customers stop using a product or service. Predictive analytics can help businesses identify experiences or actions that indicate a customer is close to dropping off and develop effective customer retention strategies.
For example, a predictive analytics tool could find that customers who set “notify me” alerts on three or more out-of-stock products tend to unsubscribe from emails. This could indicate that product shortages are increasing churn. To address this issue, a business could focus on keeping inventory in stock or providing regular updates to let customers know when products will be available.
Leveraging customer lifetime value
Customer lifetime value (CLV) is the predicted revenue per customer for the duration of the consumer-business relationship. CLV is an indicator of business health—many customers with a high CLV indicate your business runs on loyal customers who make frequent purchases. Predictive analytics can help provide detailed CLV estimations. This data can help marketing teams make budgeting decisions and set priorities, such as the decision to target high-value customers with additional conversion-focused messages.
If, for example, an online yarn store learned that 80% of its revenue comes from a small cohort of super shoppers, the team might decide to allocate more budget to taking care of this group by creating a VIP loyalty club and providing them with early access to new products.
The risks of predictive analytics
Although predictive models are quite reliable, understanding the risks associated with this approach can help your team determine how to best integrate predictive analytics with your marketing decisions. These are some factors to consider:
Low-quality data
Flawed or incomplete data, either from an inaccurate source or improper data syncing, can cause inaccurate predictions. Accuracy may be unreliable for new businesses with a limited dataset. This can lead to marketing missteps and wasted marketing budget.
Outdated and biased data
Predictive marketing is based on historical data—it makes predictions based on past behaviors. If your company has changed significantly, predictive models might not reflect this shift. For example, if an ecommerce company shipped only on the East Coast for several years before offering nationwide delivery, the company’s sales data would likely show a disproportionate number of East Coast customers.
Unexpected market shifts
Predictive models assume consistent operating conditions. If an unforeseen event, such as a pandemic or natural disaster, causes a shift in market dynamics, generated insights may be rendered moot.
Data privacy issues
Businesses that handle sensitive customer information are subject to data compliance and privacy laws. Failure to meet these standards can result in substantial fines and reputational damage.
Predictive marketing FAQ
What is an example of predictive advertising?
Netflix streaming recommendations, Amazon “for you” product listings, and Spotify-curated weekly playlists are all examples of predictive marketing. These initiatives use predictive technology to personalize content recommendations based on individual engagement history.
How do you prevent churn?
Ensuring a high-quality customer experience helps prevent churn. Consider using predictive analytics to identify experiences that contribute to customer attrition or notable drop-off rates in your customer journey. Working to resolve issues and address pain points can reduce churn.
What is the role of AI in predictive marketing?
Many predictive marketing tools, such as Salesforce Einstein, use AI to enhance forecasting speed and accuracy. With machine learning, an AI technology, software tools can ingest and analyze extremely large datasets—processing more data helps improve prediction quality.
Is predictive marketing reliable?
Predictive marketing uses historical data and trend analysis to forecast consumer behavior. Predictive marketing tools analyze large volumes of data to make the best possible estimations, but they can’t promise 100% accuracy. Factors like limited or low-quality data can diminish prediction quality.
