5 Ways Predictive Marketing Can Pilot Customer Purchases

Companies collect a wealth of data from customers, employees, and users. This overwhelming amount of data also has an overwhelming amount of potential, particularly when it comes to personalizing digital experiences. Nearly half of the 20,000 consumers in PwC’s 2024 Voice of the Consumer Survey say they’re comfortable with companies using their data to offer personalized experiences and custom services. 

The data that’s accrued, often referred to as “big data”, can be used to help you forecast customers’ needs, changing behaviors, and trends. This is the foundation for predictive marketing – an approach that uses data, analytics, and technology to create more informed and impactful marketing campaigns. Let’s look at the specifics of how predictive marketing works to inform strategic decision making.

What Is Predictive Marketing?

Predictive marketing uses a combination of data science techniques and artificial intelligence (AI) to analyze the large and complex datasets collected from customers. It includes data like customer demographics, behavior, search queries, past purchases, and website or app interactions.  

Predictive marketing tools use machine learning algorithms to analyze this data and forecast patterns and trends. The insights marketers glean from predictive analytics are based on real customer data, making them more accurate for planning various marketing approaches and strategies versus traditional techniques (e.g., “gut feelings” or using broad demographic generalizations to formulate marketing plans).

What Are Some Predictive Marketing Examples?

Predictive marketing supports various marketing use cases to support the customer. It’s a practical application for leveraging both data and AI that many businesses are benefiting from. Here are some key examples: 

1. Making more relevant product recommendations 

AI-powered algorithms that analyze customer browsing history, past purchases, and demographic data are primed to suggest products that resonate on a personal level with every customer. This is an automated personalization approach that surfaces relevant recommendations to each customer. When content is personalized like this, a customer is more likely to do the thing you want them to do (buy, subscribe, listen, watch). It’s an approach used by wildly successful companies like Amazon, Netflix, Spotify, etc. since it significantly increases conversion and engagement.

2. Creating customer segments for more effective targeting

Predictive analytics use ML to identify and segment distinct customer groups. This is an automated process that uses various criteria to create different segments (e.g., customers who love beige couches or high spenders at the holidays.) Retailers can create messaging and targeted campaigns based on a segment’s likelihood to purchase, their lifecycle status, their discount affinity, or their preferred channel (plus much more).

3. Predicting and preventing customer churn 

Predictive models analyze customer behavior patterns to identify which customers and segments are likely to churn. For example, a model might flag customers that haven’t made a purchase in a while or those who’ve reduced their engagement in some way. Reduced engagement is valuable information that you can use to proactively re-engage with and market to at-risk customers.

How Does Predictive Marketing Work?

Predictive marketing uses AI and machine learning algorithms to process and analyze very large amounts of customer data. This data may include: 

• Past purchases
• Browsing behavior
• Demographic information
• Search queries
• Time spent on specific pages
• Products added to cart
• Abandoned cart data
• Email engagement
• Customer service interactions

In short, every interaction and behavior that can be tracked can also be used by AI to identify patterns and trends, then forecast future customer actions. Machine learning models make the AI system more accurate over time. 

For example, Monetate’s AI-powered personalization engine analyzes customer data like past shopping behavior and browsing history to predict behaviors like purchase likelihood, inventory shifts, and new opportunities. These insights can inform inventory management and allow marketers to create highly targeted campaigns and personalized experiences.

The process is ongoing and self-improving. As new data comes in, the algorithms refine their predictions and improve their accuracy. This continuous learning enables marketers to adapt their strategies quickly based on changing customer behaviors and market trends.

5 Benefits of Predictive Marketing That Pilots Customer Purchases

Predictive marketing offers some tangible advantages that can drive sales and improve experiences for every customer who does business with you. Benefits include:

1. Improved customer experience and satisfaction

Since predictive marketing adjusts shopping experiences based on data-informed insights, it can create personalized interactions that are relevant. This creates a better end-to-end shopping experience rich with elements like personalized search, relevant product recommendations, and targeted offers.  

2. Higher conversion rates and ROI

Predictive marketing enables you to be more precise with your marketing messaging and strategy. This precision often results in a higher return on investment for all types of initiatives including marketing, personalization, and sales strategies. In Nespresso’s case, personalization sold more coffee. They used Monetate’s Product Finder to predict what products would be the most relevant to customers and saw an 18% increase in conversion rates for the recommended coffees.

3. More efficient resource allocation

Since predicting the future (using data) reduces guesswork, it makes marketing campaigns more impactful and effective. Marketers can incorporate the most effective channels, messages, and strategies into what works best. Your market budget goes farther, metrics like cost per sale and conversion rates improve, and customers buy more. We love efficiency. 

4. Better customer retention

Using data and AI to help you see into the future makes for a much more satisfying customer experience. You can proactively address customer needs and reduce potential churn by resolving issues and roadblocks before they spiral into bigger problems. This ability to meet customers’ needs as they occur also helps you maintain stronger customer relationships, fuels loyalty and improves customer satisfaction. 

5. Outpace your competitors 

Predictive marketing gives you X-ray vision (sort of) by identifying emerging trends and behaviors. While your competitors are still reacting to last month’s fad, you’re already getting ready for what’s next. For example, a fashion brand might spot a surge in searches for “oversized blazers” weeks before it hits the mainstream, allowing them to adjust inventory and marketing campaigns well before peak blazer season.