How to Use Predictive Analytics in Marketing: Examples, Tips, and Tools (2024)

If you could look into the future, what would you like to see? 

For businesses and marketers, being able to predict future outcomes is a game-changer. 

After all, it would solve most of your problems, like how much you should spend on ads or which products actually deserve your marketing budget. 

And, if you know a thing or two about data analytics, you'll know that this is more than possible. 

In this post, we'll cover everything you need to know about predictive analytics — what it is, why it's important, and which tools are perfect for the job. 

Let's get started. 

What is Predictive Analytics in Marketing?

Predictive analytics takes past and present data to forecast future outcomes. Statistical methods and software can be used to turn data into predictions, including but not limited to machine learning, predictive modeling, big data platforms, data mining, and optimization algorithms. 

In marketing, you can use predictive analytics to make data-driven decisions and optimize your marketing plans. 

Let's take a look at some specific benefits: 

• Ramp up marketing personalization. Predictive analytics lets you refer to customer data, including previous purchase history and in-store activities, to provide more personalized marketing experiences. Some examples are product recommendations, newsletters, special discounts, and personalized ads. 
• Improve customer segmentation. With predictive analytics, you can translate customer data into intelligent segmentation and clustering decisions. This allows for more impactful customer experiences and interactions that bring them closer to a purchase.
• Reduce Customer Acquisition Costs (CAC). Use predictive insights to identify and prioritize prospects who are most likely to convert based on past sales data. At the same time, you can save money by skipping leads who don't fit your ideal customer profile. 
• Stay ahead of consumer trends. Predictive analytics can utilize information from a diverse set of data sources to preemptively identify market trends, like stock prices, social media hashtags, and surveys. Learning and leveraging emerging trends before anyone else will give you a competitive edge.
• Make use of customer data without cookies. After a long wait, Google will officially end third-party cookie support in Chrome by the end of 2024. That means businesses need to start building up their first-party data repositories and rely on technologies like Machine Learning (ML) and predictive analytics to make data-driven marketing decisions. 
• Reduce customer churn. Losing existing customers is more financially significant than gaining new ones. That's why businesses use predictive analytics to identify the biggest factors that lead to customer churn and single out customers at risk of attrition.    

The Role of Predictive Analytics in Data Analytics

Predictive analytics is just one phase across the multiple stages of data analytics. 

It comes after descriptive analytics and diagnostic analytics — right before prescriptive analytics, which is the stage where insights are molded into actionable marketing strategies. 

Descriptive analytics measures past and current marketing campaigns, pinning down metrics and Key Performance Indicators (KPIs) and compiling them into an accessible database.

Diagnostic analytics takes a dive into your database and identifies the root causes and other influential factors that contributed to your marketing performance. 

Predictive analytics then utilizes descriptive and diagnostic data to estimate the future values of metrics and KPIs based on current campaign configurations. 

Finally, prescriptive analytics relies on machine learning, Marketing Mix Modeling (MMM), and statistical techniques to plan marketing scenarios and strategies for achieving target outcomes. 

The stages of data analytics rely on various data collection, analysis, and visualization methods — enabling businesses to take on a more holistic approach to data analytics. Just remember that each stage is designed for a specific objective, namely measuring data (descriptive), identifying the reasons behind events and outcomes (diagnostic), predicting future results (predictive), and drawing up actionable plans to accomplish marketing goals. 

Examples of Predictive Analytics in Action

The best way to learn and appreciate the value of predictive analytics is to look at some real-world examples. 

Here are some modern implementations of predictive analytics you can learn from: 

1. Facebook Ads

Facebook or, more specifically, Meta Ads use predictive analytics and Machine Learning in their ad targeting tools. This makes it easier for brands to reach their ideal audience with minimal inputs. 

2. PlayStation 

PlayStation wields big data and predictive analytics to push product recommendations to their players via email. Customer behavior data is collected from each user's shop browsing history, played games, and other types of shared in-game data (which customers agreed to prior to using the service). 

3. Spotify 

Spotify utilizes predictive analytics to enhance the user experience in a couple of ways. This includes playlist recommendations, "Smart Shuffle," and daily mixes. 

4. Nike

Nike uses data collected from their app, along with data from their consumer data analytics arm Zodiac and Internet of Things (IoT) devices to serve product recommendations to customers. The company also acquired Celect, which is a Boston-based predictive analytics firm to forecast product demand and optimize inventory management. 

How to Do Predictive Analytics: Step-by-Step Guide

Ready to take a stab at predictive analytics? 

Follow the steps below to make the most out of predictive analytics for marketing:

1. Define your data analytics question

First things first, you need to define your data analysis questions to guide the direction of your predictive analytics. 

Of course, these should be questions about future marketing results, such as: 

• "Which products are more likely to sell in the next 30 days?"
• "Which ad sets will generate the most Marketing-Qualified Leads this holiday season?"
• "Who among our prospects are days away from a purchase decision (based on brand interactions)?"

2. Data collection

After defining your predictive analytics goals, you'll be able to identify the important datasets, metrics, and KPIs you need to track. More importantly, they will point you to the data collection methods you need to employ to acquire essential information. 

For example, if you're planning an advertising campaign for the holiday season, you should prioritize products that are historically in demand this time of year. That means you need to analyze at least a year's worth of sales data, market trends, customer feedback, and competitor ads. 

3. Data processing and analysis

Next, it's time to process and analyze your data to obtain relevant insights. 

Start with data optimization to prevent your analysis from being skewed by outliers, duplicates, misformatted data, and the like.

As a rule of thumb, prioritize first-party data with which you have firsthand control over quality. It's also important to standardize your data to make streamlined data analysis techniques possible. 

Finally, it's time to build data visualizations to make the blend of information readable to key decision-makers. Take a look at your dataset to determine the best visualization types to use.

For example, you could use line charts to visualize product sales over time. 

4. Developing your model

At this point, you're ready to develop and deploy your predictive model. 

You can use ML tools or Marketing Mix Modeling to forecast, along with statistical techniques like linear regression, to forecast future outcomes based on historical data and fine adjustments to your marketing strategy. Be sure to use data as the backbone for your decision-making — not gut feelings.

5. Launch, monitor, and iterate

After planning and launching your prediction-adjusted campaign, don't forget to monitor your results. 

Put a pin on your KPIs with tools like scorecards and pivot tables. You can also use correlation charts, like scatter plots, to determine correlations between performance and specific marketing activities. 

This is where a dashboarding tool like Polymer can really make a difference. 

Predictive Analytics Tools 

Before we conclude this guide, here are some tools to help bring your predictive analytics visualizations together: 

1. Polymer

Polymer is a top-tier Business Intelligence (BI) and dashboarding platform with flexible data visualization tools. In addition to the drag-and-drop dashboard editor, it also comes with a handful of AI tools to streamline data analysis.

This includes: 

• PolyAI — Ask data-related questions or instruct the AI to generate visualizations using your dataset. 
• Suggested Insights — One-click visualization suggestions as you work on your dashboard. 
• Block suggestions — Real-time suggestions while creating your data visualizations.

2. Tableau

Tableau is also among the best BI software for businesses in the market. Some of its extra features include AI-driven workbook optimization, easy-to-use templates, and a mobile app for accessibility. 

3. Adobe Analytics 

Adobe Analytics lets you collect marketing data across all channels and process them with advanced visualization tools. It comes with built-in predictive intelligence to quickly turn data into forecasts, opportunities, and answers to your data questions. 

4. Qlik Sense

Qlik Sense  is a business analytics platform with heavy emphasis on AI and predictive analytics. The goal is to consolidate data from all your marketing channels, have AI work on your predictive model, and convert insights into readable visualizations — providing one of the easiest predictive analytics workflows available. 

Make the Most Out of Predictive Analytics with Polymer

Predictive analytics is a crucial stage in data analysis that will help you take a data-driven approach to marketing. But, just like with all things data, it's best handled with the right tools for your business. 

Polymer has everything you need to consolidate your datasets, manage your data, create visualizations, and translate data into digestible insights.

Get started with Polymer for free here.