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predictive analytics in marketing tutorial 2023

Predictive Analytics in Marketing: Ultimate Tutorial For Beginners

Predictive analytics in marketing lets you recognize and anticipate customer needs. It combines big data, artificial intelligence, and machine learning models to identify customer expectations and improve sales. 

Basically, it answers your questions like: 

“Who is my target audience?” 

“What products do they typically look for?” 

“What might be their next step?” 

If you want to gain that intelligence with technology and improve marketing tactics, this guide is for you. In this detailed article, we will reveal what predictive analytics is, its role in marketing, and the 6 step process of creating it. 

What is Predictive Analytics? 

Predictive analytics means using historical and/or current data with statistical techniques like data mining, machine learning, and predictive modeling. Marketers use predictive analytics to assess the likelihood of a specific event happening in the future. 

In other words, predictive analytics helps you to answer, “What is likely to happen in the future?” In businesses, credit scoring is one of the most intuitive applications of predictive analytics. Banks often use historical data about a person’s past payments, credit history, and other financial parameters to calculate if they are likely to make payments on time. 

If you are still unsure how the technology helps businesses, let’s analyze Netflix. They pulled historical data about their previous TV show’s success to create the award-winning series – House of Cards. But how can predictive analytics help in developing effective marketing strategies? Let’s talk about it in detail below! 

What is the Role of Predictive Analytics in Marketing? 

Since the predictive data analytics market is growing at a 23.2% rate year over year, it’s a matter of time before every top company starts using predictive analytics in their marketing efforts. 

But before you get started with the technology, it’s essential to kick off your education in the field. In simple words, predictive analytics is a form of advanced analytics that leverages leading technologies like machine learning, artificial intelligence, and historical data to make predictions. 

While the concept has been around for some time, organizations have recently started leveraging its benefits. 

Marketers, in particular, use the power of historical data to predict marketing trends and create highly personalized marketing campaigns. 

5 Examples of Predictive Analytics in Marketing 

Predictive analytics ensures the right message is delivered to the right audience at the right time. For instance, when a customer purchases a ticket, the data gets added to the ticket seller’s computer system and entered into the database. This helps them understand which movies are popular and when customers are going to make their purchases. 

Below are five more examples of predictive marketing analytics. 

Customer and Audience Segmentation 

Predictive analytics in marketing lets you segment the audience based on behavior, firmographics, demographics, and interests using cluster modeling. 

Marketers can experiment with different cluster models to find patterns that they may not have expected. Thus, it helps brands find audience segments that help them make the most of their advertising campaign. 

Product Development 

One of the best ways to stay ahead of the competition and generate better revenue is by accurately predicting which products will be in demand. 

One leading cosmetic brand that used predictive analytics and AI in product development was L’Oreal. The beauty brand uses Synthesio to predict beauty trends at least 6 to 18 months before they emerge. 

Synthesio collects data from beauty forums, YouTube, fashion blogs, popular social media channels, and other 35,000 online sources to predict packaging, ingredients, and lifestyles that will be trendy in the near future. 

Uplift Modeling 

One of the best ways to make your advertising campaigns successful is by predicting their outcome. With the advancement of technology, marketers can use data crunching assisted by machine learning models to process vast amounts of data and reduce the time it takes for model campaign uplift. 

Lead Prioritization

Generally, the in-house marketing team in a business needs to manually analyze data related to users to prioritize leads. 

Not only does it consume a substantial amount of time, but it also leads to delayed decisions and missed opportunities. 

Predictive analytics in marketing allows companies to accelerate the process, improve the reactive decision-making process, and boost conversions. 

Churn Prediction 

In the marketing realm, a churn rate is inversely proportional to customer satisfaction. In simple words, the higher the churn rate, the lower the customer satisfaction. 

With the help of predictive analytics for marketing, businesses can accurately estimate the churn rate probability for a given customer early. They can then take proactive measures to reduce churn and generate better revenue. 

Bonus: Ad Personalization

The more the relevancy of the ad, the better the click-through rate (CTR) and performance. Predictive analytics in eCommerce help marketers craft high-performing creative for audiences with different interest rates. Each marketing campaign uses real-time customer data to create personalized ad campaigns that generate results. 

In addition to successful marketing, predictive analytics is used in supply chain management. Read our detailed predictive analytics in supply chain management tutorial to understand how to use the technology in other areas of business. 

6-Step Predictive Marketing Analytics Process 

Each data analytics project is different due to the distinctive data sets used and the high level of customization. However, the steps to create predictive marketing analytics processes are similar. 

Project Definition 

Before you jump into your business data, you will need to define a clear idea of what you are doing and how you are planning to achieve it. The question you need to focus on in this step is, “What is likely to happen in the future based on what’s happened before?” 

Data Collection 

The next step is to collect relevant data that will help answer the question of the first step. Let’s say you want to understand “Which people are likely to buy tickets within the next 30 days?” In this case, you have to collect data related to potential buyers, such as their firmographics, demographics, and other relevant factors and parameters. 

Data Processing 

Once you have the complete data you need, it’s time to start crunching. You can use a data analytics tool to mine relevant information and find answers to the questions. 

Test Hypotheses 

Now that you have the list of questions and have gotten into the crunching mode, it’s time to test your hypotheses. Double-check and test all your hypotheses and go with your data. 

Develop & Deploy Model

After testing, you are all ready to develop and deploy a model. In order to create a predictive model, you’ll require an expert who is skilled in Python or R languages. If you are unsure how to develop a predictive model based on the collected data, consider contacting Inferenz experts. 

The team of skilled data analysts and engineers can help you streamline the entire predictive marketing analytics process. Once the existing predictive model is developed, you can put the results into practice and start creating marketing campaigns that generate results. 


Once the data processing is completed via statistical algorithms and models, you can interpret the data and get answers to questions asked at the beginning of the process. If you want to get a better understanding of the predictions, consider leveraging data visualization techniques. 

Get Started With Predictive Marketing Analytics Today 

Remember, there are multiple use cases for predictive analytics in marketing. From helping companies analyze the high demand product to customer behavior and optimizing price and promotion, predictive analytics can do it all. The trick, however, is to ensure you have all the marketing data in your data warehouse for quick access. 

Confused about which data warehouse can suit your business requirements? Check out our detailed comparison article, where we reveal the top 7 data warehouses like Snowflake, BigQuery, Redshift, and more. 

If you are perplexed about how to predict marketing trends and optimize marketing spend based on historical data, contact Inferenz experts today. The skilled and professional team of data analytics can help you figure out what the technical and business requirements are. 

Get in touch with the expert team today to get started with predictive analytics in marketing.

contact inferenz to implement predictive analytics in marketing