Predictive marketing 101: What is it and how to utilize it

Learn all you need to know about predictive marketing and how generative AI and a customer data platform play a role in enabling businesses to succeed.  

Content Marketing Specialist

Monika Karlović

Content Marketing Specialist

If you’ve ever played a game of chess, you can appreciate that at the core of a good game is an excellent strategy. No matter if you’re a beginner or an advanced player, you most likely have some kind of plan on how to approach each game. 

You look at every piece on the board, consider their strengths and how you can use them to your advantage. But most importantly, you look at what moves your opponent can potentially make, how you can capitalize on them to bring home a win.

Predictive marketing is a lot like that. You need to have a strong strategy in place and try to predict what moves or actions customers will make so that you can guide them in the direction you want them to go.  

Let’s break down all the essentials of predictive marketing and how you can leverage it to strengthen your campaign success. 

What is predictive marketing?

Predictive marketing is a data-driven type of marketing strategy. It uses machine learning and customer data to forecast and anticipate customer behavior, preferences, actions, and trends so that brands can optimize their marketing campaigns.

Predictive marketing helps brands: 

What tools do you need for predictive marketing?

There are two main tools you will need to get started on creating predictive marketing models: 

Customer data platform (CDP)

The data you collect on your customers is critical in helping you determine how to target them and what their future actions might be. Using a customer data platform (CDP) will help you collect a wide range of information such as:  

  • Demographics: Name, age, location 
  • Shopping behaviors: Items added to cart, items returned 
  • Interactions on various channels: Web, chat app, call center interactions 
  • Social media behavior: Likes, saves, comments 
  • Transactional data: Purchases and returns 

All this data comes together in a CDP to create detailed customer profiles that allow brands to better understand their customers on a more personal level so they can better predict how to segment them effectively. 

Why do you need such detailed profiles for customer segmentation?

In the past, many brands focused on demographic data only. Take a look at the demographic data collected on two people with no other context:  

  • Male 
  • Born in 1948 
  • Born and raised in the UK 
  • Married twice 
  • Wealthy 

You might think that the two people who share the same demographic data would fall into the same segmentation group for a campaign, but one of them is Prince Charles and the other is Ozzy Osborn. Not two people you would target with the same messaging or campaign.  

Generative artificial intelligence (Gen AI)

A lot of buzz has been created around generative AI – and with good reason. It uses algorithms to instantly create anything from text, images, voice overs and more – mimicking human creation. Gen AI can play a major role in improving how businesses segment their customers and how they communicate with customers.  

For example, Gen AI chatbots can enable conversational, human-like and personalized conversations with customers by using contextual information from their CDP profile.

Using these two tools in unison will enable brands to accurately predict what their customers are looking for, and easily generate interactions with them on various messaging channels.  

For example, your customer is a mom who is shopping for kids’ costumes and who has abandoned her cart. You also know she is active on Instagram, so you target her with a sponsored advertisement on the social media platform that directs her to your WhatsApp channel where she will interact with an AI chatbot that will guide her to purchase. 

Benefits of combining CDP data with gen AI for predictive marketing

4 examples of predictive marketing

1. Personalized product suggestions

If you’ve ever searched for a topic or product and then suddenly start to notice related advertisements appearing on your social media, that’s one tried and true way brands are using predictive marketing.

These models help predict what products you might be interested in based on how you’ve interacted with brands in the past. Brands can target segmented audiences on various channels, from social media to email campaigns, depending on the customers’ preferences.

What to do

Based on the data you collect on customers, you should be able to accurately predict what products or services are relevant to them.  
A great example is Netflix. They’ve mastered the formula of personalized suggestions and can target you based on what you’ve watched, and how much you watched to curate your homepage to your liking. 

What to avoid

Personalization is great – until it becomes creepy. Target, for example, went too far by creating an algorithm that could predict if a woman was pregnant. They received backlash for this, as many women hadn’t told their families before Target was sending baby coupons to their address or emails. Personalization is good, just don’t cross the creepy line. 

2. Churn prediction

It’s well known that acquiring new customers is much more expensive than retaining your existing customers. If you can accurately forecast who is on the brink of churning, you can hone in on that specific customer segment and launch marketing efforts that can help boost loyalty and retention.  

What to do

Segment your customer base into meaningful groups to tailor retention strategies. Segmentation helps reduce churn by targeting the right customers with the right interventions. 

What to avoid

Don’t rely heavily on one type of data, such as demographics. You must have a holistic view of your customers and pinpoint who is at risk of churning, and segment them accordingly. 

3. Customer journey prediction

Do you know what your customers’ next move will be? Or what path of the customer journey will they take? Understanding and accurately predicting their actions can help brands improve experiences by giving customers exactly what they are looking for. 

What to do

Collect data from different sources such as your web or app, chat apps or voice interactions so that you can create a full holistic view of the customer journey. This also helps with understanding the likelihood of them taking different paths during their journey to purchase, allowing you to prepare accordingly.

What to avoid

Don’t expect your customer journeys to be permanent or even that every customer will follow the same journey. Customer behaviors and preferences change often, so your journeys need to adapt and prepare for various choices customers might make during their journey to purchase. 

4. Lead scoring

Predictive lead scoring helps brands focus their resources and efforts on their most promising leads. Brands are able to determine the likelihood of a lead converting and avoid overspending on campaigns with no return. 

What to do

Regularly monitor and adjust your predictive lead scoring model to ensure you are getting the most accurate results. You must train AI to be able to do what you need it to, and regular up-keep of your tech will help you get the most accurate picture of lead potential. 

What to avoid

Don’t collect or use lead data in any way that violates local privacy laws or regulations. If you prioritize and respect data privacy and your customers’ opt-in preferences, you will ensure your business can continue to send compliant messages without any issues.

Common challenges for predictive marketing

Data privacy

Clean data

Model bias


Data privacy and security

Since generative AI and CDPs must process large amounts of data, privacy and security can become a concern. It’s important that your solution provider is equipped to implement data privacy and security measures to protect your customers. 

Data cleaning

High-quality data is essential for generative AI models to produce quality interactions for customers. Consolidating data from various sources through CDPs can enhance data quality. However, it is crucial that data you are using is high-quality and accurate for the training and operation of your generative AI model. 

Model bias

Generative AI models reflect a lot of characteristics of the people who train them – which means they carry the risk of reflecting a bias. Using a variety of techniques such as adversarial training can help counter that risk. Adversarial training involves purposefully trying to trick your gen AI algorithm by feeding it data that will likely give a poor or problematic output. This way, you can train your tools to avoid biases and give more accurate and realistic communication.


Integrating generative AI models and CDPs can be very complex and challenging. Having an experienced solution provider help with the integration process can enable you to go to market faster and with less hiccups. 

[ Trusted solution provider ]

The Infobip advantage.

Easy-to-integrate APIs. Custom integrations. 40+ data centers. 24/7 support.

What you should know about the future of predictive marketing

Even if you are just starting to become curious about predictive marketing and how it can benefit your business, it’s important to keep one eye ahead and understand where this is headed in the future.  

Gen AI and CDPs to create new specific use cases

Generative AI and CDPs can be used to create new use cases that are specific to your industry and customer base. Don’t limit yourself to what is already out there. Take advantage of tailoring your gen AI models with customer data to create unique use cases that help you stand out.  

Coding for dummies

Low-code or no-code solutions are becoming increasingly popular. You no longer need to be a coding expert to offer elevated experiences to customers. This enables a wider range of businesses more accessibility to gen AI solutions. 

Integration of gen AI and CDP with other technologies

Generative AI and CDPs are powerful in combination. But more and more businesses are integrating the two with other solutions as well. From marketing automation platforms, customer relationship management (CRM) systems, and enterprise resource planning (ERP) systems, this trend is allowing businesses to create a more unified look at their journey and customer base and offer even better experiences. 

Get ahead of the curve with predictive marketing

Utilizing gen AI and CDPs for predictive marketing will give you a competitive advantage with customers. Boost engagement and ROI, reduce ad spend and provide customers with 24/7 communication by automating and personalizing interactions across the board.

These tools are the future of marketing in a digital world. Stay ahead and learn more about how gen AI can help you reach your business goals.  

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Content Marketing Specialist

Monika Karlović

Content Marketing Specialist