In our age of data-driven marketing, we’re expected to make every marketing decision based on “the data.” Sounds good, right? Of course we want to choose strategies and tactics that are virtually guaranteed to get our customers to buy our product or sign up for our service! So we’ll just open up our database, and then run a few queries, and then make some charts… and hours later, we’re no closer to gleaning any valuable insights from the mountain of information available to us.

Turns out, there are a lot of ways to analyze and visualize data, and even more ways to turn those insights into cohesive marketing strategies. It’s an overwhelming job for any human. That’s why marketers are increasingly turning to artificial intelligence (AI) and machine learning (ML) to help us make data-driven decisions free from the pains of human analysis.

This approach is known as predictive marketing. Marketers are leveraging AI/ML tools to better understand how our customers are behaving and what strategies they should use to influence their behavior. Predictive models reveal nuances and patterns in data that previously required a highly skilled data analytics team to uncover.

Today, AI/ML is deeply ingrained into our personal lives and increasingly used in our professional lives. We open our phones with our faces, spend hours binge-watching recommended content, and write our emails with computer-suggested sentences. What’s next for predictive marketing?

1. Predictive Marketing Is Becoming More Democratic

Predictive marketing was once the exclusive domain of large corporations with lots of resources. That’s starting to change, which is good news for scrappier marketing teams who want to leverage predictive analytics in their marketing campaigns.

In the early days of predictive marketing, AI/ML models required a tremendous amount of data, time, and specialized skills to create and run. We’re seeing a shift now to more self-service predictive models that require very little front-end setup, meaning marketers don’t need to be data or coding experts to leverage these tools ourselves. Marketers simply add a few inputs (e.g. which behavior to predict, which customer segment to target) and the model does the rest of the work on the back-end.

What about the data itself? Generally speaking, AI/ML models can more accurately predict future customer behaviors when they have more data to analyze. That again benefits larger companies with their vast troves of data. The tide is turning, though: As AI/ML tools become more advanced, they’re better able to make predictions using less data. That opens the door for small- and medium-sized businesses to take advantage of predictive tools to level up their growth strategies.

The takeaway: Predictive marketing is increasingly available to marketing teams of all sizes as AI/ML tools become more efficient and self-service.

2. Ai/Ml Tools Will Be More Integrated Into Day-to-Day Marketing Flows

As access to predictive marketing tools increases, it’s only natural that marketers will look for ways to seamlessly incorporate predictive analytics into marketing campaigns. This will lead to rising demand for easy-to-use AI/ML products that tie directly into campaign orchestration platforms.

The current state of predictive marketing technology is that the “predictive” and “marketing” sides are often siloed across separate systems and sometimes separate teams. The data is analyzed in one system, and then exported to another system for use in marketing campaigns. This results in the same problems that every siloed martech stack has: Inefficiency, data latency, and inaccuracy.

The future here is clear: As more marketers seek full control of predictive marketing strategies, we’ll look for products that support these use cases. Marketers want the ability to create, run, and act on predictions all in one place, and platforms will respond by seamlessly combining predictive marketing tools with campaign orchestration tools. It makes sense—predictive marketing models are often used to predict customer behavior (for example, likelihood to churn or likelihood to purchase), so why wouldn’t you want to start messaging those customers right away?

The takeaway: Marketers don’t want any old predictive marketing system; they want easy-to-use, integrated tools that don’t rely on additional teams or systems.

3. Marketing Campaigns Will Become Increasingly Creative and Relevant

What will marketers do with all this free time once predictive marketing tools are handling some of their biggest challenges? For one thing, they’ll finally have time to flex those creative muscles!

The rise of predictive marketing will align with a rise in creative marketing. AI/ML will reveal previously hidden signals in our customer data that will help us deliver increasingly relevant customer experiences that delight customers at every stage of the customer lifecycle. Customers will get promotions for products they didn’t know they needed, or a timely check-in before a little problem becomes a big one. AI/ML will tell us who to target and when, so that marketers can focus more on the how.

Ironically, the rise of AI/ML will also contribute to more human customer engagement. With predictive technology detecting problems earlier and directing customers to solutions without the need for slow human intervention, customer engagement teams will be able to provide personalized support to those who need it most.

The takeaway: As marketing becomes more reliant on AI/ML, it will open the door for more relevant, creative, and human customer engagement strategies.

Final Thoughts

Predictive marketing sounds intimidating, and it certainly can be if you have the wrong tools and strategies! Fortunately, the future of predictive marketing is one that is increasingly accessible and easy to use, so that marketers can spend time designing creative and human experiences for customers.

Of course, good AI/ML depends on good data. Read more about effective data management in the Maximize CX and Personalization Efforts by Focusing on Privacy and Data Management survey report, and then start putting your data to work with the Braze Predictive Suite.