This article was originally published by The Drum.

AI is propelling predictive marketing to new heights. Brands need to up their game to exceed customer expectations and meet new business requirements. Tom Zawacki, chief growth officer at Acxiom, explains how AI is driving the next wave of business transformation and how brands can excel at predicting what their customers want.

We live in a predictive world. Our smartphone assistants anticipate what we will say and finish our text messages for us. After binging Yellowstone, our streaming services predict what we want to watch next. Retail websites frequently offer what we want to buy before we realize we want it ourselves… did I really need that lava lamp?

These predictive experiences are made possible by using data about us (and people like us) and AI algorithms to anticipate our needs. And we, as consumers, are increasingly comfortable with our data being used this way. Acxiom’s latest CX trends research reveals more than half (54%) of consumers find having their data used to present relevant product ads useful, rising to almost two-thirds (64%) in the 16-34 age group.

The predictive expectation is set

In fact, we’re not just getting comfortable with predictive experiences, we’ve come to expect them. Not so long ago, we knew booking a vacation would require a little research into suitable locations, accommodation types, and transportation options. Now we expect travel brands to present us with packages that meet our individual needs because they already understand who we are (family of four) and what we’ll want (all-inclusive beach vacation with a kid’s club). Equally, predictive experiences increase our productivity or, said differently, reduce the amount of time we spend accomplishing tasks. We’re no longer prepared to wade through financial products and services to find the most beneficial. We expect comparison apps, banks, and credit card companies to present our best options based on our historical value and current situation.

With the expectation of productivity and personalization set, brands must improve and change their marketing strategies and infrastructure to win in a predictive world. This shift is critical to the next stage of business transformation. The first stage was triggered by the emergence of the Internet when brands had to transform from an analog business to an e-business and adapt to e-mail, e-commerce, and digital advertising. The second stage, necessitated by social and mobile, required brands to adjust to a distributed economy across small screens and influencer marketing.

Now, the explosion of AI is driving a third wave of business transformation. Predictive marketing isn’t an entirely new concept, but rapid advances in AI, bandwidth, and processing power are shifting it from a promise to a marketing necessity.

So what can marketers predict?

In addition to anticipating customer needs, predictive marketing enables brands to maximize ROI on their marketing spend. Once again, data and AI modeling are leveraged to develop predictive models governing our marketing efforts and helping us to make smarter decisions. Consider the four C’s of predictive marketing:

Customer: Brands can predict which audience will be most receptive to their message. For an acquisition program, they’ll predict which prospects have the highest propensity to convert. For a campaign to increase lifetime value, they’ll predict which customers would welcome an upsell or cross-sell. And sometimes even more important, we can predict which audiences a brand should not reach, thus saving costs and improving media efficiency.

Channel: Brands can predict which channel is going to be most effective in delivering a particular message or reaching a particular audience. Before a dollar in the budget is spent, we can maximize investment allocations across TV, e-mail, digital, social, direct mail, or other possibilities in the ever-growing omnichannel media landscape.

Creative: Brands can predict which creative experience will be most effective for those customers, on that channel, at that time, to achieve the desired outcome. In addition, generative AI enables us to develop creative at scale to realize personalization across multiple channels, lowering production costs and improving response and conversion rates.

Conversion: Brands can determine which offer or incentive is most likely to gain a response from the customer based on historical lifetime customer value. Value might be recognized through early access to an exclusive product, a discount, or a loyalty-based reward.

Marketers can get the best possible results from their programs by applying predictive modeling in these four areas. And they don’t need to do everything at once. Even small improvements can impact the variables that contribute to ROI and boost overall performance.

A holistic framework for predictive marketing

Many marketers are already utilizing aspects of predictive marketing and finding great success. Here are the elements you’ll need to support a holistic predictive marketing strategy:

An accurate and comprehensive data foundation

Effective predictive marketing is only possible with an accurate foundation of data and identity from which AI can extract the insights required for accurate decision-making. Brands will almost always start with their own first-party customer data, and then enrich that with second and third-party data from trusted partners and providers. There are millions of variables and billions of signals that can be used to anticipate what customers want, and you need to know how to put that data to work.

An augmented intelligence layer

On top of an accurate data foundation, you’ll need to employ augmented intelligence – a combination of human intelligence and AI. We’re well past the stage where humans can process data at the speed and volumes required for predictive marketing. But there are too many risks when AI is left to its own devices, including hallucinations. Plus, our research reveals that 78% of consumers believe some form of human interaction is still essential for a good customer experience. Augmented intelligence allows humans to curate the experiences enabled by AI, while also helping brands retain ownership of the intellectual property in their predictive models. ‘Humans in the loop’ are also a key component of responsible AI use and governance.

Omnichannel excellence

Based on the insights derived through data and intelligence, marketers can then make smarter decisions about which audience to reach, across which channel, with personalized messages that drive high ROI. Not all decisions have to be made in real-time, but rather at the right time.

A measurement and optimization loop

Measurement and attribution are essential ingredients for predictive marketing, as they allow you to gauge how accurately you predict customer desires. When this data is fed back to train AI models, the result is continuous optimization, allowing the predictive model to become increasingly intelligent and precise.

Integrated supporting infrastructure

There are some incredible point-solution AI tools that will predict, for example, how you should allocate your budget across channels to get the highest response rate from a particular audience. Those tools need to be supported by the right infrastructure and intellectual property governance. Predictive marketing best practices necessitate the coordination and integration of martech and adtech platforms internal to the enterprise as well as external ecosystem partners.

Meet your customers in the predictive world

AI can help meet consumer’s expectations in an increasingly predictive world, but it’s not the only thing your brand will need to succeed. Exceptional predictive marketers have an AI-ready data foundation, augmented intelligence, attribution loop, and an integrated infrastructure throughout which AI should increasingly be woven. Focus on these things and your brand will be in a great place to ride the next big wave of business transformation and join your customers in the predictive world.

This article was originally published by The Drum.