Predictive AI trends for customer-obsessed brands in 2024

Predictive AI Trends for Customer-Obsessed Brands in 2024

We hear a lot about AI these days, and it’s tough to weed out what’s conjecture and what’s an insight based in reality.

How exactly are enterprise marketers using AI for engagement now – and how do they expect to use it in the year ahead? 

To help answer this question, we fielded The Customer Engagement & AI Survey with Ascend2 in Q4 2023.

Professionals from companies with 500+ employees shared their top successes and challenges when it comes to engaging customers with AI, and MessageGears VP of Marketing, Will Devlin, recently sat down with CMO Natalia Dykyj to walk through the results.

Here are some of the findings along with insights into what they mean for marketers. 

99% of Enterprise Marketers Agree: AI Works

Most marketers told us that AI-driven marketing automation has resulted in moderate to significant cost savings for their company. It’s hard to find anything that a full 99% of people can agree on these days, but that’s how many marketers say that their ability to understand customer preferences and behavior has improved in some way thanks to AI. 

As a CMO, Natalia shared that she wasn’t surprised to hear how high adoption rates are for AI.

“2023 was definitely the year of hype around AI, but remember that marketers, especially at big enterprise companies, have been using it for years now,” Natalia said. “I think probably the best example is that savvy data science teams have been using AI, especially in their modeling and their analytics.”

Here at MessageGears, we work almost exclusively with large, enterprise-level customer-obsessed brands with millions of customers. These companies are under a lot of pressure to drive revenue and reduce costs, and predictive AI can be their secret weapon for managing campaigns with many moving parts. 

The most basic predictive AI model among brands like these is also the most sought-after: Send Time Optimization. Looking at past customer behaviors, companies can predict when customers will be most likely to engage with their message. That timing is hugely important given how many emails are inboxes every day.

Purchase propensity is another very popular use of predictive AI. Running through the universe of customers, AI looks at factors like browse, opens, and purchase history, and scores those customers for a certain timeframe based on their likelihood to buy. Brands can then target and better personalize their campaigns based on that likelihood to buy.

Along with usage of these predictive AI models, brands are reporting some really exciting results. Campaign conversion rates are up around 25% along with major improvements in cost per acquisition, average order size, and repeat purchases.

Where can AI help move the needle?

AI is most commonly used to enhance targeted advertising campaigns, personalized email marketing, customer support and service, and customized product recommendations.

In response to our survey, 97% of marketers said that AI is effective at helping them deliver personalized content and recommendations to customers.

Marketers told us that the most helpful uses of AI are (or would be):

  • Determining which customers are most likely to make a purchase
  • Determining the channel most likely to convert customers
  • Determining prospects’ likelihood to engage with your brand
  • Determining the best day/time to engage with a customer

As a CMO, Natalia shared that predictive AI is an especially exciting type of AI, not only for its ability to drive better experiences for consumers but also thanks to its ability to drive meaningful performance. 

There are four main categories where marketers see predictive AI models making an impact on a daily basis:

  • Increasing customer loyalty – AI is helping predict key customer lifecycle events to drive repeat purchases and spark engagement with your brand
  • Optimizing paid media investments – AI is helping campaigns get higher conversion rates and also telling marketers which campaigns to pull back on or even stop because they’re ineffective
  • Reducing churn – AI is helping marketers identify and engage with at-risk customers differently to help retain them,
  • Increasing personalization – AI is helping analyze all the data around product, purchase, and channel preferences to deliver more relevant and powerful content

Some of the most exciting options, though, come through the combination of predictive models. For instance, brands are able to trigger churn prevention email sequences and do better on-site personalization for customers who are cooling off.

What’s preventing you from using AI?

Most marketers in our survey said they believe AI will be a crucial part of their future marketing strategies.

There are still challenges in implementation though. The top reported barriers are limited expertise, staff training, integration complexities, and budget/resource constraints.

Natalia’s advice for brands?

“Wherever you are in the evolution of your AI strategy or plan or execution, pause and think about what are the hurdles that stand between you and success and how can you best overcome them,” she said. “And then you’re going to have to be open and direct with your leadership about it.”

Along with these barriers to entry with AI, brands need to know that predictive AI models are only as good as the data put into them. There are a lot of vendors out there that tout their AI capabilities but their models only run on a limited data set that you’ve copied into their system. You need access to a more complete data set, and including real time behavioral data, sensitive data, all the data to maximize the effectiveness of these models. 

Keep in mind that every brand is different, so what works for one may not be the best approach for another. You may prefer to have your data science team build models that you can run in your customer engagement platform. Flexibility is important so that you’ll be able to find your brand’s best path to success with AI.

If you’re looking to turn AI-powered insights into smarter marketing decisions at your brand, MessageGears can help.

We’d love to show you how you can use predictive insights powered by your entire data set to up-level your marketing strategy across the lifecycle. 

About the Author

Elsbeth Russell

Elsbeth has nearly two decades of experience helping brands attract and engage audiences through content. For the past six years, she’s been dedicated to helping B2B companies in the email marketing space connect with audiences through community building and social media marketing.