Using AI to improve the customer experience

As the technology improves and brands find success using artificial intelligence (AI) to improve the customer experience, AI is quickly moving from trendy concept to a valuable revenue-producing tool.  

According to our recent survey of marketing pros from enterprise brands, 61% of marketers are already using AI to connect with audiences, and another 25% are gearing up to start leveraging it this year.

While an overwhelming 97% of those using AI are finding success delivering personalized content and recommendations, nearly half also said that limited expertise is holding them back from fully implementing AI for customer engagement.

To help as brands try to find their stride with AI, we sat down with industry veteran and MessageGears CEO Roger Barnette to get his thoughts on AI and how enterprise marketers can benefit from it. 

When people hear AI they’re often thinking about generative AI. Can you speak to the differences and how brands can utilize both types?

Generative AI gets a lot of buzz – largely because tools like ChatGPT are so accessible at a user level.

At the same time, generative AI is difficult for large brands. Big enterprises with millions of customers have particular concerns about the content they send customers. Does it fit their brand voice? Does it represent their brand well? Does it actually meet the customer’s needs at that moment?

You hear a lot of conversation around ‘safe’ generative AI or ‘putting guardrails around your generative AI’ and tools like Phrasee are helping to pave the way by testing and establishing these important guidelines.

Over time technology will get better and brands will be able to keep messaging on brand at a larger scale.

What is the best way for brands to use AI to improve the customer experience?

Brands need to be good at using AI to understand their customers and what’s driving their behavior, and then ultimately what’s the best content for them to receive.

MessageGears AI helps brands figure out things like churn propensity, most valuable customers, most likely to repurchase, most likely to make a second purchase. Meanwhile, our partners like Phrasee and MovableInk have AI tools to help determine what content you should deliver to the individual.

The combination of those two is especially powerful.

Any cool examples you’ve seen where predictive AI has improved customer engagement for large enterprises?

There’s a great story about a luxury retail brand that drove a 3% increase in incremental revenue across the enterprise – representing an eight-figure dollar impact.

This brand struggled with “one and done” purchase behavior and wanted to build customer loyalty and repeat business. They had significant intelligence on customer purchase behavior, but the team wasn’t able to use any of it to influence marketing.

Using predictive AI, they were able to turn that data into learning signals and score customers based on their likelihood to make a second purchase. They were then able to send targeted messages in an A/B split after the first purchase through email and social media. This allowed them to easily measure the messaging and investment strategy against high-scoring vs. low-scoring customers.

The top 30% of customers received high scores and converted at nearly double the rate of the low-score group. And that was just the initial test! The brand was able to optimize content based on the model insights to improve conversion rates even more.

How well does AI scale for huge enterprises with all sorts of specific needs? Any insights there?

In some ways, AI is actually easier and more impactful for big companies. When you have millions of customers, you have more scale on your data and can make better predictions.

Using those large data sets, you can make the best possible predictions around what your customers want to see from you next, and the kind of offers they’re most likely to respond to.

Considering the importance of data privacy and security, how can brands ensure the responsible use of customer data in conjunction with predictive AI?

The bigger the brand, the more first-party customer data, so there’s less of a need to co-mingle first-party and third-party data to generate good predictions on those customers.

At MessageGears, we have access to all your first-party data in a very secure way because the platform sits directly on top of your data. It’s more secure than other AI solutions that have to take your data outside the safety of your firewall. 

MessageGears never needs to copy or store your data in our platform. What better data set to leverage for predictive modeling than 100% of your (safe) rich first-party data?!

How can brands leverage AI specifically to improve customer loyalty and retention?

Improving customer loyalty and retention has always been about providing value to your customers and in your products.

If somebody values the content they’re receiving from you – it’s timely, relevant, and on the channels they want to consume – then it’s a welcome interruption in their day instead of an unwelcome disruption.

Behavior models are an important tool in your toolkit to be a better marketer. The job, at the highest level, is to provide customers value, and AI can help brands do that.

How do you see the integration of predictive AI shaping the future of personalized marketing strategies for enterprises in 2024?

While many of the brands we work with are anxious to test and see how AI can help them, the challenges for large enterprises implementing AI models in their marketing are very real.

2024 is the year that brands will dip their toe in the water, do real tests, and start to roll out AI in real ways. 

AI will help these brands, but it’s not going to replace the need for professionals who are experts at what they do. In 2024, AI is going to have a voice in shaping all of that complexity in a real way, but it will still only be one part of the job.

Marketers’ jobs are very safe. If anything, AI is a tool in the toolkit that makes their role more complex. It’s combining your marketing strategy with what AI is telling you that your customers want to receive, and balancing that correctly will always take human finesse. 

What makes MessageGears’ approach to AI unique?

There’s been a lot of noise around AI and the landscape can be confusing for marketers. 

MessageGears is a trusted partner for some of the world’s biggest brands, and we have a unique understanding of enterprise needs and challenges. That combination makes us the ideal guide to help large companies understand AI.

As a bonus, our unique access to first-party data makes us a natural fit as the first step toward AI for a lot of these marketers. MessageGears sits on top of the data within a brand’s existing framework, so we’re able to easily combine what they’re already doing with the predictive modeling we offer. This helps to improve customer engagement in ways that other vendors would be hard-pressed to do.