In the ever-evolving marketing landscape, one technology emerges as the potential linchpin – Gen AI. Revered for helping businesses move further, faster and more efficiently, does it also hold the key to unparalleled personalisation and an elevated customer experience?

Joining forces with industry leaders Rio Longacre, Managing Director at Slalom, and Jon Williams, Global Head of Agency Business Development at AWS, we dissect the transformative potential of Gen AI and its role in customer-centric marketing. From Gen AI's role in personalisation at scale to navigating its potential risks for unparalleled rewards, it's all below. 

Promising personalisation with Gen AI

Having worked in personalised marketing for around 20 years, Longacre says the hardest thing has always centred around the creative and content aspects of his roles. "Everyone knows personalisation works," he says. "The more personalised elements you put on a piece of creative, whether it's an email display ad or landing page, it'll generally convert better. But it's hard. 

"Personalisation is hard. You need data. You need creative. That last mile is having the right creative. Big deal if you have 50 segments. If you don't have 50 different pieces of creative, it's not going to matter, right? So that's always been the hardest part for marketers, especially in regulated Industries. What I think is really interesting is how Generative AI can potentially solve for that. It could be leveraged to build differentiated creative."

Longacre goes on to say that, for marketers, "personalisation has always been kind of the 'Holy Grail' for one-to-one marketing" and good thing is, he adds, "that's where we're seeing some super cool advancements, that's where it's exciting." 

"Another thing, he continues, is the efficiencies Gen AI is generating in operations. "It's speeding things up, doing things quicker," he says. "As far as jobs go, maybe some translators might be concerned. But, generally speaking, I don't see Gen AI replacing jobs. Instead, it's augmenting them, helping people work faster and better – even in contact centres. 

"We're finding that using generative AI, you can get customer 360 information in front of an agent. You can have an agent up to speed in a couple weeks where it used to take a couple months. It's a quantum leap in terms of productivity."

Williams chimes in, sharing heaps of Gen AI use cases, he's witnessing that come top of mind. "I'm seeing everything from social media posts to blog articles, marketing emails and copy, even language translations. You can even train a model to mimic a brand's unique voice and tone to ensure consistency across all of the communications. 

"Attribution and optimisation are also really interesting use cases. You can use AI to create more accurate attribution models. Marketers can sort of quickly generate hundreds of campaign variants from multi-variant testing and then analyse those results on an ongoing basis to continuously optimise campaigns. I think that's a really incredible use for Generative AI that massively reduces the amount of time spent on doing those things."

Gen AI cost and customer risks

Beyond the widely discussed legal and reputational risks that Gen AI poses, there's another risk to consider: customer retention and satisfaction and cost. For example, a couple of months ago, I was trying to book a flight and hotel for a trip. I went through this whole conversation with a chatbot on the booking website. Then, at the end, it wasn't able to complete the booking. 

It had asked me a lot of questions like my preferences, who I was traveling with and all of these other things. These were things it should have already known as I've made many bookings with the site before. So, I left feeling frustrated because I wasn't able to make the booking at all through this experience. It didn't enhance my discoverability because it didn't pull in any first-party data.

And back to the cost risk. This is often overlooked. But if you think about something like conversational AI, each time it has to ask the user a question, that's another request that needs to be made to the LLM API. If this happens once or twice, then no big deal. It costs a fraction of a cent. But at the scale of hundreds of millions of users, this becomes a huge business expense. To avoid this, brands must think about other ways to integrate more first-party data to both create a better customer experience and reduce costs.  

Start small with AI for big results.

My advice to brands and organisations when rolling out AI: start small. I would start with a small use case that's highly measurable and one that doesn't require major change. One place where clients we work with have seen a lot of success is just with subject line optimisation or optimising the body of emails or paid media ads. Since you can have a human in the loop here, it's a great opportunity to experiment with creating different segmentation strategies and different messages. And it's also really easy to measure and determine if those approaches are working or not. 

At Amperity, we recently announced two new generative AI capabilities, Explore and Assist, that join our existing AI-powered capabilities, Stitch and Predict, to create a comprehensive suite collectively known as AmpAi. We are committed to fixing the data quality and access challenges many brands face with traditional CDPs. With AmpAI, brands can be confident that they are making decisions based on a trusted data foundation to determine the best way to engage with customers and power downstream AI technologies. 

As the cookie crumbles and the marketing landscape continues to change, we want to ensure our technical and business users can put every last crumb of their customer data to use, unlocking more value and creating incredible user experiences. While this is a big leap forward, I can tell it's only the beginning.

Watch the full webinar here: