In a constantly evolving marketing landscape, one technology is emerging as the potential pivot: the AI generation. Known for helping businesses go further, faster and more efficiently, is it also the key to unparalleled personalization and an enhanced customer experience?
By joining forces with industry leaders Rio Longacre, Managing Director of 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 the role of AI generation in personalization at scale to managing potential risks for unprecedented rewards, it’s all below.
Promising personalization with Gen AI
Having worked in personalized marketing for around 20 years, Longacre says the hardest part has always centered around the creative and content aspects of his roles. “Everyone knows personalization works,” he says. “The more personalized elements you add to a creative, whether it’s an email display ad or a landing page, it will usually convert better. But it’s hard.
“Personalization is hard. You need data. You need creativity. That last mile is about having the right creative. It’s a big problem if you have 50 segments. If you don’t have 50 different creative elements , it won’t matter, right? This has always been the hardest part for marketers, especially in regulated industries. Which I think is really interesting, this is how generative AI can potentially solve this problem.
Longacre goes on to say that, for marketers, “personalization has always been a sort of ‘Holy Grail’ for personalized marketing” and the positive thing, he adds, is that “it’s where we are seeing some really interesting progress. That’s where it’s exciting.”
“Another thing,” he continues, “is the efficiencies generated by AI generation in operations. “It speeds things up, does them faster,” he says. “In terms of jobs, can -maybe some translators could be affected. But generally speaking, I don’t see Generation AI replacing jobs. Instead, it’s about increasing them, helping people work faster and better, even in contact centers.
“We’re seeing that by using generative AI, you can get 360° customer insights in front of an agent. You can have an agent up to date in a matter of weeks where previously it took a few months. It’s a giant step forward in terms of productivity.”
Williams chimes in, sharing many of the AI generation use cases he witnesses that come to mind. “I see everything from social media posts to blog posts to marketing emails to texts and even translations. You can even train a model to mimic a brand’s unique voice and tone to keep all communications consistent.
“Attribution and optimization are also very interesting use cases. You can use AI to create more accurate attribution models. Marketers can somehow quickly generate hundreds of campaign variations from multi-variant testing and then analyze those results continuously to continually optimize campaigns. I think this is a truly incredible use of generative AI that dramatically reduces the time spent doing these things.
Cost of AI generation and customer risks
Beyond the widely discussed legal and reputational risks posed by generation AI, there is another risk to consider: customer loyalty, satisfaction and costs. For example, a few months ago I was trying to book a flight and hotel for a trip. I went through this entire conversation with a chatbot on the booking site. Then in the end he couldn’t finalize the booking.
It had me asking a lot of questions like my preferences, who I was traveling with and all these other things. These were things he should have already known as I have made numerous bookings on the site before. So, I left frustrated because I couldn’t make the reservation at all thanks to this experience. It didn’t improve my visibility, because it didn’t extract any first-party data.
And back to cost risk. This is often overlooked. But if you think about something like conversational AI, every time it needs 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, it’s no big deal. It costs a fraction of a cent. But at the scale of hundreds of millions of users, this represents a huge expense for the company. To avoid this, brands need to think about other ways to integrate more first-party data to create a better customer experience and reduce costs.
Start small with AI for big results.
My advice to brands and organizations when deploying AI: start small. I would start with a small use case that is highly measurable and doesn’t require a major change. One area that clients we work with have had a lot of success with is simply subject line optimization or body optimization 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 messages. And it’s also very easy to measure and determine whether these approaches work or not.
At Ampère, we recently announced two new generative AI capabilities, Explore and Assist, which join our existing AI-powered capabilities, Stitch and Predict, to create a comprehensive suite known collectively as AmpAi. We are committed to solving the data quality and access issues that many brands face with traditional CDPs. With AmpAI, brands can be confident that they are making decisions based on a trusted database to determine the best way to engage with customers and power downstream AI technologies.
As cookies crumble and the marketing landscape continues to change, we want to ensure that our technical and business users can use every last crumb of their customer data, unlock more value and create incredible user experiences. Although this is a big step forward, I can say that it is only the beginning.
Watch the full webinar here:
