Unlocking Gen AI’s Potential: 3 Essential Strategies for Personalization Success

Welcome to our new column Dynamic Voices, a regular series on XP² featuring timely perspectives on personalization, from thought leaders within our network. In this edition, Harry Hanson-Smith, Vice President of Northern Europe, shares practical tips that personalization practitioners can implement to better reach their goals with the help of AI. 

From the looks of my LinkedIn feed, I’ve begun to sense that the market is feeling a bit overwhelmed by the noise around generative AI. And after speaking with personalization teams and business leaders, it’s clear that AI is being adapted faster in cultural conversation than it is being implemented in businesses and personal processes. I’m here to cut through the noise and tell you not only that teams can integrate AI into their workflows today and optimize both their results and efficiency—for their individual careers and overall business impact—but that they should ASAP.

Though AI has widened the possibilities of what a single person can do in terms of creation and analysis, it still relies on that one person’s expertise. For example, though a marketer can use GenAI to create hundreds of different copy options to A/B test for in a new campaign, that marketer still needs to write a prompt that communicates information regarding a brand and its audience and edit its outputs to ensure they feel natural and rooted in genuine human empathy. And though a marketer can use advanced machine learning tools to anticipate and fulfill customer needs, it can only do so with pinpoint accuracy if the data feed is error-free.

In sum: People need to implement generative AI into their workflows for any real gains to be made. While all this prep work and QA’ing can feel too expensive and difficult for personalization teams, it’s imperative to do the work now. But luckily, there’s much that can be done to make the journey feel less daunting. Here, three key strategies that I’ve seen work for personalization practitioners, teams, and leaders.

Invest in Your Data Now: 

There are already many AI-powered recommendation tools baked into your operating system (we’ll get to them later), but to ensure maximum impact, you’ll need to collect and utilize your data effectively. AI capabilities hinge entirely on the quality and quantity of data it’s exposed to, so your team needs to make sure all data is relevant and consistent.

It’s not an easy task, but it’s worth it. Let’s look at home24 as an example : As the company grew their personalization program, they noticed their product feed was less-than-perfect, due to a previous strategy that focused on time to market over data integrity. To maintain their competitive advantage, home24 had to completely reorganize the structure of their product feed, purging any duplicated, irrelevant, or inconsistent attributes, and adding those that are timely. While the project was an incredibly technical, time-consuming, and complicated project, it was also the only way to improve AI-powered recommendation quality and ensure the data feed’s integrity as the program scaled.

While teams may want to delay this work, the time to tidy up the product feed is now as it will guarantee a competitive advantage.

Explore AI Prompt Training: 

Marketers can supplement their workflows with text-based AI tools like Gemini or ChatGPT, or image-based tools like Canva to create different copy and visual variations for A/B testing. I’ve heard from my network that the most common challenge teams face around AI adoption was knowing how to nail the perfect prompt—and this is easily solved with proper training. You need to be explicit to create any usable asset that aligns with your brand and goals. The context you provide for a text-based AI tool is just as important as the type of tool you’re using. As we know, AI is still a fallible technology, but continuing to experiment with writing AI prompts will give you a better sense of the level of specificity the tool requires to suit your needs.

You can unlock new ways to fuel creativity and produce captivating campaigns in seconds within Dynamic Yield’s Experience OS. Learn how to break free from repetitive messaging and visuals with generative AI-powered offerings. 

Marketers need practical tips for using AI, and I found this resource from Harvard University to be an excellent jumping-off point. You can provide any number of directions to the tool, like what you do or do not want to be included and how you want it to be presented. Feedback is essential, too. If you aren’t satisfied with an output, let the tool know so it can correct the mistake. And if you’re stuck on creating a prompt altogether, ask AI to help generate one for you. When given the appropriate context and direction, AI can yield incredible results.

Experiment with Different AI Tools: 

Beyond GenAI, there are advanced AI-powered tools that can plug directly into your personalization provider and improve the user experience and product recommendations. For example, in a world where customers seek highly personalized digital experiences, sophisticated generative AI-powered chatbots can create a conversational commerce experience that mimics the in-store consultative experience, using machine learning capabilities that identify and surface visually similar products. You can also improve recommendations with deep learning that processes data inputs across users to identify trends and patterns across your customer’s behavior.

The Future Is Now

As AI continues to revolutionize how marketers can interact with consumers, there’s pressure to leverage it as much as possible to stay efficient and ahead of the game. Experimenting with what’s available will take time and patience, but once you cut through the fluff, the benefits you will see will far outweigh any growing pains, plus help you discover more innovative and elevated personalization strategies. I hope these practical examples give you a place to start in your everyday role.