LLM-based Search: A New Era in Personalization

This week, I met the founders of Brandlight.ai, a high-momentum start-up focused on helping companies understand how they are positioned when people ask questions to AI chatbots, especially the larger LLM's, such as Chat GPT, Perplexity, and Google Gemini. As I dove in, it became rapidly clear to me how the change in Search that's coming via AI will be a massive leap forward in personalization. 

Brandlight uses AI to hit all the major chatbots with questions relating to one's brand, category, and generally relevant customer needs. Then another AI analyzes the responses, and a third renders visuals explaining the results. And the results are not only a share of voice and ranking of mention but also what are the contextual areas where one is strong vs. weak. 

Of course, there is still a lingering question about what a brand can do to affect its standing, but learning is emerging from their analyses about how the next wave of essentially SEO strategies will lead marketers to write content to meet the demands of the new kinds of algorithms that drive the learning of these AI models.

But from a personalization point of view, let's dig a little further.  Essentially, the AI bots are now enabling you to ask very specific questions, according to your needs, to find options and answers. "I want to buy a chef's knife for cutting meat into small pieces for kebobs, and don't want to bother with constantly sharpening it." 

The specifics of what you can probe are powerful, and as these AI bots learn more about you in general, they will remember your context, your preferences, your price points, and use that to steer the answers.  Of course, this is much more useful than just getting a series of links to then chase down. 

Even basic packaged goods will find themselves more concerned with Search, as people ask questions such as "What should be my shopping list for a 10-year-old's birthday party." The bots may start naming specific brands, and you need to be on that list. 

In our book, "Personalized: Customer Strategy in the Age of AI," Mark Abraham and I assert that true personalization comes from using information about a customer to provide them with clear value. Already, that definition now has to start expanding to include generating content that will likely address the full range of questions customers may have, so answers can be assembled by third-party LLM's, or by chatbot assistance on your own site. 

As we cite, the big pivot that Personalization demands is taking the customer's point of view. With the inevitable expansion of people using AI for Search, and their expectation that you can help them if they come to your environment, anticipating all the ways to serve individuals through their journey becomes even more important. 

Product managers may be focused on constant improvement in the value proposition they offer, but they need to partner with the rest of the business to improve the full customer journey involved in learning, buying, and using that product. And that journey requires delivering to the personalized needs of each customer.

So, step back and ask: Throughout our customer's journey, even upstream when they are using Search, how are we empowering them, using what we know about them, reaching them appropriately, showing them useful content, and delighting them more and more over time.

The move to Search via AI chatbots is a massive wake-up call for all companies to consider their full personalization strategy. 

Understanding what your baseline is, using tools such as Brandlight, will be a start, but then, getting ahead of delivering on what customers will be a primary requirement in 2025. 

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