Understanding what the client needs is vital. Two powerful techniques to address that: user tracking and recommendation engines. User tracking is used to understand possible buying interests. With a big push into a privacy-focused and no-tracking world, user tracking becomes a challenge. Therefore, getting proper client interests based on prior tracking will be getting harder and harder. For the recommendation engine to be efficient, it needs a list of interests or shopping history. However, user behavior history is unavailable when dealing with the client for the first time.
What can we do to address the challenge?
Usually, the solution was a traditional search approach, which does work well for particular requests. However, what if the question is broad or we deal with a discovery request? The answer lies in emulating the approach used in physical stores: allowing customers to express their needs. We can effectively handle more natural queries that simulate in-store human interaction by leveraging Large Language Models (LLMs) in conjunction with vector databases.
By enabling customers to share their needs during the initial interaction, we can overcome the challenge of limited information that typically blocks recommendation engines. Just like humans, LLM can select the right products or address client questions.
This solution doesn’t require many changes, making implementation painless. At the same time, it dramatically improves the User Experience. Rather than further intensifying technological user tracking, we should allow our customers to voice and meet their needs.
