Imagine a world where an AI system not only listens but observes, constantly collecting data unless explicitly told not to. This “exoskeleton for the brain” is not just a tool but a transformative extension of human capability, enhancing efficiency and decision-making in everyday tasks. For some, this raises alarms about privacy and the potential for misuse in a society already wary of data overreach. Yet, the real question lingers: how will such capabilities shape the way we live, work, and interact not just with technology but with each other?

As we stand on the brink of making these integrations a reality, it’s important to consider not only the current implications but also the generational shift that might come with it. The youth of today, growing up with AI as a norm rather than an addition, might view this integration as indispensable—melding their cognitive functions with artificial intelligence to an extent previously unimagined. This narrative isn’t just about the technology we create but also about the human experiences and societal structures that will evolve with it. Dive into the full discussion to explore the profound shifts looming on the horizon.

In this article, I want to talk about my experience with how to make a website or online store search more responsive to business requirements and user expectations.

It’s common to give too much attention to technical issues, especially now, with the advent of machine learning/AI in this field. But more often than not, the deepest problems are on the surface and their solution is much simpler than a functional refinement.

One of the main challenges in managing e-сommerce websites is ensuring the product search feature works effectively. Imagine this: you notice an issue with how products are being found through search, and you decide to fix it by tweaking some settings. However, altering these settings could unexpectedly impact your business. When we talk about something being ‘broken’, it’s not just about error messages popping up. Rather, it could mean that customers are having a harder time finding what they want, which might lead to fewer sales over time. This is a subtle effect that can spread across your entire platform. But how do you pinpoint the exact cause of such a decline? Testing and monitoring changes in an ecommerce environment are complex because even small modifications can have wide-ranging effects on customer behavior and, consequently, sales performance.

To monitor changes, it’s essential to understand what to collect and how to extract data from the gathered information. Specifically, for e-commerce search, there are no good ready-made monitoring tools available, but there are various DIY kits that address some needs. Such tools suggest collecting and analyzing search results for reference queries and visualizing changes over time. However, this approach falls short in many respects. For instance, it only covers the most frequent queries.

When it comes to modernizing site search, there’s a big question about whether this is done from scratch or if it’s possible to use accumulated data from existing sessions. Developing from scratch is always a risk—because any assumptions you have about how users will search for content may be wrong. Therefore, regardless of how good the first version of the search turned out to be, it will need to be revised and reworked many times.

Today, I want to share the approach that I have used in several projects. I call it Search Analytics. Read More »