The big problem of Search and how AI can fix it.
The Big Problem of Search
The main challenge with almost all types of search is determining user intent. This is relevant for all Apps on online shopping, food, music and so on.
For example, when you search on Amazon, you will see sponsored products, lists, and recommendations based on keywords, which suggest the buying intent of customers. However, this intent is limited. There is no way to determine if customers are also interested in other products since the search is based on a single keyword or may be a few.
Google addresses this issue to a great extent by leveraging long-tail keywords and longer text, enabling it to better understand user intent with greater accuracy. Yet, even here, we are constrained by the customer’s input, and there is no way to identify any unstated intent. This is why Google continually updates its search engine results page (SERP) to present more relevant information, thereby improving its ability to infer user intent.
The problem becomes more complex when searching for complicated products such as apps, software, or educational materials. The input is limited, and the system often fails to consider all relevant factors since there is no input there. It shows what is available, sending the user on a never-ending search for better products or companies.
This challenge is even more significant for companies selling data based on customer intent to buy. There is no definitive way to determine true intent because the system only accounts for probabilities and keywords.
In all cases, customer demographics, profiling, and context are often overlooked since the primary method of determining intent is through keywords. There is no way to confirm whether a customer is genuinely interested in buying, just browsing without intent, or looking for something entirely different and being misdirected.
How AI Can Solve the Problem
How can we determine intent more accurately? The solution lies in real-time customer behavior on a portal or webpage. AI can discern whether a user is casually browsing, actively searching for a purchase, or exploring with no immediate intent.
It can take into account various factors, such as the user’s location, activity across multiple screens, changing input patterns, or even a more focused search.
AI can also deploy multimodal search when necessary or switch to conversational prompts like, “Are you looking to buy this now, or just browsing?” or “Would you prefer recommendations for similar products?”
Additionally, AI could leverage collective intelligence from large groups of similar users to map search behavior more accurately.
It might detect patterns indicating that users prefer to browse and compare products before eventually making a purchase after multiple visits, and could then recommend comparison tools in future sessions.
A new layer could be added where implicit intent (like the user’s lifestyle, preferences, social media activity, etc.) AI could employ cognitive science to gently nudge users toward better decision-making without overwhelming them with choices.
There are many ways, AI is still evolving, the question is how will you will use search within your app, portal or e-commerce website.
