Optimizing product search using AI on websites and online stores is critical to improving user experience and engagement. However, search results are not always relevant and accurate for the user, leading to several problems. In this post, we will show you how 7Puentes optimizes retailers’ smart product search experience by developing embedding models.
Currently, when we search for products in a store or browse different portals to make a purchase decision, we need the results we get to be the most relevant, pertinent and related to what we are looking for.
However, this is not always the case, and many stores or product portals, whether e-commerce, mass consumption or delivery, have numerous problems in returning an efficient result.
Let’s imagine that we search for the term «bag of oranges» on the website of our favorite market. However, in this search, instead of getting a set of oranges, we see garbage bags and cloth bags, and as other results appear other related fruits (such as apples or bananas), and then finally the «oranges» (which is what we wanted to buy). Our search experience and potential purchase was poor and the result was quite ambiguous because the search engine did not contextualize our query.
The same would happen if, on a food portal, instead of searching for a product, we searched for «typical Japanese food» and it returned different types of ethnic food until a typical sushi or ramen appeared. The search was not that precise. And we are left to place the order. How is this solved? Below is the answer…
Embedding models for efficient representation of retail products
One of the great advantages of natural language processing techniques is that they can answer user queries in a natural way, reducing ambiguities, contextualizing searches, and improving the user experience.
In this aspect, the development of embedding models is a desirable solution to optimize the search experience. To understand what these models are, we can think of them as the words or phrases of natural language represented as vectors of real numbers. But this is not limited to the text, the images are also correlated with the meaning of what is being searched. For example, if we search for «Marilyn Monroe» in our retail store, the search engine should return those products with images that are most related to our search, such as clothing, paintings, cosmetics, accessories with the iconic image of Marilyn, and so on. And if the retailer still had no products, it should return similar results by context (but never 0 results). In addition, the search function should return results quickly and efficiently.
The solution to solve this search problem is to assign an embedding model for each specific product. This allows you to obtain a very precise vector numerical representation from a database of images, descriptions, and reviews of all products. So if we search for a «bag of oranges» on a supermarket website, the search engine will discard all the irrelevant products and understand that we are looking for oranges. The problem becomes more complex when we search for products that are key ingredients in a food recipe, such as Mexican tacos, paella, or Thai food, but the search engine must be robust enough to solve this problem. Currently, not all food retailers can generate an optimal solution.
So what are the key benefits of an embedding model for your retail business?
- These models solve the classic problems of inefficient results in common product searches, both in text and images, providing an improved, efficient and personalized experience.
- Maximize the visibility and performance of your retail site with our advanced Smart product search solution.
- Prioritize relevant search results and increase conversion rates through intelligent data organization and personalized search algorithms.
- Finally, by optimizing your product search results, you will be able to attract, retain and acquire new customers and differentiate yourself from your competitors.
It should be noted that 7P’s AI solutions include intelligent search algorithms; personalized user experience; contextual recommendations; dynamic reorganization and performance monitoring.
7Puentes: Retail value services that improve your business results
At 7Puentes, we have the capacity and expertise to develop and apply embedding models to your entire product base. We have over 15 years of experience, more than 50 satisfied clients and more than 100 successful projects..
Our AI-powered product search solution uses innovative approaches and implements advanced relevance algorithms to analyze user queries and deliver highly relevant search results, taking into account factors such as user behavior and contextual relevance. It also leverages a scalable and optimized infrastructure, as well as advanced content organization techniques, including improved taxonomy and rich metadata.
Are you ready to provide your users with an improved and efficient search experience for your retail business? Contact our AI and Smart product search specialists for a free consultation.
