Is your store’s Search your top salesman?
Customers tell your Search what they want, yet it makes no effort to make sure that they don’t leave the store without finding something they like. All it does is match the words they said, with the words written on your products. Would you retain this salesman on your team?
What to expect from Search
Your store’s Search should be like that shopping assistant in a retail outlet whom a customer asks, “I am looking for something to wear at my friend's wedding”, or “I am looking for a safe SUV for my family of 6 under $15000”, and they'll know exactly what the customer means and show them just the right products. In case the customer asks for a specific Sony speaker, and you don’t have one available, they’ll present other speakers that match what was asked for, but they are always trying to give the customer options they would want to buy.
The assistant will also suggest the shoes that go with those trousers, and the woofers that go with those speakers that the customer liked so much.
They are always selling. So should your search.
Search, the salesman. How?
Almost all search works on text matching, and you can only match so much. Text matching does not know that Pepsi is a good result if Coke is out of stock, or that Shirts and Shorts, although similar in spelling, are entirely different products. It doesn’t know that Cornflakes is breakfast cereal, or that harem pants - although a type of trousers, are not what people mean when they say “trousers for office”. Text search does not understand.
It takes three things for your store Search to become a conversion machine: It needs to understand the product catalog, it needs to understand what the user really wants from the search queries, and it needs to learn from users' behavior continuously.
Understanding the Product Catalog
For a retail search system to understand a product means to know what the product is, the relationships and meanings of its different attributes, what it is used for and how. It includes analyzing the images and figuring out the details which may not be available in the form of data attributes, like a dress being flared, or a chair having wooden arm rests.
Mapping the relationships between these attributes, purpose and ways in which a user may look for them builds this understanding.
Understanding Search Queries
To understand a search query is to understand the real need of the customer. They may be looking for a specific product, but what is the problem they need solved, the “jobs to be done”? What do you have in your catalog which can solve their problem? What does a user mean when they are looking for "Formal Shoes", or "Floor lamp for living room"?
Only when Search understands both the products and the query can you expect it to return high quality results that serve the user's needs.
“Understanding” is a loaded word, however we are closer to mimicking human understanding than we have ever been in computing history. The latest developments in AI allow us to build infrastructure that can mimic human understanding to a good extent.
How Neuralens does it
Neuralens search is designed like an exceptional salesman.
Unlike other generic search systems, it is built for retails and knows what customers care about when they search something.
It understands products by extracting data from images, utilizing industry context, and gaining insights from customer behavior to enrich its data. This enhanced data helps service user requests more effectively.
Neuralens also comprehends complex user queries to figure out what users care about the most, what they are trying to solve and what products will fulfill their need.
It marries query understanding with product knowledge to return results optimized for conversion.
Learning and getting better
Neuralens gets better at converting users with time in two ways.
The relevance engine learns out what kind of products users prefer in certain cases, and it figures out what queries are not performing well and how to make them work better.
Unlike most systems where product, growth, data-ops, and engineering teams manually fix non-performing queries, Neuralens automates this process, making performance improvements faster, more efficient, and cost-effective.
Try it out for yourself
Interesting in giving it a spin? Reach out to us and we'll help you set up Neuralens on your store.
Say 👋 to us at hello@neuralens.ai