Why Neural Search Is Emerging As The Game Changer For Most Ecommerce Websites

Are you tired of seeing customers leave your ecommerce website without making a purchase? Plenty of times, your customers know what they’re looking for, and you have it in stock! And yet, your website's search engine just can't seem to find it.

A solution may be on the horizon: neural search.

Imagine being able to search for a product on an ecommerce website just like you would on Google. Type in a few keywords hit enter, and boom - there it is, the exact product you were looking for. No more sifting through pages of irrelevant results or struggling to find the right combination of search terms. This is what neural search aims to accomplish, and it's quickly emerging as a game-changer for ecommerce websites.

But what exactly is neural search, and how does it work?

Simply put, a neural search engine is a type of search algorithm that uses artificial intelligence (AI) and machine learning to understand and interpret natural language queries. It works by using a network of algorithms, called a neural network, to process and analyze data.

Unlike traditional search algorithms that rely on pre-defined rules and keywords to return results, neural search engines are able to understand the context and intent behind a search query. This means they can return results that are more relevant and accurate to the user's needs.

Neural search allows users to get much more accurate results faster than traditional keyword-based searches. AI-powered algorithms can quickly understand a query and sift through vast amounts of data to provide the most appropriate results. It also provides much more comprehensive results, as it takes into account not only keywords but also related topics and context.

Neural search under the hood 

Artificial neural networks, which are used in neural search, are made up of interconnected nodes called "neurons." These neurons are organized into layers, with the input layer receiving data and the output layer providing the resulting output. Between the input and output layers are one or more hidden layers, where the actual processing of the data takes place.

In order to understand and interpret data, neural networks use a mathematical representation called a "vector." A vector is a list of numbers that can represent a variety of different types of data, such as words, images, or sounds. Each element in the vector corresponds to a specific feature or characteristic of the data being represented..

In a neural search engine, a vector might be used to represent a search query. The elements of the vector would correspond to different words or phrases in the query, and the values of these elements would reflect the relative importance or emphasis of those words or phrases.

Transform your eCommerce site with neural search

1. Leverage the strengths of both AI and neural search advancements:

Ecommerce websites can provide users with highly accurate and relevant results that are tailored to their specific needs and preferences.

• For example, using neural search to understand and interpret natural language queries, while AI search helps to filter and rank the results. This can provide users with a more personalized search experience, as the results are tailored to their specific needs and preferences.

Another way that these two technologies can work together is by using neural search to [point2], while AI search is used to suggest related or alternative search terms. 

• For example, Amazon uses neural networks to make personalized recommendations to seller partners based on customer search history and browsing habits. These technologies are also used to optimize product catalogues for search engines, recommend products to customers, and identify pricing patterns.

2. Understand and interpret complex, natural language queries:

Neural search algorithms are designed to understand and interpret human language, allowing users to input queries in a natural, conversational manner. This provides greater accuracy and a human-like search experience, as users are able to input queries in the same way they would ask a question to another person.

3. Provide highly personalized results:

By understanding a user's search history, preferences, and browsing habits, neural search algorithms can present search results that feel custom-made. This leads to a better user experience, as your users are presented with results that are relevant and useful to them.

80% of shoppers will leave your site if they experience poor search results – personalization is the key to preventing this from happening. No more struggling to find the right product or settling for something that's not quite what they had in mind. With personalized and accurate search results, your customers will have a seamless and enjoyable shopping experience on your site.

4. Provide instantaneous results:

Even a single second of delay in a webpage’s load speed leads to an 11% drop in page views, a 7% drop in conversion rate, and triples the bounce rate!

Neural search engines are able to process and return results in milliseconds, providing your users with ultra-fast results – you won't miss out on any potential customers due to slow loading times

5. Process any search format:

Neural search engines are able to understand and interpret a wide range of formats, including text, images, videos, and audio. 

So users can search in any of these formats, and receive relevant results regardless of the format. This is specially important since non-text based searches are becoming increasingly common.

For example: Imagine your customer sees their favorite singer rocking a dress they adore. With a neural search engine, they can simply snap a picture of the singer and use that as their search query. In a matter of seconds, the search engine will understand the image and provide them with a list of options to choose from – whether it's the exact dress they're looking for or similar looking ones. 

Conclusion 

Neural search is an incredibly powerful technology that has the potential to revolutionize how we find information online. By harnessing the power of AI and deep learning algorithms, you can provide users with accurate results faster than ever before, overhauling your user's search.

At Zevi, we are at the forefront of neural search technology. Zevi applies state-of-the-art machine learning methods that help users find the perfect product based on their preferences and interests. We can deliver highly precise personalized search results tailored to your customer's needs. Contact us for a free demo today!