image search for e-commerce

Image search for e-commerce: See revenue and satisfaction soar

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You’re looking for something unique and can picture it in your head, but aren’t having any luck finding it online. Sound familiar? You’re not alone – and that’s why image search for e-commerce is becoming a must-have for retailers.

Customers don’t have a lot of patience when it comes to purchasing online, so e-commerce businesses providing a catalog of thousands of products isn’t necessarily useful. Online shops allowing people to search for fashion items in their catalogs and on websites (or online shops) by only providing text-based searches are missing the boat when it comes to customer experience.

Customers use text-based search to try to describe the products they like, but can rarely find the right items in the search results. It’s not easy to describe a jacket with a specific texture or sunglass with a unique pattern, and there is a need to explain them into words.

Filtering by product category and manually searching are both extremely time-consuming, and sometimes not successful. After failing several experiences, customers often give up, producing a negative effect on conversion rate and revenue.

Visuals are powerful resources when it comes to a shopper’s ability to identify products and make decisions.

In fact, it’s estimated that the human brain can process an entire image in just 13 milliseconds, meaning they’re processed 6 to 600 times faster than text.

Image search for e-commerce: Visual search optimization boosts engagement and conversion

If e-commerce websites upgraded their platform and search strategies to support image-based search where users could use images, including products to search for other similar products, the customer experience would be greatly enhanced.

Image search for e-commerce would significantly improve:

  • Conversion rates, as customers can quickly and interactively find the items they’re looking for
  • Customer interaction
  • Customer’s shopping experience
  • Sales and cross-selling opportunities

In order to have an efficient search, there’s a need to develop automated ways to learn image features. A machine learning approach is one possible solution.

With the help of algorithms, machine learning can train the system from historical data to develop an intelligent platform that can make decisions. Then, it predicts next steps based on trained data.

E-commerce and image search can take advantage of machine learning, providing a valuable and personalized experience for customers. Machine learning is making search engines smarter, and deep learning is a branch of machine learning that’s useful for image search.

AI and machine learning in visual search: How it works

For computers, an image is data representing a 2D matrix, and it includes hundreds of thousands of pixels. In the meantime, an image is an arrangement of semantic patterns, lines, curves, textures, and colors.

image search for e-commerce 2

There are different types of machine learning algorithms for image search. Here, we’re talking about two popular algorithms: Convolutional Neural Network (CNN) and k-Nearest Neighbors (k-NN).

We have two phases shown in the diagram above: Offline and Online.

During the offline phase, we train the catalog of an e-commerce shop by CNN algorithm. A CNN is a sequence of layers; the input of each layer is the output of the previous layer. An image is input into the first layer, the first layers extract low level features, like edges. Meaningful features are extracted at the end. The output is a list of feature vectors.

During the online phase, we already have the vector representation of each image in e-commerce shop catalogs. The stored vectors include meaningful description that can be used to catch link between one image and the other. By the help of CNN, we extract latent meaning from uploaded image by customer and it becomes easier to make image-to-image comparisons to find the most visually similar matches.

However, we still need another step to compare the extracted feature vectors for similarity. K-NN is one of classification algorithms for supervised learning. The goal is to search for closest match of the uploaded image in feature vectors.

The following steps summarize how image search with machine learning works:

  1. The client uploads the image
  2. Preprocessing of an input image
  3. Extract the visual features of the uploaded image
  4. Calculate the similarity between extracted features and trained data

What machine learning can do for your e-commerce business

With image search powered by machine learning technologies, your e-commerce business will have a plethora of benefits.

With powerful machine learning at the engine, your business can enjoy:

  • Faster decisions: Machine learning algorithms can prioritize and automate decision making. They can also flag opportunities and smart actions that should be taken immediately – so you can achieve the best results.
  • Adaptability: Artificial intelligence doesn’t just look at a customer’s historical data. It can process real-time inputs – so your site recommendations can adjust on the fly. Imagine a customer who just saw a jacket on the train he/she likes, and they enter your site to look for it, with a few feature inputs, your e-commerce site would be able to offer relevant products.
  • Deeper insights: Machine learning can analyze big, complex, and streaming data, and find insights – including predictive insights – that are beyond human capabilities. A customer goes through multiple websites and platforms which contain different forms of media/content. Often that journey is done via multiple devices. Machine Learning can analyze all these touchpoints to find insights and then trigger actions based on those insight.
  • Better outcomes: From triggering smart actions based on new opportunities and risks, to accurately predicting the results of a decision before it’s made, machine learning can help you drive better business outcomes.

The future is now.
Learn how to make the most of
machine learning for your business HERE.

Hanieh Alipour
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Hanieh Alipour

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