With the advent of disruptive technologies, visual search technology is slowly gaining traction and is getting incorporated into numerous applications and websites. Visual search technology is much more complex than any of the search methods, be it a text or voice-based search, as with visual search the image itself is the query. However, Machine Learning is playing a vital role in getting the search engines trained for better understanding the minute components of images appear, resulting in more reliable results and keen usage. To demonstrate the same, this study focuses on Bing, Google and Pinterest and how they are applying visual search technology to enhance their search strategy.

Though text search is still leading the search engine market, visual search technology is creating prominent benchmarks in the same field. It is continually improving on its capabilities and is expanding across various platforms. With the advent of disruptive technologies, visual search technology is slowly gaining traction and is getting incorporated into numerous applications and websites.

Applications and websites like Google, Bing, Pinterest have already ventured into this new domain by incorporating extended functionalities into their respective platforms. Visual search is slowly changing the search marketing landscape as we tend to know it better.

“The future of search will be about pictures rather than keywords – Ben Silbermann, CEO, Pinterest”

1. engine giants have gone an extra mile towards providing a seamless interface to implement the former technology. A user just needs to snap an image put it into the search engine and Voila! In a fraction of second the user gets the desired results.

Its time saving, convenient and brings out an immensely exciting opportunity for ecommerce retailers.

Let’s dive a bit deeper into the world of Visual Search Technology and gain some more insights on how visual search technology could change or influence search habits over the next few years and what businesses need to do in order to survive in this rapidly changing era:

The Growth of Visual Search Technology

Visual search technology is much more complex than any of the search methods, be it a text or voice-based search, as with visual search the image itself is the query.

However, Machine Learning is playing a vital role for getting the search engines trained at better understanding the minute components of images, resulting in more reliable results and keen usage.

Text and voice search usage is already there from the past few years. But now visual search is slowly penetrating the search engine world. Not only image search, but also live search queries with augmented reality is rapidly changing the whole visual search technology scenario.

Visual search is on ultimate rise, with 27 percent of all searches across major search engine platforms being images (Source Moz and Jumpshot)

Use Cases

Pinterest

Recently, Pinterest has reported that their users carried out more than 600 Mn monthly searches combinedly using their ‘Lens’ technology in the month of February 2018. They have claimed to have more number of image results than ever being returned by Google.

Since 2014, Pinterest has incorporated a new feature, which allows its users to highlight specific sections of Pins to search visually similar Pins to a selected area.

Also, on the technological front, Pinterest has rolled out a “Pinterest Lens – a unique search function that aims at matching images, not word search queries.” Pinterest Lens allows users to use their camera function on the app and discover ideas inspired by objects they see through the lens, out in the real world.

Pinterest is using this new technology to help build its user base and boost itself as an advertising platform. In a recent report published, it is said that 90 percent of weekly Pinterest users’ buying decisions are influenced by the items they’ve encountered or liked on Pinterest.

Bing

Just like Pinterest, Bing have also developed a similar technology where smart image-based search is carried out using the images clicked from the user’s camera.

Bing’s this technology allows its users to select portions of images and focus it on specific items to further refine their search process.

Further, Bing has also claimed that when a user uses this feature for any shopping intent then a query is generated to scheme out related information and provide details related to the search query with pricing and availability information.

It is still in the development phase and sometimes fails to recognize selected items and would return any arbitrary result simply based on the color, despite the fact they are totally different objects.

Google

The search engine giant has gone an extra mile to make image search simpler and more accessible by launching their own camera-based search product known as ‘Google Lens.’

It is still in beta phase and is soon going to be rolled out to almost all of the Android devices. Being native to the device, it won’t need any app to get operated. This will surely take user experience to another level by outshining its competitors, to further monetize its search results.

1. is also on the verge of rolling out “Style Match” feature emphasizing on providing users the functionality of accessing respective URL’s based on what they have snapped and searched.

Technology Stack

Measuring ROI on Visual Search Technology

Measuring ROI based on visual search technology is to find exactly how the technology is improving any business in terms of financial gains and hard metrics, such as:

• Conversion rates
• Click through rates (CTR)
• Product view numbers
• Bounce rate and exit rate

Cost Factor

• Software cost
• Implementation cost

To further optimize the metrics, A/B testing needs to be carried out with and without visual search feature also with functions placement and position on the website. Consumer conversion is the most crucial part to reap out better engagement.

Overview

The technology works best for such businesses whose database is large and varied. It’s a best practice to have 3,000 SKUs per vertical in a database as a prerequisite for the software to be effective.

The reason being that as the software is applied to a platform with requisitely large database because the search results generated not only depend on the accuracy of the technology, but also on the size of the database.

So, if the volume of images in any database is low, it directly limits the results based on the insufficient data of the search query. Thus, implementing visual search technology to such platforms would not achieve optimal results.