Visual intelligence is a term that marketers will find themselves becoming very familiar with in the coming months. Visual, or spatial intelligence, is one of the nine types of intelligence defined by the American developmental psychologist Howard Gardener. It refers to the ability to remember images, faces and details as well as the preference to learn and communicate using visual stimuli.
From a technology perspective, the term is used to describe the ability of machines to produce results based on queries that include only visual information, such as images and videos. Visual intelligence is not anything new: Google Visual Search was first demoed in 2009, the same year Zappos launched its own iteration of the technology. Amazon also experimented in the field a few years later, equipping its Fire Phone with the Firefly visual research software in 2014. At that time it failed to meet customer expectations, having problems identifying products without barcodes.
But three years are an eternity in the technology industry, and things have changed since 2014. Machine learning has laid the foundations upon which visual intelligence can substantially benefit users, and change forever the way they search online. Thanks to visual intelligence, users won’t have to type a myriad of keywords trying to describe the product they are looking for; a picture will be enough for the AI to provide them with the most relevant results and ultimately lead them to their target.
Today Vs. Tomorrow
Visual intelligence is not some sort of a sci-fi project; it’s happening now. Try searching for a ‘cocktail dress’ or a pair of ‘Converse All Stars’ on Google and the first thing on the search results’ page will be the engine’s own (i.e. sponsored) suggestions and recommendations, complete with pictures, prices, ratings, and of course links to purchase them. Although this is far from the visual intelligence concept described above, it is the first step towards realising the real-world, consumer-focussed applications of the tool. Thanks to machine learning, visual intelligence can start exploring its potential.
Immediacy is the goal here: users find something they fancy, they paste it in a search engine (be it Google’s, Amazon’s or whoever else’s) and instantly get their results. Simplicity and convenience at their best; using only a device they always carry with them (i.e their smartphone), they’re able to instantly find whatever they encounter, even in non-digital media such as a newspaper or a magazine. Even with minimal user input, consumers are presented with a series of suggestions, a technique that heavily favours the brands that invest in it.
A study from Google in 2016 found that 44 per cent of customers tend to use images to gather ideas while shopping online. Visualised ads not only offer higher click-through rates but often lead to clicks of a higher quality – and value. Visual intelligence, however, can significantly benefit a brand, even when a customer is already browsing inside its e-shop. By applying the proper machine learning algorithms, a company can offer its customers more accurate personalised recommendations, and further enhance the ‘similar to’ section of a product page.
A Trend Rising
Google is perhaps the most well-known brand to be actively working on visual intelligence but it certainly is not the only one doing so. Pinterest, the social network with such a strong footing in visualised bookmarks and purchases, is expected to launch its own visualised search feature soon, while Snapchat has filed a patent application for a system that allows it to serve ads based on the products identified in the users’ snaps.
UK-based Cortexica is another firm working in this area. Having run its first trial in the Brent Cross shopping centre in London, it has already attracted huge brands such as John Lewis and Macy’s. Retailers are now understanding the advantages visual intelligence has to offer and along with A.I brands from Europe and the US are working towards new solutions and implementations. In tomorrow’s window shopping, the most difficult thing to do will be to just unlock your smartphone.
Visual intelligence will gather momentum as it becomes further implemented in the everyday life of the consumer in the coming months. Communication has arguably already succumbed to visualisation; stickers, emojis, gifs and images prove this billions of times on a daily basis. It follows that ecommerce is a natural progression for the advantages of this technology. Consumers search first and then purchase; by serving them content in a different way and therefore making sure they can find what they’re looking for, a company can improve its sales performance. Machine learning will allow businesses to become more effective while still keeping their customers happy. After all, who doesn’t like window-shopping?