In the near future, artificial intelligence (AI) would be in major demand by the consumers to make interactions as seamless as possible. Many of us as an end user are unaware of the fact that Google uses AI to improve their search. It provides us with the correct results most of the times.
How would an end user get impacted by AI?
Behind the scenes of any AI-powered systems, there is a long and complex computational process involved with the trained data set for the algorithm to perform so that the user gets an overwhelming experience. This experience is so fast and seamless that the user thinks everything is happening magically.
Here are some interesting facts about AI and retail:
- By 2020, 85 percent of customer interactions in retail will be managed by artificial intelligence, according to Gartner.
- According to Business Insider, shoppers who interact with online reviews and opinions are 97 percent more likely to convert with a retailer than customers who do not.
- Seventy percent of US millennials and 62 percent of millennials in the UK say they would appreciate a brand or retailer using AI technology to show more interesting products.
From my two years of experience of working at the juncture of AI and retail industry, there are certain conclusions and insights I have drawn. There has been a humongous transformation over the years in the retail industry and I am pretty sure this field is ready to be disrupted that will make the consumer journey more intuitive with the ease to interact with the products/experience of their liking.
Currently, there is no upload button in any e-commerce store. Today’s e-commerce stores provide two general types of recommendations: one is cross-products recommendations from a different category (items that are bought together) and the other is similar product recommendations (items you may like). For these recommendations, stores generally use traditional collaborative filtering, cluster models, and search-based methods to recommend the end user. These recommendations don’t perform well most of the times. The other reasons they are not so effective is because they are manually fed and have inconsistent product tags. In fact, in the case of fashion products, these product tags are insufficient in describing a visually rich fashion product.
North Face is one brand that has been experimenting in this space. It is working with a tool called the Fluid Expert Personal Shopper powered by IBM’s Watson, which enables users to have more intuitive search experience.
Early adopters of VR are Tommy Hilfiger, home improvement store Lowe’s. Tommy have created virtual environment in which from the set and music to backstage moments, consumers are able to watch the clothes move and see the collection in the original show environment.
How AI can transform the entire retail industry
- Personalised online shopping
Personalised experience in the online retail industry is the most sought-after and the next big thing right from the personalised page, personalised search results, personalised product recommendations, and personalised offers.
Personalisation can be on the landing page as well as the product page. By tracking the user’s past activities on the website, like purchase history, view history, clicks, products added to the wish list and cart, we can easily analyse and predict the likes and dislikes of the user. All these data points are given to the algorithms and these algorithms predict the best-personalised pages having the products the user is most likely to purchase/need. Not only the products on personalised pages but the layout of the pages can also be customised according to how the user interacts with the site.
Personalised products recommendations
Fashion products like dresses, bags, jewellery, and other accessories, etc., have rich attributes like colour, shape, style, and the pattern. The choice of these attributes varies from person to person.
Using the above information about the end user behaviour on the site, a retailer/e-commerce store can start recommending possible items to be purchased or items which may be liked by the user. In addition to that, by knowing your fashion activities on social media, smart machine learning algorithms will have a better understanding of the user’s likes and dislikes. And these social media clues can also add up to existing recommendations. For example, one of your close friends posted his picture wearing a red sweater and you liked his picture with a positive comment. Now, algorithms can take this signal and can show you some similar red sweaters in your personalised recommendations.
These algorithms don’t only recommend the similar products based on the user’s likings but also the complementary products he/she will like. This way, products that go well with the user’s likings can also be recommended and personalised based on the social element of the user, the latest trends, colour preference, style, mood, weather, etc.
To analyse what a user thinks about the provided recommendations, we can also use the front camera of the user’s device to track his/her facial emotions and give feedback to improve the systems.
The user takes a picture of what he likes and uploads it on the website or in the mobile app. The e-commerce then reverts the exact purchasable products present in his catalogue or the similar products.
- Virtual reality
VR requires stand-alone technologies such as headsets and, typically a controller.
In the advent of new technologies, VR has seen a tremendous growth in the hands of early adopters. By sitting at home, consumers can visit various stores, view their entire catalogue, look for all the latest collections from their favourite brands or designers. This will be on the rise like online buying as it saves time and avoids pushing sales pitches from the salesman at the store. VR store is very well arranged to locate the category of the products and helps you find what you are looking for.
- Video analytics
Till now, we have talked about online stores. However, a major chunk of purchase happens in-store and we need a system to analyse the user behaviour in physical stores as well.
With major advancement in computer vision technology, video analytics industry is ready for the upgrade. A shopping mall will have useful insights from the videos recorded by their CCTV cameras which will include how much time a user spent on each product, who is taking the product from the self and without putting it to the cart keeping it back on the self. Technology will also help an offline mall to see which part of the store is not at all visited. This intelligence will provide the store to take necessary actions like giving offers to the place where there are least visitors, shuffling their products shelves, like products which look appealing can be placed on low selling shelves and vice versa. A security alert will be activated on the screen when a person tries to put something in the pocket without actually paying for it by putting into the cart. Information like how many people visited their store at a particular time, their age and gender will help to know their consumer better and take actions accordingly.
- Augmented reality
As we all know the core technology behind the success of Pokemon Go was AR. Retail industry also has to use this technology to lure their consumers. Brands/retailers can promote their product by using AR. When a user comes to the physical store for shopping, brands can place some signs on the product through which it will help a user to scan the code and a video will pop for how to use that product or can play an interactive game on top of the product. For home decor industry, from the store catalogue, user can scan the barcode of the item he likes, and after coming home can see the product in the real world with the camera positioned at the place where he wants to keep the liked furniture the app will show, whether it will fit in that place or not, how it will look in the overall room.
Augmented reality will help any brand to launch their user manual in a short video for the user on hovering the camera over the logo on the product packaging. This is what Blippar/Layer is doing.
- Offline stores
To improve the buying experience in physical stores, we can implement:
You have an image of a particular product like a dress, you can send the image via Bluetooth/ Wi-Fi and the kiosk pops up with the exact dress/similar dress present in the store. Also, it will guide you to the exact location of the dress in the store.
Virtual trial mirror
Stand in front of the mirror and you would be surprised to see the entire catalogue present in front of the big mirror. You can actually see a different product on yourself, change the background, the colour of the dress, take a selfie, match with the complementary products and see the entire look without actually wearing the dress. Also, you could change the background and see how will you look like in different lighting conditions.
We will be consuming AI in the future like we consume oxygen without knowing and paying much attention.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)
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- virtual reality
- eCommerce stores
- retail industry
- ecommerce store
- online buying
- business finance
- Computational neuroscience
- online reviews
- Social Media & Networking
- intuitive search experience
- smart machine learning algorithms
- computer vision technology