How AI is powering the transformation of the retail industry
Retailers must catch up with how recommendation engines are redefining customer experience, how retail business value chain transformation is shaping up, and how AI can enhance the supply chain aspects of their business.
The brick-and-mortar retail industry is not in a good moment right now. Much of the turmoil in this industry comes from the fact that consumers are seeking a richer and indulging retail experience that reduces friction – much like what they have now become used to as they shop online. Consumers also expect traditional retailers to capture the essence of their individuality - who they are, what they like, and how they prefer to consume information. Retailers need to understand and align themselves with the expectations of the consumers in order to increase profitability and customer loyalty. They need to not only be aware, but also go full throttle for adopting technologies such as AI for influencing and revolutionising customer behaviour.
Retailers need to explore use cases pertaining to exponential technologies to address the disruption that their industry is going through. They need to catch up with how recommendation engines are redefining customer experience, how retail business value chain transformation is shaping up, and how AI can enhance the supply chain aspects of their business. And as I mentioned, awareness is simply not enough – they need to assess and adopt these technologies on a war footing to survive in the world we live in today.
The data-powered disruption of retail
Data in the retail industry is increasing exponentially in terms of volume, variety, velocity – and more importantly – value with every passing year. Smarter retailers are increasingly aware of how every interaction between the business and customers holds the potential to increase customer loyalty and drive additional customer revenue. Retailers that adopt AI today have the potential to raise their operating margins by as much as 60 percent.
But just having the data and building reports that summarise customer behaviour at an aggregate level are not enough. Insights are important, no doubt, but retailers desperately need systems that can make actionable decisions from the data. Insights into average user behaviour is simply too broad. Retailers need to now create a meaningful dialogue with each individual customer that honours their shopper’s preferred level and mode of engagement. This requires more than summarised reports. It requires technologies powered by AI – the ability to minutely understand every customer individually and take actions that each individual customer expects.
We now live in a time where data-driven decisions are extremely pervasive and useful. So much so that the worlds of ecommerce and traditional commerce are seamlessly merging. Every company is now omni-channel. Whether you think of Walmart buying Flipkart to boost their online presence or you take Amazon purchasing Whole Foods to bolster their brick-and-mortar presence. It is not about the web, the app, or the store – it is about having all of those. With the quantum of data available, we’ve seen an extraordinary few years in the retail industry – in the sense that data is actively deconstructing and rebuilding what retail will look like tomorrow. Traditional incumbents need to pay heed to the warning signs signalled by their defunct counterparts and aggressively embrace the data-driven disruption of retail.
AI transforming retail
Predictive analytics has been used in retail for several years now. However, in the last few years – with advances in technology and artificial intelligence – we are seeing the high speed, scale, and value that predictive analytics can deliver. The AI-enabled world of retail helps business transition into a world where consumers are always connected, more mobile, more social, and have choices about where they shop.
Through AI innovations, retailers are starting to shift their businesses forward. While AI is continuously refining, expanding, and improving its utility for retail, here are some examples of how artificial intelligence can enable a paradigm shift in the world of retail.
Deep learning in commerce
The retail industry is one with a lot of benefit to be gained from deep learning, in part because it’s such a data-rich industry and because there is some momentum around AI gathering already. Further a lot of the AI techniques enjoying success in other applications across industries powered by deep learning are well positioned to make serious impact on retail, streamlining processes, and transforming customer experience into something that largely resembles the experience customers get when they visit online portals.
Deep learning has been the fuel for much of the recent success in applied AI, so it is not surprising that some of the first attempts at augmenting the offline shopping experience have been making use of the power of deep learning in classifying images. If you look at something like Shelf analytics to seek out merchandising effectiveness, you can see the beginnings of how deep learning fits snugly in a retail context.
Automated AI
The most readily used and utilitarian form of AI is automation. Machine learning is powering artificial applications that curate product recommendations without the need for explicit human intervention. Top tier players in AI are today capable of developing automated systems that can read digital user reviews, scour through past searches and purchases, and present product recommendations automatically.
Now with minimal effort, retailers that can leverage automated AI capabilities will see a strong rise in customer engagement and sales. The best part is – this can be accomplished by data that is already available to them and captured in their enterprise systems. There’s more. The algorithms required for powering these systems, such as collaborative filtering, are relatively simple to deploy and efficient to run.
Intelligent product searches
Another great use case for retailers is leveraging AI for powering intelligent product searches. By using AI, customers can take pictures of things that they see in the real world, or even in an ad, and then locate a retailer who has that item in stock. This can easily serve as the start of a shopping experience. Typically, consumers do often see something that they like, but do not know the name of the item, brand or where they can source it from.
But taking photos is not the only modality for shopping, and there are other areas in the shopping experience where AI can play a part. In online commerce retail, for instance, customers often know what they are looking for but do not know the exact search terms or must scroll through multiple pages of inventory to find it. Deep learning can be of help here as well. Auto-encoding features of images in an inventory based on similarities and differences brings about a rich model of what is available in the inventory, and the model is surprisingly close to how we, as humans, perceive shoppable items. The model alone, of course, is not enough: We need a way to understand a shopper’s preferences as they interact with the inventory.
Speed and communication in real time
Just a few years ago, retailers used to take weeks to analyse customer data and make product offers. Machine learning and AI are changing the game by streaming live data and curating products in real time – based on their understanding of each customer. This significant drop in the amount of time taken between receiving data and powering an intelligent decision is made possible by AI and helps improve the uptake of products from retailers. For instance, by using mobile geo-location capabilities retailers can now offer deals or promotions when customers walk into or near the store (not after they’ve paid at the checkout and departed). Mobile push notifications on company apps allow retailers to track engagement the second it happens.
Given this rapid evolution, retailers have many choices on how to use AI in their marketing and sales strategies. Consumers are seeking a richer retail experience that reduces friction while also capturing the essence of who they are, what they like, and how they prefer to consume information. The sooner a retailer understands this and creates the best consumer experience they can, the sooner they will increase profitability and retention rates. I predict that this retail revolution will continue to unfold and expand over the next several years. But as the industry expands one thing is certain: in retail, personalisation and the customer journey are key, regardless of how you get there.
The ‘segment of one’ approach
A generic, aggregative understanding of customer behaviour is no longer enough. Individual segmentation is the next step for retailers looking to create a super-personalised experience for their users.
Global experts such as Netflix have developed the ability to make hyper-granular behavioural profiles of the usage habits of their individual customers. And they have done it at massive scale. Can retailers with millions of customers make product recommendations based on each person’s unique shopping and browsing history? Retailers would do well to crack this puzzle using AI.
The worlds of traditional commerce retail and ecommerce retail are rapidly merging. I think ecommerce retail for many years was an interesting trend, but it was on the side, largely, of what was happening in retail. Today ecommerce retail is less an ancillary part of retail and more about the way business is now done. Online and offline experiences are fast coming together and without an omni-channel experience, it will be extremely difficult for a retailer to survive. That said, I do not doubt there is a future for brick-and-mortar retail, but there will need to be a transformation of retail real estate. Stores are going to become as much distribution and fulfilment centres as they are full-fledged shopping experiences. And they will need to be highly technology enabled.
This article is part of a series that will dissect the potential impact of artificial intelligence across various industries. Read the rest here.
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)