How AI is powering the transformation of the retail industry?

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AI is powering the transformation

The brick-and-mortar retail industry is of course not in a good moment right now. Much of the turmoil in this industry does  come from the fact that consumers are  indeed 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 do expect traditional retailers to capture the essence of their individuality – which they are, what they like, and how they prefer to consume information.

Retailers do need to understand and align themselves with the expectations of the consumers in order to increase profitability and customer loyalty. They do need to not only be aware, but also go full throttle for adopting technologies such as AI for influencing and revolutionizing customer behavior.

Retailers do need to explore use cases pertaining to exponential technologies to address the disruption that their industry is going through. They do indeed  need to catch up with how recommendation engines  that are redefining customer experience, how retail business value chain transformation is shaping up, and how AI can rather  enhance the supply chain aspects of their business.

The data-powered disruption of retail

Data in the retail industry is rather increasing exponentially in terms of volume, variety, velocity – and more importantly – value with every passing year. Smarter retailers are no doubt  increasingly are aware of how every interaction between the business and customers holds the potential to increase customer loyalty and drive additional customer revenue. Retailers that do adopt AI today have the potential to raise their operating margins by as much about 60 percent.

But just having the data and also building reports that summarize customer behavior at an aggregate level are not enough. Insights are important, no doubt, but retailers desperately do need systems that can make actionable decisions from the data. Insights into average user behavior is indeed simply too broad. Retailers need to now rather create a meaningful dialogue with each individual customer that honors their shopper’s preferred level and mode of engagement. This does require more than summarized reports. It does require technologies powered by AI – the ability to minutely understand every customer individually and also take actions that each individual customer expects.

Nowadays data-driven decisions are extremely pervasive and useful. This means that the worlds of ecommerce and traditional commerce are seamlessly merging. Every company is now omni-channel. Whether one thinks of Walmart buying Flipkart to boost their online presence or one takes to 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.

AI is powering the transformationAI transforming retail

Predictive analytics has indeed been used in retail for several years now. However, in the last few years – with advances in technology and artificial intelligence – one is 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 rather always connected, more mobile, more social, and have choices about where they shop.

Through AI innovations, retailers are no doubt starting to shift their businesses forward.

 Deep learning in commerce

The retail industry has a lot of benefits to be gained from deep learning, as it is data-rich industry. Furthermore, 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 who get when they visit online portals.

Automated AI

Utilitarian form of AI is automation. Machine learning is no doubt powering artificial applications that involve curate product recommendations without the need for explicit human intervention. Top tier players in AI are off late capable of developing automated systems that can read digital user reviews, scour through past searches and purchases, and also present product recommendations automatically.

Now with minimal effort, retailers can leverage automated AI capabilities and will see a strong rise in customer engagement and sales.

Intelligent product searches

Leveraging AI for powering intelligent product searches is very useful. By using AI, customers can indeed take pictures of things that they see in the real world, or even in an ad, and then also locate a retailer who has that item in stock. This can also easily serve as the start of a shopping experience. Typically, consumers do often see something that they do like, but do not know the name of the item, brand or where they can source it from.

But taking photos does not happen to be the only modality for shopping, and there are other areas in the shopping experience where AI can indeed play a part. In online commerce retail, for instance, customers often do 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 of course be of much help here as well.

Speed and communication in real time

 Machine learning and AI are no doubt 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 also powering an intelligent decision is indeed made possible by AI and also helps improve the uptake of products from retailers

 Consumers are  indeed seeking a richer retail experience that does  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 also creates the best consumer experience they can, the sooner they will also increase profitability and retention rates.

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