Never before have customers been more in control of the retail trade than today. But are they really? Or has the retailer wrested control of the exchange? Let’s revisit this in the light of new technologies and sensors deployed in this “game”.
In the sixties through the eighties, the Sears, Walmart and K-mart kind of super stores aggregated purchase information to decide what to buy and stock their shelves. Improving the scale of procurement they drove down the purchase price of things like Levi’s Jeans to the detriment of the manufacturer. Superior logistics, data warehousing, POS systems, and strategic placement of stores – the retailer was in total control of the retail trade. With the advent of the Internet everything changed. The balance of power was in favor of the retailer. The customer was just the bystander in this game of thrones. The retailer squeezed the manufacturer through logistics, aggregation of demand, not sharing information about the end customer with the manufacturer, among other things. Information was deployed as a weapon in the negotiations between the retailer and the supplier.
With the advent of the Internet, the suppliers set up their own storefronts on the Web in order to reach out to the customer. By capturing the customer’s buying behavior suppliers were able to reach the former directly and thus, effectively improve margins. Third party logistics (TPL) companies became the best friend of the manufacturer. By tracking parcels every step of the way the supplier established a personal relationship with every customer.
But there was still a problem of the plenty. The data volume soon overwhelmed the processing power of human analysts. There was a need for self learning programs that could analyze the data instead of human beings. This is where Artificial intelligence (AI) got introduced to retail.
AI programs perform better with more data. The more the data, the better the prediction. AI is changing a lot of industries but where its impact is visible the most perhaps is the e-commerce space. Because retail is an industry rich in data, AI can show its influence more clearly.
Let’s look at some of the ways in which Artificial Intelligence has impacted e-commerce today for the retailer:
In today’s intense competition in the retail industry, every cent matters. The difference of even a few dollars in the price of related products or services could mean the loss of a customer. Product pricing is one of the biggest challenges that retailers face today, especially while selling on e-commerce platforms such as Amazon. Competition is such that it’s no longer enough to change the price of a product at previously-seen frequencies such as every week or a month.
Because of retail coming online, and a market that’s getting more and more omnichannel, market dynamics must operate at a granular level now, where operations are measured in minutes and hours, not days and weeks. So much so that the price of individual products must be adjusted as frequently as possible in response to market demand. That’s quite a challenge, and something that no human can handle. But with the advent of AI, repricing products in a ever-fluctuating market no longer poses the challenge that once used to be.
In a fast-changing retail market, inventory management constitutes yet another major challenge which is now being easily tackled by AI algorithms. Until now running out of stock was every retailer’s recurring nightmare, while overstocking meant the need for extra capital to meet inventory cost, rentals, and the fear of a commodity perishing if not sold within time.
Traditional forecasting inventory velocity methods just cannot keep up with the vagaries of a constantly fluctuating market. AI technology now design predictive analytics models that, for example, can identify key factors affecting the velocity of orders. Machine learning techniques also means the system getting smarter over time as more and more data is fed in and read, thus helping retailers predict their inventory needs with a high degree of accuracy, even in a highly volatile market.
Machine learning has also helped in another area of retail – managing the product range or line. Retailers must figure out which products are flying off the shelves and which are not, and react accordingly. Assortment planning requires forecasting where analysts have to monitor market trends and changes in customer demand. It requires a highly sophisticated analytics model, and this is a job that is now being competently handled by AI in many companies.
Successful case studies
- Amazon’s virtual assistant, Alexa, is a classic example of the use of AI in retail. Alexa’s been integrated into Amazon’s own products like Echo, as well as products from other manufacturers. A recent update now means customers can arrange a Uber pickup using Echo, or order food online.
- American stock photo company Getty Images uses an AI-driven predictive lead scoring software to find out websites that publish images belonging to its rivals. This gives the marketing and sales team a way to target prospects more effectively.
- Coffee giant Starbucks has been using AI to analyze all the data it has gathered on its consumers in order to deliver highly personalized suggestions. It recently launched ‘My Starbucks Barista’, which uses AI to allow coffee drinkers to place orders with voice command or messaging.
In the 2nd and last part of this post, we shall see how AI has affected the customer’s journey.