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, Sears, Walmart, and K-mart kind of superstores aggregated purchase information to decide what to buy and stock their shelves.
By increasing procurement scale, they drove down the purchase price of items like Levi’s Jeans to the detriment of the manufacturer.
Superior logistics, data warehousing, POS systems, and strategic store placement – the retailer was in total control of retail trade.
With the advent of the Internet, everything changed. The balance of power was in the retailer's favor. The customer was just a bystander in this game of thrones.
The retailer squeezed the manufacturer through logistics, demand aggregation, and a lack of information sharing about the end customer, among other things.
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Information was deployed as a weapon in the negotiations between the retailer and the supplier.
With the advent of the Internet, suppliers set up their own storefronts on the Web to reach customers.
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 have become the manufacturer's best friend.
By tracking parcels every step of the way, the supplier established a personal relationship with every customer.
But there was still a problem of plenty. The data volume soon overwhelmed the processing power of human analysts.
There was a need for self-learning programs capable of analyzing data rather than relying on human beings. This is where Artificial Intelligence (AI) was introduced to retail.
AI programs perform better with more data. The more the data, the better the prediction.
AI is changing many industries, but perhaps its impact is most visible in e-commerce. Because retail is an industry rich in data, AI can show its influence more clearly.
How Artificial Intelligence has impacted e-commerce today for the retailer
Pricing
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 losing 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 weekly or monthly.
Because of retail coming online and an increasingly omnichannel market, market dynamics must operate at a granular level now, with operations 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 an ever-fluctuating market no longer poses the challenge it once did.
Inventory
In a fast-changing retail market, inventory management is yet another major challenge that AI algorithms now tackle easily.
Until now, running out of stock has been every retailer’s recurring nightmare, while overstocking has meant the need for extra capital to cover inventory costs, rentals, and the fear that a commodity would perish if not sold within a specific time.
Traditional forecasting inventory velocity methods cannot keep up with the vagaries of a constantly fluctuating market.
AI technology now designs predictive analytics models that, for example, can identify key factors affecting order velocity.
Machine learning techniques also mean the system gets 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, in which analysts monitor market trends and changes in customer demand.
It requires a highly sophisticated analytics model, and this job is now being handled competently by AI in many companies.
Stocking the Store: Stock the store smart.
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Successful Case Studies of Artificial Intelligence in Retail
Amazon’s virtual assistant, Alexa, is a classic example of AI in retail.
Alexa’s been integrated into Amazon’s own products like the Echo, as well as products from other manufacturers.
A recent update now lets customers arrange an Uber pickup using Echo or order food online.
American stock photo company Getty Images uses AI-driven predictive lead-scoring software to identify 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 to deliver highly personalized suggestions.
It recently launched ‘My Starbucks Barista’, which uses AI to let coffee drinkers place orders via voice commands or messages.
In the 2nd and last part of this post, we shall see how AI has affected the customer’s journey.
