The Rise and Fall of the Mail Order Business

The Rise and Fall of the Mail Order Business

The mail order business was, for a long time, an independent business and, for a while, a subset of retail business—a format that enabled customers to access products from far and wide. The mail order business took off in the late 19th and early 20th century, specifically in the US market, with companies such as Sears, Roebuck & Co. and Montgomery Ward offering a wide range of products to their customers. By producing attractive catalogues with thousands of products, they could reach customers far and wide—those who did not have access to such options otherwise. Rural customers in the American markets found this particularly advantageous coupled with the offer of return and refund if they didn’t like the products on arrival and, in later years, payment in instalments too.

After a while, the mail order business model became international, and in the era after the Second World War, the patronage of the expanded base of customers helped the businesses to increase their revenues substantially. At its peak, in the early 1980s, the mail order business reached an estimated annual revenue of US$30 billion with a significant share of the business with companies such as Sears, JCPenney, Montgomery Ward and Spiegel.

In time, mail order business customers embraced e-commerce. Instead of physical catalogues, digital content provided information about the products in much more user-friendly formats, enabling access to a vast range of products at competitive prices, enhanced flexibility, convenience, independence and support where required to make purchase decisions. New businesses like Amazon have come into the e-commerce space with reduced overheads, building upon the advantages of mail order businesses and in-person retail formats in life-altering ways. Mail order retail laid the foundation for today’s e-commerce: the key tenets for the retail business, the convenience factor and access to a wide choice of products have been drawn from the mail order business and embedded into e-commerce.

Since 2010, retail has transitioned from the traditional marketplace recreated on the internet where buyers and sellers interact directly or through intermediaries to influence marketing and social commerce. User-generated content is being effectively utilised to reach out to audiences who are sliced and diced and precisely targeted based on the enormous data being generated through every touch point of the customer. Use of mobile phones as a medium of connection to the retail experience has resulted in mobile-first designs making the buying process simple and easy to handle.

AI Applications in the Retail Industry

Retail is one of the domains where AI and other emerging tools are already being deployed for gathering insights regarding buying patterns and coming up with solutions that enhance customer delight. Hyper-personalisation, prediction of customer needs and preferences with greater accuracy and perfection of the buying experience across online and offline modes would be the focus of AI and digital technologies in the coming years. In this context, let us examine the domains in retail where AI solutions would have a significant impact and how retail businesses should be equipped to implement AI solutions.

AI-led Seamless Shopping Experience

The shopping experience is one of the applications where AI is being used to make security and the buying process seamless. While face recognition and biometric payments create enhanced security that identifies suspicious behaviour and enables quick access without the need to use passwords, the checkout technologies being implemented by companies like Amazon Go remove the hassles of payments and time spent in long queues for checkouts. To maximise sales and create customer delight, with the help of geolocation and data analytics, AI recommendation engines could suggest real-time discounts to the stores to be made available to the individual customer or a group of customers who are in the vicinity of the store, thus making the entire shopping experience much more engaging and purposeful.

Personalised Recommendations

Current recommendation systems are built around patterns and trends of datasets of similar customers and their shopping preferences, buying behaviour and browsing history. Future recommendation systems would be hyper-personalised, that is, the recommendations would be highly personalised and tuned to individual preferences and buying patterns, and context-specific based on situations or real-time needs. For instance, as you are walking up the street at dinner time, it would suggest restaurants offering your dietary preferences, or on a hot summer day, the recommendation engine could suggest ice cream options of your choice available at special prices. Visual- and style-embedded recommendation engines could also have the ability to remember and contextualise the needs of the individual and suggest the right options instead of the shopper having to recall/know the product or style.

In stores where there are store assistants stationed, they could be empowered on a real-time basis with customer data indicating their preferences of products, style and price points based on their previous browsing history or purchases, thus enabling them to personalise the sales process and minimise the time for sales conversion.

For instance, in a store selling expensive ortho footwear, if the store assistant has access to a customer’s previous purchase or browsing history, the recommendation engine would indicate options that the store assistant could provide to the customer, thus making the shopping experience personalised and satisfying.