Artificial Intelligence in Retail - Part 2

 



In part 1 of the article on Artificial Intelligence in Retail, we saw how AI is transforming the retail industry and helping the industry grow in sales and profits. Let us dive deeper into the topic with some live examples.

AI refers predominantly to computational technology driven by ways in which people use their brains’ neurons and nervous systems to reason and make conclusions and decisions, although they usually work very differently.

There are several ways through which retail can benefit from the robotic system. For instance, the attractiveness of robotic technology will encourage customers to engage more in shopping activities, and this will help the retailers boost their sales. Customers will benefit from faster and intelligent guidance during shopping and thus make more effective and efficient purchase decisions. Robotic technology will support retailers’ endeavours towards minimizing the personnel costs and enhancing the staff well-being.

But consultant jargon and terms are one thing: translating data into merchandising and management practices is much harder. Yet the examples of how this is being done are both insightful and often so eye-opening as to almost border on common sense once they are better understood.

Consider these cases of how companies are helping retailers use customer experience analysis, not just after-the-fact questionnaires or bot-driven online surveys:

British chain Marks & Spencer analyzed its luggage sales and realized that the same product sold much better online than in-store. Working with an outside analytics company revealed that the duffle bag in question was shown fully stuffed online but empty and a bit crumpled in-store. By assigning a numerical value to each sale the retailer was able to understand it should show the bag stuffed in-store as well. As soon as it did, sales rose to match the online rate.

Western-style boot retailer Dan Post wanted to understand how country of origin would impact the relative value of its products. It tested the same exact style boots at the same price but listed one as made in China, the other made in Mexico. Consumers perceived a lower value to the Chinese-made boots even though they were identical. The company then knew if it did move its production to China it would need to price its boots lower to reflect consumer sentiment.

Rue21 used the current pandemic work-from-home conditions to reexamine its entire product development and testing process, eliminating physical samples sent from factories to its buying office and replacing them with sophisticated digital images. By streamlining the process it has saved time, saved money and allowed for a more efficient product development cycle.

Back at M&S, the retailer was introducing swimwear made of recycled polyester and wanted to know how to name the product and whether to play up the sustainability angle. It tested the same product under four different names and using data scoring found that calling attention to the eco-aspect resulted in a higher rate of sale than a more fashion-oriented brand.

Companies offering these services like to say it’s all about “understanding more through better listening.” It sounds simple but getting the data in the first place and then understanding how to apply it to your business is anything but simple. Analytics may not tell the entire story but retailers are finding they increasingly help with the narrative.

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