How AI can reinvent retail

Written with love on by Ionela Bărbuță

Posted in #Artificial Intelligence

Artificial intelligence adoption across industries is skyrocketing. A McKinsey Global survey revealed that 47% of the interviewed companies have embedded at least one AI capability in 2018, compared with only 20% in the previous year. Furthermore, 30% said they are piloting AI and 71% stated they expect the overall investment in AI to grow in the coming year.

One domain in which we can witness an increase in the number of AI-driven technologies used in the business processes is retail. A report from CB Insights shows that between 2013 and 2018, retail artificial intelligence startups raised $1.8 billion across 374 deals.

Artificial intelligence helps the retail sector tackle and solve a series of frustrating problems such as inaccurate inventory, overstretched and undertrained store associates, product placement and sub-optimal pricing. By processing huge amounts of information from different sources, AI systems are able to faster and better identify viable solutions.

In support of this idea is another study released by IBM earlier in 2019, which identifies six ways the retail industry plans on using AI, based on respondents' feedback:

·       Supply chain planning (85%)

·       Demand forecasting (85%)

·       Customer intelligence (79%)

·       Marketing, advertising, and campaign management (75%)

·       Store operations (73%)

·       Pricing and promotion (73%)

Even with such advent on the e-commerce front, brick-and-mortar stores are still here and are looking for innovative ways to remain at the forefront of the sales chain. So, AI can also play a crucial part in helping bridge the gap between virtual and physical sales channels.

From reducing shipping costs and improving supply chain efficiency to personalizing shopping experiences and helping workers acquire new skills, AI technologies allow retailers to compete in the 21st century economy and better serve their customers. This is what the future of retail looks like. — Mark Mathews, NRF vice president of research development and industry analysis

Revolutionized online and offline customer experience

Fashion e-retailer Farfetch launched a Store Of The Future platform for retailers, which allows businesses to collect customer data while browsing in-store. This is possible with the use of smart mirror technology installed in the fitting room that enables shoppers to request different sizes, colors or items. The customer’s information and shopping habits are then stored in Farfetch’s database so that the company can later access information about their personal style, previous purchases and browsing history. A retail location with embedded AI technology is a great way to gain insights into shoppers’ preferences.

Courtesy of Business of Fashion

Personali is also an AI platform which allows retailers to leverage behavioural economics and target customers with personalized offers. Personali’s Intelligent Incentive Algorithm is trained to analyze customers’ emotional responses and behavioural patterns during their previous shopping sessions. It then generates optimal pricing offers and incentives for each shopper.

Another creative way that retailers are trying to communicate with shoppers through personalized online experiences is by using conversational robots, chatbots and voice shopping.

Pepper, a humanoid robot that can interact with customers and “perceive” human emotions, has already become quite popular in Japan and in some parts in the US. Built in 2010, the  system was introduced in 140 SoftBank mobile stores in Japan. Furthermore, statistics show that a pilot of the Pepper robot in California’s b88ta stores in both Palo Alto and Santa Monica yielded a 70% increase in foot traffic in Palo Alto, and 50% of Neo-pen sales in Santa Monica. Nestle also announced that it planned to acquire Pepper robots to put in 1,000 of its Nescafe shops in Japan.

Inventory and shelving

To help retailers make better business decisions, the artificial intelligence systems they integrate needs to use cognitive search and machine learning capabilities. The part of AI that can handle inventory management and supply chain in a pragmatic way is prescriptive analytics.

Prescriptive analytics refers to a computer-based operation that uses raw data to automatically identify opportunities to enhance processes and client service, increase revenue and strengthen margins. As an example, a computer might identify that a precise product hasn’t been sold all day in a convenience store, despite seeming to have enough in inventory. The computer will then “prescribe” an action for the store clerk to correct the situation. Hence, he will be directed to go check the shelf and make sure the product is restocked, either from the back-room inventory or expedited shipment. Prescriptive analytics can identify the lost sale opportunities quickly, which means that, instead of the store going all day without selling a certain product, the shelves are stocked and sales return to normal.

Courtesy of Forbes

There are companies that also use robots to handle the inventory. In this case, the robots check the stock and order new products, if needed. Using robots for such activities is effortless and less time consuming. They are also used to pick out orders and move them as per the facilities, as their cognitive learning system and sensors allow them to understand the quality of the product.


Conclusions

The retail sector has always been a competitive one. For decades, retailers struggled to give customers what they wanted, be it more value, increased convenience, exceptional customer service, good prices, customized offers and various purchasing options to choose from. The amount of data that retailers needed to take into account to make optimal business decisions was immense and something would always be lost in the process.

That is why now the number of retail companies exploiting the potential of artificial intelligence is growing substantially. AI enables retailers to gather customer insights in an automated manner and make predictions based on identified patterns or previous images.

AI uses predictive patterns to better understand desires, motivations and actions across both physical and virtual channels. With this information, retailers will now be able to create more targeted and personalized marketing campaigns and improve trade promotion efforts. AI can also automate forecasting of inventory needs, more accurately predict out-of-stock incidences and ultimately help optimize supply chains.

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Ionela Bărbuță

Written with love by Ionela Bărbuță

Ionela is an enthusiastic professional, with a proven experience in the payments and fintech industry. She likes working with people, creating things, and writing about AI, security and fraud.