Top 5 Applications of AI in Retail

A digital revolution

The digital revolution of the retail sector has provided an unparalleled opportunity to change the way people shop for products.

Artificial intelligence and machine learning provide solutions designed to personalise the shopping experience to each individual customer. Operational research and automation are leveraged to optimise key business processes and maximise operational efficiency. Not only can AI help boost the bottom line of a retail organisation, but thanks to highly personalised experience and increased customer satisfaction, businesses adopting AI are also likely to accelerate growth like never before. So, how exactly is AI helping the retail sector? Here are five good examples.

Recommendation Engines

Advanced e-commerce platforms are accustomed to learning customers’ preferences through repeated interactions.

AI uses this data to develop a detailed customer profile, with a goal to create personalised shopping experiences over time. Recommendation engines are then applied by analysing information on users’ interests, preferences, demographics or behavioural history. Recommender systems apply collaborative or content-based filtering to understand the patterns and relationships between these datapoints and draw meaningful conclusions on which products are most likely to be of interest. These tailored personalised experiences not only make shopping more pleasant and efficient for the buyer, but also increase the customer lifetime value and add loyalty. “Customer who bought this item also bought…” on Amazon is a good example of recommendation system working well in practice.

Customer Support

Online chatbots have been one of the most revolutionary inventions for customer support in the past decade.

AI-powered conversational assistants deploy natural language processing (NLP) for assisting consumers and enhancing the customer experience by offering 24/7 support. The chatbots can engage with customers by answering their queries, troubleshooting, and guiding them to desired outcomes. This process streamlines staffing whilst gathering customer data which can be utilised to inform future business decisions. Nearly all online retailers have launched chatbots to help their customers, and although many of them are far from perfect, they are likely to become more accurate and helpful over time the more data they collect.

Product Categorisation

AI combined with machine learning can help regulate stores and keep a check on product categorisation.

Machine vision-based image classification tools can compare the images of items to find a degree of similarity to the large repository of product categorization data already available, to then accurately match and tag items. This can serve as a guide for people looking for a specific product, leading them to the right section instead of rummaging through unwanted products. One interesting example of such AI-enhanced C2C marketplace is Lalafo[1] which applies computer vision and NLP to reinvent the way people sell second-hand items such as cars, find real estate, jobs and services. The retail software uses computer vision to recognise the product from an uploaded picture, classify it, and suggest a price. The platform processes more than 900 requests in a second, improving sales with relevant content.

Logistics and Supply Chain Planning

AI-powered logistics management systems optimise retailers’ delivery schemes and inventory in real-time, providing an opportunity to reduce operational costs and maximise work efficiency.

Poor supply chain management leads to losses for retailers around the world of about $1.1 trillion[2] every year. Using predictive analytics models to adjust production and stock control leads to better warehouse efficiency and less costs for unsold inventory.

As an example, Morrisons has used the help of a platform BlueYonder[3] to improve stock forecasting and replenishment in 491 stores. It resulted in an estimated 30% reduction of in-store shelf gaps.

Price Regulation Strategies

AI can help businesses set prices for their products more competitively to intelligently increase their market share.

By reading data from the business’ earlier transactions and from the market information of product’s actual cost, promotional activities, and sales figures, AI can visualise the outcome of different pricing strategies to generate profits with a higher efficiency. Predictive analytics provides valuable information on demand, seasonal trends, characteristics, likely customer choices and the optimal release date of new models that influence the key price point decisions. As an example, eBay applies AI[4] to stay flexible with their ability to adjust prices and promotions according to industry-wide and customer-specific information gathered on their platform.

AI and machine learning-based solutions help retail businesses accelerate growth. AI algorithms have proven to have the power to increase sales and effective customer engagement, giving the AI-enhanced businesses a competitive advantage over the rest of the market. For a customer, the AI revolution means a more pleasant, personalised shopping experience designed to minimise time spent shopping and maximise satisfaction with the product. Hopefully this revolution in the coming decades will give us an opportunity to shift focus from mundane day-to-day activities such as shopping, and free up our time to focus on the things that matter the most.

[1] https://play.google.com/store/apps/details?id=com.lalafo&hl=en_CA&gl=US
[2] https://spd.group/artificial-intelligence/ai-for-retail/
[3] https://blueyonder.com/
[4] https://www.analyticssteps.com/blogs/6-applications-ai-retail-sector

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