For decades, traditional analytics have worked perfectly fine for the data-driven retail industry. However, Artificial Intelligence (AI) and Machine Learning (ML) have introduced an entirely new level of data processing which leads to deeper business insights. Data scientists could open a new world of possibilities to business owners extracting anomalies and correlations from hundreds of Artificial Intelligence/Machine Learning models.
Between 2013 and 2018, Artificial Intelligence startups raised $1.8 billion in 374 deals, according to CB Insights. Amazon can take credit for these impressive numbers, because they made business leaders change their minds about Artificial Intelligence in the retail market – both physical stores and e-commerce strategies to stay ahead of the competition. At the moment over 28% of retailers are already deploying Artificial Intelligence/Machine Learning solutions, which is a sevenfold increase from 2016 when the number was only 4%.
Here at CSS, we know how retail businesses could benefit from AI because we have practical experience. We developed a system for product suggestions based on tracking a customer’s location and actions in a store. It was aimed to boost sales for shop owners while improving customer satisfaction by providing smart recommendations.
So, Artificial Intelligence solutions still have plenty of room to grow. However, we can already present to you some examples of real-world AI applications with proven business value.
The robotization of stores will result in reducing lines, lowering the number of human employees, and significant savings on operational expenses. Amazon AI has already introduced checkout-free stores. The Amazon Go and Just Walk Out Shopping technology react when you take something from the shelf or put it back. When you walk out of the shop with products, the Amazon account will take money for your purchase. Amazon wants to make more shops driven by Artificial Intelligence like Amazon Go, where only six to twenty human staff members are needed.
AI chatbots provide an even higher level of customer service, improve searching, send notifications about new collections, and suggest similar products. If a customer already bought a black hoodie, a chatbot can suggest a snapback to match — and the look is complete. Eighty percent of brands worldwide are already using or going to use chatbots in the near future. Tommy Hilfiger and Burberry have launched chatbots to help their customers navigate through their collections.
Retailers also invest in technologies that help customers in the shopping process and also help staff in stores. Kroger Edge technology eliminates paper price tags in their stores; smart shelf tags are now used. This technology also provides video ads, nutritional info, and promotions on the displays. Lowebot, an autonomous in-store robot from Lowe’s, helps customers find what they need in the store in different languages. At the same time, it helps with inventory management thanks to real-time monitoring capabilities.
Applications of AI for retail stores could help businesses set prices for their products, visualizing the likely outcomes of multiple pricing strategies. To be able to execute this, systems collect information about other products, promotional activities, sales figures, and additional data. Business leaders can present the best offers and get new customers and boost sales as a result. eBay and Kroger already apply Artificial Intelligence for their price optimization and stay flexible with their ability to adjust prices and promotions according to the information obtained.
Price forecasting is a prediction of the price of a product based on demand, seasonal trends, characteristics, the release date of new models of the same item, etc. Its obvious implementation lies in the travel industry; however, it could be used in retail as well. Just imagine an app or service that helps your customers know beforehand how the price for a certain product will change. With Artificial Intelligence, this is possible and it is very easy to implement. A Price prediction feature could help you build customer loyalty. Predictive Analytics and Machine Learning in the Retail Industry, however, could achieve much more than just a price prediction.
Poor execution in this area leads to losses for retailers around the world in the amount of about $1.1 trillion every year. Leftovers and out-of-stock scenarios can be eliminated. AI in the retail supply chain can be used for restocking — calculating the demand for a particular product by taking into account a history of sales, location, weather, trends, promotions, and other parameters. Morrisons has made a better situation with stock forecasting and replenishment in 491 stores with the help of BlueYonder. It resulted in an up to 30% reduction of in-store shelf gaps.
CSS is a great example of Machine Learning in the Retail Industry — it uses Machine Learning Models to classify over a million items from various sellers. Systems based on Machine Learning tag goods and sort them in different categories for customers who are seeking a particular type of product. Lalafo sellers can just upload the image of the products they want to sell and Machine Learning retail software with computer vision would recognize it.
Visual Search systems powered by Artificial Intelligence allow customers to upload images and find similar products based on colors, shapes, and patterns. Image recognition technology from Cortexica promises close to 95% accuracy. Customers approved The Find Similar feature with 90% positive feedback. The IR technology of American Eagle uses Visual Search — which not only helps people get the same or similar clothes, but also suggests what would go well with it.
Walmart, Tesco, Kohl’s, Costco, and many other big brands use Google or Amazon AI technology to provide customers with simple and quick voice search. Now customers can just ask Alexa for the desired item and its delivery status without typing anything. In fact, 27% of people worldwide use voice search on mobile, and 52% of them prefer it to mobile apps and websites for their convenience.