AI-based Data Management with Retail Industry Case Study
AI-based data management involves leveraging artificial intelligence to automate and optimize the processes of collecting, storing, and utilizing data in order to help retailers make informed, data-driven business decisions. Artificial intelligence in retail includes machine learning, behavioral analytics, customer intelligence, predictive analytics, bots, text analytics and deep learning to get insights (Marotta, n.d.). Unlike the times when solely Relational Databases and Data warehouses were used, Big Data datastores like noSQL databases and data lakes have also been introduced to the data management process (Kapoor, 2020). Introducing artificial intelligence to data management transforms the system into intelligent, self-learning systems, and automating the process of data management introduces higher levels of efficiency. The effectiveness of AI-based data management, however, is largely dependent on the quality of data and the infrastructures available.
According to a study by IBM, the retail industry will utilize AI in supply chain planning, demand forecasting, customer intelligence, marketing, advertising, and campaign management, store operations, and pricing and promotion (Bayern, 2019). In the retail industry, the use of chatbots to collect and analyze customer data have become more prevalent. The bots help customers by answering common questions and referring them to the appropriate place to get answers. Retailers can use the data collected to customize the experience of each customer. AI also empowers retailers to correctly predict customer behaviors based on past actions. Using AI in data management has eliminated the inefficiencies and human error associated with manually managing data.
Artificial intelligence can also be used for inventory management in retail by developing demand forecasts. Insights are mined from various sources and the business can use the insights to make proactive changes to the ordering of inventory, marketing, and supply chain planning. AI tools can predict shifts in the industry.
Thanks to artificial intelligence, many systems can now analyze previous behaviors by customers to suggest products that are frequently bought together, related products, or recommend items that a shopper might like. Additionally, algorithms collect and analyze data and customer feedback to guide businesses in creating better deigns, better products that will remove gaps from the market and increase customer satisfaction.
The IBM Watson Cognitive Computing has vastly improved how customers engage with eCommerce retailers. 1–800-Flowers.com launched Gifts When You Need (GWYN) using this technology to tailor gift recommendations. The customer doing the shopping inputs information about the recipient of the gift and the system compares the information against the database to find gifts that were purchased in the past for people with similar characteristics. The new system became so popular that 70% of online orders were completed via this system in two months (Mejia, n.d.).
In the case of Walmart, the Intelligent Retail Lab was developed. This lab is equipped with thousands of cameras that send information about inventory and the shopping behaviors of customers. The information from the cameras is sent to a database for a machine learning model. Some of the AI functions are monitoring of the opening and closing of registers, product inventory and determining the availability of shopping carts (Mejia, n.d.). For example, the system can detect if a shelf is close to being empty and needs to be restocked and it sends a message to staff to do so. It can also keep track of the color of fruit as it ripens and inform staff on when fruit has exceeded the shelf life and needs to be discarded. One of my favorite functions is the cameras being able to determine if there is a need to open a new register by comparing the number of customers waiting in queue to the number of operational registers.
Introducing Artificial Intelligence to data management has numerous benefits for the retail industry. Retailers are constantly compiling data about customers from their purchases and behaviors and a lot of this data becomes “dark data” that have been accumulated but people are unaware of. Combining the power of machine learning and algorithms can determine how the various data can be sorted into useful piles. Artificial Intelligence can also learn from the use of previous data to identify data that is never used and suggest that it be discarded to conserve space. This saves human capital time that would have been spent looking through the data to determine what is obsolete or irrelevant. Artificial Intelligence has also revolutionized how data storage is utilized. “Smart” storage devices are able to determine how often data is used and transfer the seldom used data to slower storage, thereby automatically optimizing storage and reducing storage cost.
AI-based data management allows a business capture more of the market share by engaging customers in unique ways. The predictive capability of artificial intelligence allows retailers to be proactive and innovative industry leaders, rather than being reactive to trends. Additionally, AI allows for easy integration between the physical and digital retail businesses and reduces operational inefficiencies which alienate customers.
References
Bayern, M. (2019). 6 ways AI will revolutionize retail.
<https://www.techrepublic.com/article/6-ways-ai-will-revolutionize-retail/>
Kapoor, S. (2020). AI and Databases: A Symbiotic Relationship.
<https://www.itexchangeweb.com/blog/ai-and-databases-a-symbiotic-relationship/>
Marotta, D. Artificial Intelligence: How AI Is Changing Retail.
<https://global.hitachi-solutions.com/blog/ai-in-retail>
Mejia, N. Machine Learning in Big Box Retail — Walmart, Target, and Costco.
<https://bit.ly/3lfXUe1>
