Retail analytics is the process of using big data to optimize prices, supply chain movements, and increase customer loyalty. Big data describes a large amount of data that is used to uncover patterns, trends, and associations, especially with regard to human behavior and interactions.
What is big data?
Big data is usually defined as heterogeneous and time-series data that is regularly generated by various IT systems and regularly stored and used in the business domain. By definition, big data, though heterogeneous in nature, is variable in size and volume, with an indefinite time dimension and a high frequency. The term ‘big’ is used to describe the available quantity of the data compared to small, variable data. In computing, ‘big data’ may refer to heterogeneous data and big is used to describe information having significant monetary value. The volume of big data in the business domain is usually in the hundreds of terabytes and in some cases, more than petabytes.
What is retail analytics?
Retail analytics combines quantitative and qualitative methods to solve problems and explore solutions using large volumes of data to anticipate and respond to customer needs. Retailers use the tools of big data analytics to set strategies and manage their operations, including purchasing, operations, marketing, finance, human resources, security, and sales.
Retail analytics involves four major components: analysis of customers’ behavior, organization and structure, technical tools and systems, and data and the collaboration between these three areas.
Big data analytics in retail and how to utilize it Retail analytics systems are sophisticated, involved, and utilize a variety of technologies, such as SAS, MATLAB, Hadoop, and Spark.
The types of data in retail analytics
In a recent study published in the journal econometrics and finance, it was revealed that retailers spend between 12% and 19% of their entire annual sales on making data-informed decisions.
Most of these decisions are made when retailers make these data-driven decisions, which are a prerequisite for achieving business objectives. These decisions can be more effective when they are made using the right data; in fact, retailers spend most of their funds on inputs that have little relevance for achieving their goals.
For example, retailers spend nearly 10% of their total sales on marketing, which leaves room for potential return on investment (ROI) in other areas. In 2016, 29.3 billion data records were recorded for the retail sector.
The benefits of retail analytics
When used in retail marketing, analytics help in making decisions regarding the effectiveness of product promotions, designs, customer profiling, and more. In the words of Cisco, Big data analytics are often used by retailers to uncover new opportunities for growth through combining traditional and digital customer data. Retailers can analyze customer preferences, behavior, and buying habits to discover new ways to improve business profitability. Additionally, analytics can help retailers optimize their supply chain management and improve efficiency and speed in inventory management while enabling them to identify patterns and assess inventory levels in real-time. It also helps in better predictions and reduction in returns and clearance costs.
Conclusion
Retail analytics is a new field, which is receiving increasing interest from retailers, and financial companies are becoming more active in it as well. Even today, the general consensus is that AI is too complex to be used to implement big data analytics in retail. However, things are rapidly changing, as the AI industry itself is evolving at a rapid pace, and it will soon become a mainstream technology.
Big Data Analytics in Retail Drones are being widely used in the supply chain of retailing, which is also expected to drive growth in the future. Consumer data analytics will become one of the major driving forces behind improving customer loyalty, personalization, and customer satisfaction in the future.