Fashion, inventory, investments, and online platforms are some of the areas of retail sector in which big data analytics can play a crucial role in decision making and attaining growth.
Big data analytics has become a buzzword across different industries as the need of insights extracted from data increases for decision making and achieve targets. With a huge amount of data generated in the retail sector, it is important to extract valuable insights such as buying preferences, changing trends, and peak performance from the data for efficient management and decision making. Valuable insights can always be a blessing for market players operating in the retail sectors to know customer buying patterns, grow their businesses, expand into new regions, and take investment decisions. The application of big data analytics would grow in the retail sector in the coming years. According to the report published by Allied Market Research, the global big data analytics in retail market is expected to generate $17.85 billion by 2027. Following are some of the ways big data analytics can be useful in different ways for the retail sector.
Big data analytics in fashion retail:
Though the equation big data and fashion seems odd, many fashion labels, stores, and industry players have been leveraging benefits of big data analytics for different applications. Rapidly changing trends, lack of unified sizes, and budgets of customer are some of the major factors that influence the fashion retail industry. Determining customer behavior and buying patterns from a huge amount of data can give valuable insights to players along with giving a competitive advantage. With the help of data-driven sentiment analysis on social media and other platforms, market players can determine trends and take necessary steps. Another major application of big data analytics is the identification of target markets. It is possible that the trend in the U.S. may have become irrelevant in the European region. So, determining the target region to sell the product will offer profitability and competitive advantage.
Big data analytics in retail inventory management:
Manual management of inventory can work when the inventory is small in size. However, it will be tiresome and inefficient task when it comes to management of large inventory. Utilization of advanced machine learning techniques and advanced algorithms can determine patterns and correlations. These insights would be helpful for taking supply chain decisions on when to order, what to order, and how much stock is remaining. The efficient management can free up the space for inventory and help managers in making rapid decisions. This also frees up time of managers and owners to focus other tasks.
Big data analytics for retail investors:
Making smart investments that would give optimum return on investments is one of the priorities of investors. Those investors looking for opportunities in the retail sector can leverage the capabilities of big data analytics to make investment decisions. With the help of data-driven insights gained through advanced machine learning algorithms and quantitative investing methodologies, investors can gain unique investment ideas and an edge in the market. Advanced APIs and platform capabilities would help retail investors in achieving their targets through intelligent investments. Determining the performance of the organizations and various sectors based on data and extracting insights to help investors in gaining market insights and take further steps.
Big data analytics for online retailers:
Online retailers can utilize the capabilities of big data analytics in different ways. One of the crucial ways includes recommendations. On the basis of previously bought items and ratings given to those products, retailers can recommend other products to boost sales. Through the implementation of artificial intelligence (AI) algorithm on online platforms, retailers can show relevant products for upsell and cross-sell to their target customers. This would improve the probability of further sales and enhance brand loyalty among customers. AI algorithms can provide a personalized shopping experience and readymade solutions.