The retail industry creates a wealth of information about its markets and customers. Big data sifts through this sea of facts and unearths nuggets of knowledge about consumer or market trends. What purpose is this data bank if merchants can't use it to increase their market share and sustain profitability? Decision-makers are better equipped to select the appropriate course of action when data is used effectively. Here, predictive analytics works well. Terabytes of data can be analysed, and a model that predicts sales growth based on historical trends can be created. This aids shops in staying competitive and expanding their market share.

60% of consumers, according to survey reoprts, consider pricing to be the primary motivator when making a purchase. They use data analytics models to track rival activity as well as inventory levels, demand levels, pricing, and more. Retailers can determine whether current pricing is appropriate or whether it needs to be adjusted based on the model's findings. The optimization aids in determining when it is best to modify pricing levels. Pricing is frequently modified based on client demand trends. Customer segmentation, regional data collection, mystery shopping, and other techniques are used to optimise prices. The pricing optimization model employs algorithms that analyse in-the-moment consumer reactions to prices, marketing initiatives, discount programmes, holiday specials, etc.