The inevitable aim of every retail business is to maximize the sale. And to do so, the retail honchos use analytics. Before we engage in the uses of analytics in retails, let us first understand retail analytics.
Retail Analytics is any statistics which assists retail honchos in making intelligent decisions and managing their business proficiently. The focal point of retail analytics is to provide cognizance regarding sales, inventory, customers and many more facets which are pivotal in decision making for the business. Now, advancing towards the uses of analytics in retail, we get to know the following:
Consumer Data – The necessary data is collected and analyzed by the retailers identify what measures are to be taken and also what implementation process is required to drive customers to make more purchases.
Consumer behavior – Retail analytics helps to predict the behavior of consumers. The response can be noted, and thus a good knowledge about the behavioral patterns of consumers can be established. These patterns can further be employed towards enhancing consumer satisfaction, improving customer service and upgrading overall customer experience. The foundation of understanding consumer behavior is about having computed data on actual consumer activities. These include data on the frequency of purchasing done by customers, their duration of visit, the area of purchases, price sensitivity, their acknowledgment to promotional activities, etc. Predictive analytics processes this information to forecast future buying behavior of consumers.
Modulating marketing mix
1) Product- Predictive analytics helps better understand the need and wants of the consumers. Such statistics is essential for the development of existing and new products. The analyst could get to know which product to focus on and which one to discard.
2) Price- Predictive analytics provides necessary information and price sensitivity survey as essential in the pricing process.
3) Place- The retailers decide their channel of distribution and medium of delivery with the help of predictive analytics. It also helps retailers to know about the geographical concentration of the consumers which makes a comfortable and appropriate distribution.
4) Promotion- With predictive analytics, it becomes easier for retailers to differentiate between buying and non-buying customers and thus perform promotional campaigns accordingly.
Supply chain management – Along with effective costing, timely delivery of the product to the customers is also of vital importance for customer satisfaction and retention. Hence predictive analytics has become a tool of utmost importance in supply chain management.
With the above uses, it is evident that analytics has created immense opportunity for the retailers by providing future marketing forecast.
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