The retail sector is one of the prominent sectors to use analytics for deriving insights so as to boost sales. With Google claiming that the focus is shifting from business intelligence to business analytics, the retailers are taking the pragmatic approach of storing and analyzing the humongous amount of data. The data being stored ranges from simple purchase order to customer data being pulled from social networking sites like Facebook and tweeter.
With the advancement in big data technology, it has now become possible to store and analyze data in the order of petabytes. Apart from deriving the KPI from historical data ( the classic case of BI) retailers are focusing more on deriving the insights and predicting the future events (Business Analytics or Predictive Analytics). Though the online retail stores like Flipkart and Amazon seems to be in the forefront of the action, retailers having the traditional business models have also started heeding towards business Analytics.
In a retail domain, the focus is more on increasing the sales by predicting the customer behavior rather than increasing the efficiency of product manufacturing and distribution by predicting the trends in demand and identifying the possible bottle-neck. Currently, there are a lot of commercial offerings in terms of a recommendation engine, which claim to increase the conversion rates multifold but there is the shortfall of offerings which address the problem of traditional retailers. Predictive analytics can be used in multiple ways to increase the efficiency and profitability of a business.
Analyzing the POS data for Better production and supply chain management – The point of sale data can be used to analyze the user preference for different products and their demands can be analyzed in real time. This data can further be used to predict the demand in future. This will help the manufacturer to plan the production and stocking of product efficiently. The problem of overstocking and nonperforming inventory can be minimized by predicting the future demands. One or more of the algorithm like linear regression, trend analysis, seasonality etc. can be used for prediction.
Having realistic sales target by forecasting the sales – By predicting the demand for various products, manufacturers can have realistic figures for sales and revenue. By predicting the sales, Sales target can be kept reasonable and practical to achieve. Predicting the demands of products in future can help bringing stability to the market. With realistic sales target, organizations can align their sales team to work efficiently and focus on emerging markets.
Analyzing the feeds from social media to engineer new products – Sentimental analysis is being used now a days to analyze the opinion of customers for any given product. This information can be used further to improve the product quality by fine tuning the product features so as to address the customer requirements in a better way. By clustering the products based on their features, new products can be designed with features having higher demands and acceptance. This can further reduce the cost of planning the product and its subsequent launch in the market. Various clustering and collaborative filtering algorithms are available to group the products having similar features. These algorithms are also used to predict the user preference which can be helpful for deciding the product features targeting a specific customer group.
Though the use of Artificial intelligence and machine learning in Bricks and Mortar stores seems to be a distant dream there use in the future is inexorable. Predictive analytics is a step forward in that direction.