subject: Retail Information Analysis [print this page] Retail Information Analysis Retail Information Analysis
Analytics play a pivotal role in the info flow scheme at intervals a retail organization. A typical retailer generates more than thousands of knowledge points through POS machine. It is difficult for a retailer to make strategic decisions based mostly on this raw data.
A typical retailer has massive quantity of sales information stored in their systems. The new technologies have the ability to use these historical knowledge to improve retail productivity. To make sustainable advantage over competition, retailers are attempting to boost their product offerings, service levels and pricing models. To forestall worth attrition and to shield margins, retailers are attempting to cut back their value-to-serve per customer and thereby making certain that the overall price of possession of a customer over time is reduced. Managing promotional plans is another essential space for retailers to concentrate on and target customers additional effectively and efficiently.
Small and midsize retailers are facing problem with limited analytical resources to read the heart beat of their business processes. Retailers aren't ready to follow up with daily basis sales analysis, class analysis and complete share analysis for all the products.
Most retailers collect every transaction from every store, track every movement of products and record each client service interaction. Hence there is no shortage of knowledge, however how does one translate all that data into actionable data? How this information will be used to make better decisions? The most objective of a retail store IT department is to convert the raw information into valuable and helpful information.
Business analytics helps to urge insights from the structured data, such as sales and productivity reporting, forecasting, inventory management, market basket analysis, product affinity, customer clustering, customer segmentation, identifying trend, identifying seasonality and understanding hidden patterns for loss prevention and store administration.
Analytical techniques like statistical analysis, knowledge analysis and analytical tools facilitate in understanding patterns and trends at intervals large databases. When we use them for creating analytical models, they supply the sting to decision making. Whereas descriptive analysis helps to spot issues and examine causes, predictive analytics enhances the accuracy and effectiveness of call making process.
