Retail Data Sales Analytics

Sales Analytics - Analytics plays an important role in data flow schemes within retail organizations. Typical retailers generate more than thousands of data points via a POS machine. It is difficult for retailers to make strategic decisions based on this raw data. Typical retailers have large amounts of sales data stored on their systems. This new technology has the ability to use this historical data to improve retail productivity. To create a sustainable advantage in competition, retailers are trying to improve product offerings. Resellers are trying to reduce the cost they should pay per customer and thus ensure that the total cost of subscribers' ownership over time decreases.

Managing a promotional plan is an important area for resellers to focus and target customers more effectively and efficiently. Small and medium retailers face problems with limited sources of analysis to read their business process pulses. Resellers can not follow up on day-to-day sales analysis, category analysis and sales analytics brand for all products. Most retailers collect every transaction from each store, track every move of goods and record every customer service interaction. Therefore there is no shortage of data, but how does one translate all that data into actionable information? How can this information be used to make better decisions? The main purpose of the IT department of retail stores is to convert raw data into valuable and useful information.

Sales analytics this helps gain insights from structured data, such as sales and productivity reporting, forecasting, inventory management, market basket analysis, product affinity, customer clustering, customer segmentation, trend identification, seasonal identification and hidden pattern understanding for prevention and storage of losses. Administration, analytical techniques such as statistical analysis, data analysis, and analysis tools help in understanding patterns and trends in large databases. When we use them to create analytical models, they give the edge of decision-making. While descriptive analysis helps identify problems and examines their causes, predictive analysis increases the accuracy and effectiveness of the decision-making process.