Web Analytics

Part I

Analytics play a central role in the data flow control within a retail organization. A typical retailer generates more than thousands of data points through the POS machine. It is for a trader to make strategic decisions based on these raw data.

A typical dealer has a great amount of sales data are stored in their systems more difficult. The new technologies have the ability to use these historical data to improve retail productivity. Improve to create a sustainable advantage over competitors, the retailers are trying their product offerings, service levels and pricing models. To prevent, protect and value fluctuation margins, retailers are trying to reduce their cost-to-serve per customer and therefore ensures that the total cost of ownership of a customer will be reduced over time. Managing advertising plans is another critical area of ​​focus for retailers to target customers and more effectively and efficiently.

Small and medium-sized retailers are facing problems with limited analytical resources, to check the pulse of their business processes. The retailers are not able to take appropriate follow-up to day to day sales analysis, category analysis and brand share analysis for all products. We collect

Most retailers any transaction by anyone who track every movement of goods and record every customer service interaction. Therefore, there is no shortage of data, but how can you translate all this data into actionable information? How can this information be used to make better decisions? The main goal of a retail business IT department is to convert the raw data into valuable and useful information.

Business Analytics helps findings from the structured data such as turnover and productivity receive reporting, forecasting, inventory management, market basket analysis, product affinity, customer clustering, customer segmentation, to identify determining trend seasonality and clever hidden patterns for loss prevention and inventory management.

Analytical techniques such as statistical analysis, data analysis and analytical tools to help in the understanding of trends and patterns in large databases. If we can use to create analytical models, they provide the margin of decision making. While descriptive analysis helps to identify problems and investigate causes, predictive analytics for increased accuracy and efficiency of decision making. Some analysis for retailers are:

1 Sales Reporting and Analysis
2 Predictive Analysis
3 Inventory Management
4 Promotion effectiveness analysis
5 Demand Forecasting
6 Brand and Category Analysis

Predictive Analytics helps retail organization, their decision-making powers by looking at the future with increasing analytical stiffness. Predictive analytics is the key to exploiting these opportunities, enabling retailers to develop their capacity to increase their customer’s behavior and plan accordingly prognosis. Data analysis functions include a range of possible analysis, using statistical software such as SPSS, SAS, Excel and Minitab.

Data analysis helps in decision making with operational efficiency, cost savings by providing high quality solutions, facilitating flexible working arrangements and state of the art data security. A well-trained analytical team can help in automating the data cleaning, processing and recurring reporting.

Part II

In the constantly changing competitive environment, informed and intelligent decisions are the focus for each stage of the corporate form. To assist data analysis and statistical techniques to make decisions and provide valuable insights for an organization.

Data Analytics is the science of games with sales to make logical decisions slicing and dicing the data means in order to understand patterns and correlations, which the company a competitive advantage.

Retailers must be different strategies for merchandising, pricing, promotion, discount and markup in a position to analyze to make the right decision. Statistical and mathematical methods used to analyze current and historical data in order to make predictions about future events. The patterns in historical and transactional data is found used to identify opportunities and risks.

Data analysis provides an overview of top performers, bottom performers, key-value items, sales trends, forecasts, trends and seasonality. Inventory Management Analysis helps a trader with no minimum holding small quantities to keep in stock. Analytical team use the power of advanced statistical software, super computers and sophisticated mathematical actionable insights for the client to give. Advanced mathematical techniques, formulas and statistical methods are used to predict the future demand of a product. This analysis considers the impact of the holiday, seasonal and trend effects.

retail data analysis helps a retailer to improve its customers targeted by campaigns to reaction time to changes in the market, increase employee productivity and improve customer service in the stores. Take analytical models to examine a customer’s recency, frequency and monetary value of customer visits and purchase behavior and customer churn probabilities on retailers corrective action to strengthen loyalty. Some of the main analysis are: •

Customer profitability analysis • Market Basket Analysis

• opportunities for up-selling or cross selling
• Customer Satisfaction Survey
• RFM analysis

An analytical process can take care of data preparation, modeling, and generate reports. Analytics provides actionable insights and powerful decision. These decisions are necessary for developing and maintaining profitable customer relationships. Retail reports provide powerful insights and actionable analysis results on the desks of managers and retail analysts in real time.