Data St

Part I

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

A typical retailer has large amount of sales data stored in their systems. The new technologies have the ability to use these historical data to improve retail productivity. To create sustainable advantage over competition, retailers are trying to enhance their product offerings, service levels and pricing models. To prevent value attrition and to protect margins, retailers are trying to reduce their cost-to-serve per customer and thereby making sure that the total cost of ownership of a customer over time is reduced. Managing promotional plans is another critical area for retailers to focus on and target customers more effectively and efficiently.

Small and midsize retailers are facing problem with limited analytical resources to read the pulse of their business processes. Retailers are not able to follow up with day to day sales analysis, category analysis and brand share analysis for all the products.

Most retailers collect every transaction from every store, track every movement of goods and record every customer service interaction. Hence there is no shortage of data, but how does one translate all that data into actionable information? How this information can be used to make better decisions? The main objective of a retail store IT department is to convert the raw data into valuable and useful information.

Business analytics helps to get 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 such as statistical analysis, data analysis and analytical tools help in understanding patterns and trends within large databases. When we use them for creating analytical models, they provide the edge to decision making. While descriptive analysis helps to identify issues and examine causes, predictive analytics enhances the accuracy and effectiveness of decision making process.

Some analyses applicable to retailers are:

1. Reporting and Sales Analysis
2. Predictive Analysis
3. Inventory Management
4. Promotion-Effectiveness Analysis
5. Demand Forecasting
6. Brand and Category Analysis

Predictive analytics helps a retail organization to enhance its decision making powers by looking at the future with analytical rigidity. Predictive analytics holds the key to taking advantage of these opportunities such that retailers can increase their ability to forecast their customer’s behaviour and plan accordingly. Data analytics capabilities cover a number of possible analyses, using statistical software such as SPSS, SAS, Excel and Minitab.

Data analysis helps in decision making process with operational efficiency, saves costs by providing high quality solutions, facilitates flexible working models and state of the art data security. A well trained analytical team can help in the automation of data cleansing, processing and recurring reporting.

Part II

In the constantly changing competitive business environment, informed and intelligent decisions are the centre stage for every business organization. Data analytics and statistical techniques help to make business decisions and provide valuable insights to an organization.

Data Analytics is the science of playing with sales numbers to arrive at logical decisions by slicing and dicing the data to understand patterns and correlations that could give the company a competitive edge.

Retailers need to analyze various strategies surrounding merchandizing, pricing, promotion, markup and markdown to be able to make the right decision. Statistical and mathematical techniques are used to analyze current and historical data to make predictions about future events. The patterns found in historical and transactional data is used to identify risks and opportunities.

Data analytics gives a summary on top performers, bottom performers, key value items, sales performance, forecasting, trend and seasonality. Inventory management analysis helps a retailer to keep minimum inventory without running out of stock. Analytical team use the power of advanced statistical software, super computers and sophisticated mathematics to give actionable insights to the customer. Advanced mathematical techniques, formulas and statistical methods are used to predict the future demand of a product. This analysis considers the impact of holiday, seasonality and trend effect.

Retail data analysis helps a retailer to target their customers more effectively by campaigns, to improve response time to market changes, to increase employee productivity and to improve customer service at stores. Analytic models examine a customer’s recency, frequency and monetary value of customer visits along with purchase behaviour and provide a customer’s attrition probabilities on which retailers can take corrective action to reinforce loyalty. Some of the key analyses are:

o Customer profitability analysis
o Market basket analysis
o Opportunities for up selling or cross-selling
o Customer satisfaction analysis
o RFM Analysis

An analytical process can take care of data preparation, modelling activities and generates reports. Analytics provide actionable and powerful decision insights. These decisions are required for developing and maintaining profitable customer relationships. Retail reports give powerful insights and actionable analytics to the desks of retail managers and analysts in real time.