| Part I | | | | models and state of the art data security. A well |
| Analytics play a pivotal role in the data flow | | | | trained analytical team can help in the automation |
| scheme within a retail organization. A typical | | | | of data cleansing, processing and recurring |
| retailer generates more than thousands of data | | | | reporting. |
| points through POS machine. It is difficult for a | | | | Part II |
| retailer to make strategic decisions based on this | | | | In the constantly changing competitive business |
| raw data. | | | | environment, informed and intelligent decisions are |
| | | | the centre stage for every business organization. |
| Small and midsize retailers are facing problem with | | | | Data analytics and statistical techniques help to |
| limited analytical resources to read the pulse of | | | | make business decisions and provide valuable |
| their business processes. Retailers are not able to | | | | insights to an organization. |
| follow up with day to day sales analysis, category | | | | Data Analytics is the science of playing with sales |
| analysis and brand share analysis for all the | | | | numbers to arrive at logical decisions by slicing and |
| products. | | | | dicing the data to understand patterns and |
| Most retailers collect every transaction from | | | | correlations that could give the company a |
| every store, track every movement of goods | | | | competitive edge. |
| and record every customer service interaction. | | | | Retailers need to analyze various strategies |
| Hence there is no shortage of data, but how does | | | | surrounding merchandizing, pricing, promotion, |
| one translate all that data into actionable | | | | markup and markdown to be able to make the |
| information? How this information can be used to | | | | right decision. Statistical and mathematical |
| make better decisions? The main objective of a | | | | techniques are used to analyze current and |
| retail store IT department is to convert the raw | | | | historical data to make predictions about future |
| data into valuable and useful information. | | | | events. The patterns found in historical and |
| Business analytics helps to get insights from the | | | | transactional data is used to identify risks and |
| structured data, such as sales and productivity | | | | opportunities. |
| reporting, forecasting, inventory management, | | | | Data analytics gives a summary on top |
| market basket analysis, product affinity, customer | | | | performers, bottom performers, key value items, |
| clustering, customer segmentation, identifying | | | | sales performance, forecasting, trend and |
| trend, identifying seasonality and understanding | | | | seasonality. Inventory management analysis helps |
| hidden patterns for loss prevention and store | | | | a retailer to keep minimum inventory without |
| administration. | | | | running out of stock. Analytical team use the |
| Analytical techniques such as statistical analysis, | | | | power of advanced statistical software, super |
| data analysis and analytical tools help in | | | | computers and sophisticated mathematics to give |
| understanding patterns and trends within large | | | | actionable insights to the customer. Advanced |
| databases. When we use them for creating | | | | mathematical techniques, formulas and statistical |
| analytical models, they provide the edge to | | | | methods are used to predict the future demand |
| decision making. While descriptive analysis helps to | | | | of a product. This analysis considers the impact of |
| identify issues and examine causes, predictive | | | | holiday, seasonality and trend effect. |
| analytics enhances the accuracy and | | | | Retail data analysis helps a retailer to target their |
| effectiveness of decision making process. | | | | customers more effectively by campaigns, to |
| Some analyses applicable to retailers are: | | | | improve response time to market changes, to |
| 1. Reporting and Sales Analysis | | | | increase employee productivity and to improve |
| 2. Predictive Analysis | | | | customer service at stores. Analytic models |
| 3. Inventory Management | | | | examine a customer's recency, frequency and |
| 4. Promotion-Effectiveness Analysis | | | | monetary value of customer visits along with |
| 5. Demand Forecasting | | | | purchase behaviour and provide a customer's |
| 6. Brand and Category Analysis | | | | attrition probabilities on which retailers can take |
| Predictive analytics helps a retail organization to | | | | corrective action to reinforce loyalty. Some of the |
| enhance its decision making powers by looking at | | | | key analyses are:o Customer profitability analysiso |
| the future with analytical rigidity. Predictive | | | | Market basket analysiso Opportunities for up |
| analytics holds the key to taking advantage of | | | | selling or cross-sellingo Customer satisfaction |
| these opportunities such that retailers can | | | | analysiso RFM Analysis |
| increase their ability to forecast their customer's | | | | An analytical process can take care of data |
| behaviour and plan accordingly. Data analytics | | | | preparation, modelling activities and generates |
| capabilities cover a number of possible analyses, | | | | reports. Analytics provide actionable and powerful |
| using statistical software such as SPSS, SAS, | | | | decision insights. These decisions are required for |
| Excel and Minitab. | | | | developing and maintaining profitable customer |
| Data analysis helps in decision making process with | | | | relationships. Retail reports give powerful insights |
| operational efficiency, saves costs by providing | | | | and actionable analytics to the desks of retail |
| high quality solutions, facilitates flexible working | | | | managers and analysts in real time. |