Summary of eight application points of data analysis in the retail industry

1. Sales indicator analysis: mainly analyzes various sales indicators, such as gross profit, gross profit margin, Ping effect, cross ratio, sales promotion ratio, profitability, turnover rate, year-on-year comparison, and period-over-period comparison; the analysis dimension can be viewed from the perspective of management architecture, category brand, date, and time period. These analysis dimensions adopt multi-level drilling to get a thorough analysis idea.

1. Sales indicator analysis: mainly analyzes various sales indicators, such as gross profit, gross profit margin, Ping effect, cross ratio, sales promotion ratio, profitability, turnover rate, year-on-year comparison, and period-over-period comparison; the analysis dimension can be viewed from the perspective of management architecture, category brand, date, and time period. These analysis dimensions adopt multi-level drilling to get a thorough analysis idea.

1. Sales indicator analysis:

Mainly analyzes various sales indicators, such as gross profit, gross profit margin, efficiency, cross-ratio, sales-to-market ratio, profitability, turnover rate, year-on-year comparison, and period-over-period comparison; the analysis dimension can be viewed from the perspective of management architecture, category brand, date, time period, etc. These analysis dimensions adopt multi-level drilling to get a thorough analysis idea; at the same time, analysis data such as prediction information and alarm information can be generated based on massive data volumes. New pivot tables can also be generated based on various sales indicators, for example, the most common ABC classification table, product sensitive classification table, and product profit classification table.

These complex indicators are difficult to implement in the original database. Although we know that they are very useful, they are not available, so that the status of these indicators is unknown. It was not until the advent of BI technology that these indicators were again favored by managers and analysts.

2. Sales Forecasting

Based on the analysis of past sales data and factors affecting sales, the sales forecasting model is constructed through modeling. This model can be used for time forecasting and analysis based on sales data, or simply mobile average prediction. Under the premise of Data perfection, more influencing factors can also be eliminated, such as the company's sales policy, competitor's sales strategy, and the company's product release, whether the sales staff is sufficient or not to construct a complex Neural Network System for better prediction and analysis.

3. Financial Analysis:

1) analyze the company's financial status, understand the company's assets liquidity, cash flow, debt level, and the company's ability to repay long and short-term debts, so as to evaluate the company's financial status and risks;

2) analyze the enterprise's asset management level, understand the enterprise's Asset Management Status and capital turnover;

3) analyze the profitability of an enterprise;

4) analyze the development trend of the enterprise and predict the business prospects of the enterprise;

At the same time, the system should also comprehensively analyze various financial indicators by department, personnel, commodities, suppliers, time and other dimensions, such: cost, gross profit, profit, inventory, settlement, break-even point, sales volume, sales amount, market share, etc.

4. Product Structure Analysis:

The main data of commodity analysis comes from the sales data and basic commodity data, which generates analysis ideas with the analysis structure as the main line. The main analysis data includes the product category structure, brand structure, price structure, gross profit structure, settlement method structure, and origin structure, in this way, multiple indicators such as commodity breadth, commodity depth, commodity elimination rate, Commodity Import Rate, commodity replacement rate, key commodities, best-selling commodities, unsalable commodities, and seasonal commodities are generated. Through the analysis of these indicators, we can guide the adjustment of the enterprise's commodity structure, and strengthen the competitiveness and reasonable configuration of the commodities.

5. Customer Segmentation:

Customer analysis mainly refers to the analysis of purchasing behaviors of customer groups. For example, if the customer is simply divided into rich and poor, then who is the rich and who is the poor? Enterprises that adopt membership card system can distinguish by the monthly income of Member registration. If membership card is not implemented, it can be assumed by the small ticket amount. For example, if we think we are rich, if we are less than 100 yuan, we think we are poor. Now, the boss needs to know a lot of things, for example, what kinds of products the rich and the poor like, and when the rich and the poor enjoy their shopping time; in your own business area, there are many rich people or many poor people. The rich make great contributions to the mall, the poor make great contributions, and the rich and the poor prefer to pay for it. In addition, there are also analysis of the business area's customer volume, shopping peak hours and holiday economy impact on enterprises.

6. Supplier analysis:

Through various indicators of the supplier in a specific period of time, including order quantity, order amount, import amount, purchase amount, arrival time, inventory, inventory amount, return amount, return amount, sales volume, sales volume, gross margin of the supplied goods, turnover rate, cross ratio, etc., provide a basis for supplier import, reserve, elimination (or elimination of some of its varieties) and handling of supplier inventory commodities. The main analysis themes include supplier composition, delivery, settlement, and supply of goods, such as sales contribution and profit contribution. Through analysis, we may find that some suppliers have always sold well, and their settlement is also very stable in a certain period of time, and the settlement method of this supplier is consignment. Well, the analysis shows that the sales risk of the commodities provided by this supplier is small. if the funds are not tight, why don't you consider changing them to purchase or sale? This can reduce costs.

7. Personnel analysis:

Analyze the company's personnel indicators, especially the sales personnel indicators (mainly sales indicators, supplemented by gross profit indicators) and buyer indicators (sales, gross profit, supplier replacement, number of purchased and sold goods, number of consignment goods, capital occupation, capital turnover, etc.) to assess employee performance and improve employee enthusiasm, it provides a scientific basis for the rational use of human resources. Main analysis topics include, staff composition, per-capita sales of sales personnel, individual sales performance of billing sales, per-capita sales of various management architectures, gross profit contribution, number of items purchased by the procurement personnel in charge, purchase and sales consignment proportion, sales volume of introduced products, etc.

8. Customer maintenance and retention

Through Previous customer purchase behaviors and records, combined with the customer's basic information and other information, the customer can predict the loss of users by building a data mining model to identify high-loss users, based on the value contribution of the customer, different retention strategies are adopted to stabilize revenue and reduce customer loss.