Digital management insight into retail and e-commerce operations -- Introduction to digital management

Data management

Concept

Use analytical tools to objectively 、 Scientific analysis of real data , And apply the analysis results to generate 、 Operation 、 A management method used in various links such as sales

significance

Quantitative management

• KPI

Maximum sales performance 、 Maximize productivity

• Apply the correct analysis results to the business level in the most practical way

Effectively save various costs and expenses of the enterprise

Organizational management 、 Tools for department coordination

Improve the speed and correctness of enterprise managers' decisions

level

Management level

• Business guidance management

  • Through data collection 、 Data monitoring 、 Perspective business by means of data tracking , Through data analysis 、 Data mining and other ways to build business management model
  • Target and forecast management 、 Profit and expense management

• Operation analysis management

  • in personam 、 cargo 、 site 、 Financial analysis management
  • Performance appraisal management 、 Inventory analysis management 、 Supply chain analysis management 、 Passenger flow analysis management 、 Fund analysis management 、 Customer relationship management （CRM）

• Business strategy management

  • Through the corresponding data analysis of each business link to achieve the purpose of formulating or modifying strategies
  • Analysis of consumer buying behavior 、 Member customer strategy 、 Commodity pricing strategy 、 Brand positioning strategy 、 Competitor strategy management 、 Resource allocation strategy

• Strategic planning management

  • Through internal and external data of the enterprise , The process of formulating the long-term plan of the enterprise
  • Macroeconomic analysis 、 Industry environment analysis 、 Business environment analysis 、 Internal resource analysis

Business logic

• Data management in sales
• Data management in commodities
• Data management in finance
• Data management in personnel
• Data management in production
• Data management in logistics

technological process

Analyze requirements

• Collect requirements 、 Analyze requirements 、 Clear requirements

collecting data

• Data collection methods include company databases 、 Public publications 、 market research 、 Internet 、 Purchase professional company data, etc
• Basic links of data analysis , Make sure the data source is reliable

Collating data

• Preprocess the collected data , The process of making it a standard format for further analysis

• Data in non-standard format

  • Date in text format , Numbers in text format , Extra space symbols in the field

• Data that does not conform to business logic

  • False purchase records

Analyze the data

• On the basis of business logic , The process of processing data by using the simplest and effective analysis methods and the most reasonable analysis tools

Data visualization

Application template development

Analysis report

• Have a point , Focus on key businesses and the focus of the audience
• argument 、 argument 、 demonstration
• Logicality
• Readability

application

• The problems found in the process of data analysis 、 Opportunities are broken down into business units , And through data monitoring 、 Early warning of key indicators 、 Make a reasonable judgment on the trend and other means to know the business improvement of each department

Application of the template

form

• Custom area
• Data source area
• Auxiliary analysis area
• Business analysis area
• Report display area

effect

Monitoring function

• Monitor all levels of business through data and corresponding analysis indicators

Early warning function

• Predict sales in advance 、 passenger flow 、 Traffic volume 、 Profit and loss data

Supporting role

• For new products 、 New strategy 、 The formulation of new policies provides data support

The core

• The core of enterprise management

Data preprocessing

Data cleaning

• Missing value processing

  • Delete

  • fill

    • Quantitative filling
    • Statistics fill
    • Interpolation fills
    • Model filling

Data integration

• Combine data from multiple data sources 、 Stored in a consistent data store

Data protocol

• Remove attributes irrelevant to business