Unlocking the Power of Data Marketing: Three Data Mining Cases
In the world of data marketing, the ability to extract valuable insights from large datasets is crucial for making informed business decisions. At our community, we have seen a surge in interest from professionals from Taobao, Tencent, Huawei, and other businesses who are eager to learn about data acquisition, analysis, and mining. Our small, dense circle of data marketing enthusiasts has grown to 170 members, with many sharing their expertise and experiences through tutorials and case studies.
Classic Marketing Materials and Channels
Our community has created a wealth of resources for our members, including:
- 100 Classic Cases of Internet Marketing Success: This comprehensive guide showcases successful marketing strategies and tactics that have been proven to work in the digital landscape.
- Precise One-Minute Tutorial: Learn how to quickly and efficiently find information on the web using search engines and other online tools.
- Taobao Lynx Half-Price Method: Discover how to save money shopping on Taobao using this effective strategy.
- Headlines Today: Public Micro-Channel Number from the Media and Liquidity Drainage Courses: Stay up-to-date with the latest marketing trends and strategies in this engaging course.
- 2000 Microblogging, Forums, QQ, and Other Essential Software Marketing Channels: Learn how to effectively use these channels to reach your target audience.
- 100 Copies of the Necessary Marketing and Sales Assessment, Analysis, Summary Tables: Get the tools you need to assess and analyze your marketing and sales efforts.
- Unused Code, The 10-Minute Twitter, Taobao, Almost Known, Watercress, and Other Data Collection Platform Video Tutorial: Learn how to quickly and efficiently collect data using these platforms.
- The More Money the More Lucrative Marketing Programs, Product Profits Method of Combat Operations: Discover how to create and execute effective marketing programs that drive profits.
- After My Screening Python from Entry, Data Analysis, Machine Learning Materials and Courses: Take your data analysis skills to the next level with these comprehensive courses.
- Super Super Detailed Copy Books, Copywriting and Copywriting Accumulated Video: Learn the art of effective copywriting and how to create compelling copy.
- 120 More Income than 40% of Business Projects: Discover how to create and execute business projects that drive significant revenue.
- 70 Class Stream Advertising System Optimization and Upgrading Combat Full Set of Video Tutorials: Learn how to optimize and upgrade your advertising systems for maximum effectiveness.
Sharing Studied Cases of Data Mining
In addition to sharing our resources and expertise, we also share studied cases of data mining to help our members understand the operational data mining processes and methods. Our main contents include:
First Case: Bank Fraud and Credit Card Delinquencies Analysis
- Customer Credit Rating Factors: Identify the key factors that influence customer credit ratings.
1.1 Customer Credit Card Application Data Preprocessing: Preprocess customer credit card application data to prepare it for analysis.
1.2 Credit Card Application Success Factors: Determine the factors that contribute to successful credit card applications. - A Credit Card Customer Credit Rating Factors: Identify the key factors that influence credit card customer credit ratings.
- Based on the Factors Affecting Consumer Credit Rating: Analyze the factors that affect consumer credit ratings.
- Credit Card Fraud Judgment Model: Develop a credit card fraud judgment model using Apriori, discrimination, and classification algorithms.
4.1 Fraud Model Based on Apriori Algorithm: Create a fraud model using the Apriori algorithm.
4.2 Based Discrimination Fraud Model: Develop a discrimination-based fraud model.
4.3 Fraud Model Based Classification Algorithm: Create a fraud model using a classification algorithm. - Fraud Population Attribute Analysis: Analyze the demographic attributes of fraud populations.
5.1 Statistical Analysis of Demographic Attributes Fraud: Perform statistical analysis of demographic attributes of fraud populations.
5.2 Analysis of Demographic Attributes Based on Logistic Regression of Fraud: Analyze demographic attributes using logistic regression.
5.3 Late Payment Customers Features: Identify the features of late payment customers.
5.4 Overdue Customer Characteristics Based on the Decision Tree Analysis: Analyze the characteristics of overdue customers using decision trees.
5.5 Based on Regression Analysis of Overdue Customer Characteristics: Analyze overdue customer characteristics using regression analysis.
5.6 Based on Historical Analysis of Customer Consumption Characteristics: Analyze customer consumption characteristics using historical data.
5.7 Based on Clustering Analysis of Customer Characteristics: Analyze customer characteristics using clustering analysis.
5.8 Analysis of Customer Segmentation Based on Clustering: Segment customers based on clustering analysis.
Second Case: Competition Analysis Business Hotel
- The Current Economy Hotel Industry Competition: Analyze the current economy hotel industry competition.
- Based on Business Objectives and Analyze Data to Find Ready: Identify business objectives and analyze data to find ready-made solutions.
- Data Capture through Programming Python: Capture data using programming Python.
- The Initial Data Pre-processing: Preprocess initial data to prepare it for analysis.
- Customer Data Analysis Business Hotels: Analyze customer data for business hotels.
5.1 Factors Hotel Reviews: Identify factors that influence hotel reviews.
5.2 Hotel Reviews the Relationship between Hotel Performance: Analyze the relationship between hotel reviews and performance.
5.3 Hotel Score Analysis: Analyze hotel scores.
5.4 Customer Sentiment Analysis: Analyze customer sentiment.
5.5 Hotel Competition Points: Identify hotel competition points. - According to the Recommendations Given in the Corresponding Analysis: Make recommendations based on the analysis.
Third Case: Operational Analysis of Three Hot Pot, Sea Fishing
- Pot-Related Business Analysis, Analysis of Indicators: Analyze pot-related business indicators.
- Data Fetch Pot: Capture data from pot-related sources.
- Data Preprocessing: Preprocess data to prepare it for analysis.
- Sea Fishing Operations State Analysis: Analyze the state of sea fishing operations.
- Shop Location Analysis: Analyze the location of shops.
- Dishes Associated Sales Analysis: Analyze the sales of dishes.
- User Comments and Ratings Associated Analysis: Analyze user comments and ratings.
- Emotional Post-Consumer Customer Analysis: Analyze the emotional post-consumer customer.
- Analysis of Operational Recommendations Sea Fishing: Make recommendations for sea fishing operations.
By following these cases, our members can gain a deeper understanding of data mining processes and methods, and enhance their competitiveness in the market.