Data Mining
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This document discusses data mining in marketing. It begins with defining data mining and explaining why it is important for predicting trends and analyzing customer behavior. The purpose of data mining in marketing is to better understand customers to target campaigns accurately. Key techniques discussed include market basket analysis, social media marketing, and knowledge-based marketing. Common data mining tools for marketing include RapidMiner, WEKA, and Python libraries. In conclusion, data mining can help businesses increase profits, reduce costs, and analyze customer purchase patterns.
Data Mining
- 2. Agenda • What is Data Mining? • Why is Data Mining important? • The Purpose of Data Mining • Data Mining in Marketing • Benefits using Data Mining in Marketing • Barriers using Data Mining in Marketing • Data Mining Techniques for Marketing • Data Mining Tools for Marketing • Conclusion
- 3. What is data mining – Methods for finding interesting structure in large databases • E.g. patterns, prediction rules, unusual cases – Focus on efficient, scalable algorithms • Contrasts with emphasis on correct inference in statistics – Related to data warehousing, machine learning
- 4. Why is data mining important? – Data Mining can be used in many different sectors of business to both predict and discover trends. In the past, we were only able to analyse what a company’s customers or clients HAD DONE, but now, with the help of Data Mining, we can predict what clientele WILL DO. – Can make data analysis more accessible to end users Results can be easier to interpret than e.g. regression models Strong focus on decisions and their implementation
- 5. The Purpose of Data Mining – Data Mining helps marketing professionals improve their understanding of customer behaviour. – In turn, this better understanding allows them to target marketing campaigns more accurately and to align campaigns more closely with the needs, wants and attitudes of customers and prospects. – Handles large databases directly
- 6. Data Mining in Marketing • Data mining technology allows to learn more about their customers and make smart marketing decisions. • The data mining business, grows 10 percent a year as the amount of data produced is booming. • DM Information can help to – increase return on investment (ROI), – improve CRM and market analysis, – reduce marketing campaign costs, – facilitate fraud detection and customer retention.
- 7. Data Mining in Marketing • The 4Ps is one way of the best way of defining the marketing: – Product (or Service) – Price – Place – Promotion
- 8. Benefits of Data Mining in Marketing • Predict future trends • customer purchase habits • Help with decision making • Improve company revenue and lower costs • Market basket analysis • Quick Fraud detection
- 9. Barriers using Data Mining in Marketing • User privacy/security • Amount of data is overwhelming • Great cost at implementation stage • Possible misuse of information • Possible in accuracy of data
- 10. Data Mining Techniques for Marketing • Knowledge-based Marketing • Market Basket Analysis • Social Media Marketing
- 11. Knowledge Based Marketing • It is marketing which makes use of the macro and micro-environmental knowledge that is available to the marketing functional unit in an organization. • There are three major areas of application of data mining for knowledge- based marketing are customers profiling, deviation analysis, and trend analysis.
- 12. Knowledge Based Marketing • The Customers profiling systems can analyse the frequency of purchases, companies can know how many times the customers can buy this product or visit the store. • The Deviation analysis gives the marketer a good capability to query changes that occurred as a result of recent price changes or promotions. • The Trend analysis can determine trends in sales, costs and profits by products or markets in order to achieve the highest amount of sales.
- 13. Market Based Analysis • Most common and useful types of data analysis for marketing and retailing. • Determine what products customers purchase together. • Improve the effectiveness of marketing and sales tactics using customer data already available to the company.
- 15. Social Media Marketing • SMM is a form of internet marketing that implements various social media networks in order to achieve marketing communication and branding goals. • SMM primarily covers activities involving social sharing of content, videos, and images for marketing purposes, as well as paid social media advertising.
- 17. Data Mining Tools for Marketing • WEKA • Rapid Miner • R-Programming Tool • Python Based Orange and NTLK • KNIME
- 18. • RapidMiner, formerly known as YALE (Yet Another Learning Environment), • RapidMiner is a software platform developed by the company of the same name that provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. • RapidMiner uses a client/server model with the server offered as Software as a Service or on cloud infrastructures.
- 19. • Used for business and commercial applications and supports all steps of the data mining process including data preparation, results visualization, validation and optimization. • RapidMiner is written in the Java programming language. • RapidMiner provides a GUI to design and execute analytical workflows.
- 20. Conclusion • In sum, data mining in marketing is very helpful because business owners can able to summarize and analyze to discover useful information. • They have capability to increase the profits and reduce the cost of products. • They have the capability to analyze the frequency of customers purchase. • Collecting data gives business firms a lot of good things, such as increased profits, keeping company in competition with other companies, and streamlining outreach between consumers and companies.