The document discusses data mining's significance in marketing, highlighting its purpose, techniques, and tools that enhance understanding of customer behavior and marketing strategies. It emphasizes benefits such as predicting trends, improving ROI, and facilitating better decision-making, while also noting barriers like privacy concerns and implementation costs. A case study illustrates how data mining can predict purchase probabilities, leading to more focused marketing efforts and increased relevance for customers.

1. Use Of Data Mining
in Marketing…

2. Outline
• 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
• Case Study
• 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 Using 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 Basket 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.

14. Market Basket Analysis

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.

16. Data Mining Tools for
Marketing
• WEKA
• Rapid Miner
• R-Programming Tool
• Python Based Orange and NTLK
• KNIME

17. • 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.

18. • 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.

19. • Video RapidMiner

20. Case Study
• A company wants to launch an advertising campaign for a
product. Among its present customers the company wants to
post product information to those with a high probability of
purchasing the product. The company has data describing
the past customer behaviour and personal data about each of
its customers.
• There are also customers who have already bought the
product, e.g. in a trial period. The customers of the trial
period are divided into two classes: those who have bought
the product and those who have not. With this data a
prediction model is created to predict the probability of
purchasing the product.

21. Case Study
• After that the probability of purchasing the product is
predicted for all other customers. Only those with a higher
probability are addressed. As a side effect the company
learns with this data mining analysis which are the relevant
driver attributes of its customers buying a specific product.
• The example shows how DM can help in marketing to
predict the purchase probability of customers for a specific
product. This reduces cost, because sales activity can be
focused much better. The customers benefit at the same time
because the average relevance of the company’s offers
increases.

22. Conclusion
• In sum, data mining in marketing is very helpful
because business owners can able to summarize
and analyse to discover useful information.
• They have capability to increase the profits and
reduce the cost of products.
• They have the capability to analyse the frequency
of customers purchase.

23. Conclusion
• 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.