Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Business Intelligence
    Data Mining
      (Part 2 of 2)
The End?
How far can I go?

• Storing and analyzing historical data you can see just
  one part of reality (the past and the presen...
Data Mining

• Is a process to extract patterns from data
• “We’re drowning in data but information
  thirsty”
• Data Mini...
Business Problems
• Recommendations
• Anomaly Detection
• Customer abandon analysis
• Risk Management
• Customer segmentat...
Data Mining Tasks

• Classification
• Estimation / Regression
• Prediction / Projection (Forecasting)
• Association Rules /...
Predictive Models
• Classifications
 • Discrete value prediction
   • Yes, No
   • High, Medium, Low
• Estimation / Regress...
Descriptive Models
• Association Rules / Affinity
 • Looks for correlation indexes among
    diverse associated elements
 •...
Work Cycle

                           Transform
                            Data to
                          Information...
Data Mining and DWh

• The Data Warehsouse unifies diverse data sources
  in one common repository
• Before the DM process,...
Project Cycle
• Business Problem Formulation
• Data Gathering
• Data transformation and cleansing
• Model Construction
• M...
What is a Model?
• The model is a set of conclusions reached (in
  mathematical format) after data processing
• Is used to...
Cases
Outlook    Temperature (C)    Humidity   Wind   Play Golf?
 Sunny          29.4            85%      NO        No
 Su...
Model
                  Outlook

Overcast        Rainy                    Sunny

  YES              Wind              Humi...
Data Mining Algorithms
 • Naive Bayes
 • Decission Trees
 • Autoregression trees (ARTxp and ARIMA)
 • K-Means
 • Kohonen M...
Where can I use them?
• Marketing: Segmentation, Campaigns, Results,
  Loyalty,...
• Sales: Behaviour detection, Sales hab...
Data Mining and CRM (1)

 • Detect the best prospect / customers
 • Select the best communication channel for
   prospects...
Data Mining and CRM (2)

 • Improve direct marketing campaign results
 • Customer base segmentation
 • Reduce credit risk ...
Clustering
• “Self” Customer Segmentation
 • Descriptive Characteristics
 • Behavioural Characteristics
   • Relationship
...
Classification
• Customers by purchase behaviour
• Customers by payment behaviour
• Customers by resources devoted/needed
 ...
Association Rules

• Market Basket Analysis
• Cross Selling
• Up Selling
Prediction / Forecasting

• Revenue Projection
• Payment Projection
• Number of Products sold Projection
• Cash Flow Proje...
Some other DM cases


• Key Influencers
• Predictions Calculator
Some Possible
             Problems (1)
• To learn things that are not true
 • The patterns may not represent any underlyi...
Possible Problems... (1I)

• To learn things that are true, but not
  useful
 • Learn things that we already knew
 • Learn...
Thank you!
Upcoming SlideShare
Loading in …5
×

Business Intelligence Presentation - Data Mining (2/2)

2,727 views

Published on

In this second part of the Business Intelligence Presentation, we dive into Data Mining, what it is, its business applications and some CRM related examples.

  • DOWNLOAD THE BOOK INTO AVAILABLE FORMAT (New Update) ......................................................................................................................... ......................................................................................................................... Download Full PDF EBOOK here { https://urlzs.com/UABbn } ......................................................................................................................... Download Full EPUB Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... Download Full doc Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... Download PDF EBOOK here { https://urlzs.com/UABbn } ......................................................................................................................... Download EPUB Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... Download doc Ebook here { https://urlzs.com/UABbn } ......................................................................................................................... ......................................................................................................................... ................................................................................................................................... eBook is an electronic version of a traditional print book THE can be read by using a personal computer or by using an eBook reader. (An eBook reader can be a software application for use on a computer such as Microsoft's free Reader application, or a book-sized computer THE is used solely as a reading device such as Nuvomedia's Rocket eBook.) Users can purchase an eBook on diskette or CD, but the most popular method of getting an eBook is to purchase a downloadable file of the eBook (or other reading material) from a Web site (such as Barnes and Noble) to be read from the user's computer or reading device. Generally, an eBook can be downloaded in five minutes or less ......................................................................................................................... .............. Browse by Genre Available eBOOK .............................................................................................................................. Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, CookBOOK, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult, Crime, EBOOK, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, ......................................................................................................................... ......................................................................................................................... .....BEST SELLER FOR EBOOK RECOMMEND............................................................. ......................................................................................................................... Blowout: Corrupted Democracy, Rogue State Russia, and the Richest, Most Destructive Industry on Earth,-- The Ride of a Lifetime: Lessons Learned from 15 Years as CEO of the Walt Disney Company,-- Call Sign Chaos: Learning to Lead,-- StrengthsFinder 2.0,-- Stillness Is the Key,-- She Said: Breaking the Sexual Harassment Story THE Helped Ignite a Movement,-- Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones,-- Everything Is Figureoutable,-- What It Takes: Lessons in the Pursuit of Excellence,-- Rich Dad Poor Dad: What the Rich Teach Their Kids About Money THE the Poor and Middle Class Do Not!,-- The Total Money Makeover: Classic Edition: A Proven Plan for Financial Fitness,-- Shut Up and Listen!: Hard Business Truths THE Will Help You Succeed, ......................................................................................................................... .........................................................................................................................
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Business Intelligence Presentation - Data Mining (2/2)

  1. 1. Business Intelligence Data Mining (Part 2 of 2)
  2. 2. The End?
  3. 3. How far can I go? • Storing and analyzing historical data you can see just one part of reality (the past and the present) • Is there a way to answer questions not yet made? Can I look into the future? • Can I predict how my business is going to work? What about the market? And my customers?
  4. 4. Data Mining • Is a process to extract patterns from data • “We’re drowning in data but information thirsty” • Data Mining borrows techniques from statistics, probability, maths, artificial intelligence and other fields
  5. 5. Business Problems • Recommendations • Anomaly Detection • Customer abandon analysis • Risk Management • Customer segmentation • Targeted advertising • Projections
  6. 6. Data Mining Tasks • Classification • Estimation / Regression • Prediction / Projection (Forecasting) • Association Rules / Affinity Groups • Clusterization
  7. 7. Predictive Models • Classifications • Discrete value prediction • Yes, No • High, Medium, Low • Estimation / Regression • Continuous value prediction • Amounts • Numbers • Projection / Forecasting
  8. 8. Descriptive Models • Association Rules / Affinity • Looks for correlation indexes among diverse associated elements • Market Basket Analysis • Clusterization • Groups items according to similarity • “Automatic” classification
  9. 9. Work Cycle Transform Data to Information Identify Business Opportunities Act with Information Measure Results
  10. 10. Data Mining and DWh • The Data Warehsouse unifies diverse data sources in one common repository • Before the DM process, you must have reliable data sources • Data must be presented in a way that eases analysis
  11. 11. Project Cycle • Business Problem Formulation • Data Gathering • Data transformation and cleansing • Model Construction • Model Evaluation • Reports and Prediction • Application Integration • Model Management
  12. 12. What is a Model? • The model is a set of conclusions reached (in mathematical format) after data processing • Is used to extract knowledge and to compare it to new data to reach to new conclusions • It has some efficency percentage • Must be adjusted to make helpful predictions • It is time-constrainted
  13. 13. Cases Outlook Temperature (C) Humidity Wind Play Golf? Sunny 29.4 85% NO No Sunny 26.6 90% YES No Overcast 28.3 78% NO Yes Rainy 21.1 96% NO Yes Rainy 20.0 80% NO Yes Rainy 18.3 70% YES No Overcast 17.7 65% YES Yes Sunny 22.2 95% NO No Sunny 20.5 70% NO Yes Rainy 23.8 80% NO Yes Sunny 23.8 70% YES Yes Overcast 22.2 90% YES Yes Overcast 27.2 75% NO Yes Rainy 21.6 80% YES No
  14. 14. Model Outlook Overcast Rainy Sunny YES Wind Humidity NO YES <=77.5 >77.5 YES NO YES NO
  15. 15. Data Mining Algorithms • Naive Bayes • Decission Trees • Autoregression trees (ARTxp and ARIMA) • K-Means • Kohonen Maps • Neural Networks • Logistic regression • Time Series
  16. 16. Where can I use them? • Marketing: Segmentation, Campaigns, Results, Loyalty,... • Sales: Behaviour detection, Sales habits • Finances: Investments, Portfolio Management • Banks and Assurance: Credit Check • Security: Fraud Detection • Medicine: Possible treatment analysis • Manufacturing: Quality Control • Internet: Click analysis, Text Mining
  17. 17. Data Mining and CRM (1) • Detect the best prospect / customers • Select the best communication channel for prospects / customers • Select an appropriate message to prospects / customers • Cross-selling, Up-selling and sales recommendation engines
  18. 18. Data Mining and CRM (2) • Improve direct marketing campaign results • Customer base segmentation • Reduce credit risk exposure • Customer Lifetime Value • Customer retention and loss
  19. 19. Clustering • “Self” Customer Segmentation • Descriptive Characteristics • Behavioural Characteristics • Relationship • Purchases • Payments
  20. 20. Classification • Customers by purchase behaviour • Customers by payment behaviour • Customers by resources devoted/needed to their service • Customers by credit profile • Customers by attention required
  21. 21. Association Rules • Market Basket Analysis • Cross Selling • Up Selling
  22. 22. Prediction / Forecasting • Revenue Projection • Payment Projection • Number of Products sold Projection • Cash Flow Projection
  23. 23. Some other DM cases • Key Influencers • Predictions Calculator
  24. 24. Some Possible Problems (1) • To learn things that are not true • The patterns may not represent any underlying rule • The model may not represent a relevant number of examples • Data may be in a detail level not enough for analysis
  25. 25. Possible Problems... (1I) • To learn things that are true, but not useful • Learn things that we already knew • Learn things that cannot be applied
  26. 26. Thank you!

×