Bdml ecom


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Bdml ecom

  1. 1. BDML Ecommerce
  2. 2. What is Big Data?• “Big data," is a group of data technologies that are making the storage, manipulation and analysis of large volumes of data cheaper and faster than ever.• Types of “Big data” – Transactional Data – Data from mobile app • Location data , Profiles – Data from Social media • Blogs, Facebook, Twitter and other social media apps 2
  3. 3. Big Data Challenge• Managing the three “V”s of big data – Volume – Velocity • The speed at which data is coming and changing – Variety • Text, Audio, Video• Big Data is mainly unstructured data• Technology to store big data• Technology to analyze big data 3
  4. 4. The Business Needs• Traditionally business wanted answers to Five Questions• Traditional BI answers two of those questions – What Happened? – Reports and Ad-hoc Queries – Why it Happened? – Analytics, Cubes• Dash Boards and Score Cards Answer the third – What is happening Now?• Data Mining and Predictive Analytics Answer the last two – What is going to Happen in Future? – Data Mining – What can I do to stop it or make it better in future? – Predictive Analytics 4
  5. 5. Big Data Opportunity• The relational databases has limitations – Data needs to be modeled – Need to know the business needs to create good data models – Data needs to be structured to support queries• Can we do analytics on big data and answer all Five business questions? 5
  6. 6. Value Potential of Big Data 6
  7. 7. Pattern-Based Strategy Model 7
  8. 8. Patterns for Competitive Advantage 8
  9. 9. Examples: Zara (Retail Clothing) 9
  10. 10. Major Appliance Retailer 10
  11. 11. Enterprise Hadoop Solutions Rating Q1 2012 11
  12. 12. Big Data Opportunities• McKinsey projects that in the U.S. alone, there will be a need by 2018 for 140,000 to 190,000 “data scientists”• Steep technical learning curves and a lack of qualified technical staff create barriers to adoption 12
  13. 13. Big Data Opportunities• Need for another 1.5 million data-literate managers – Formal training in predictive analytics and statistics.• The technologies in the big data area are not Analyst Friendly – Need Programmers with knowledge of Hadoop, Statistics and analytics • Companies Retraining programmers and database analysts to get them up to speed on advanced analytics. • Getting started with Hadoop doesnt require a large investment as the software is open source, and is available instantly through the Amazon Web Services cloud (Elastic MapReduce service) 13
  14. 14. McKinsey Predicts the Magnitude of Big Data Potential Across Sectors 14
  15. 15. How Big Data is going to change BI and Analytics – MIT Research 15
  16. 16. Billion dollar idea 16
  17. 17. DMA Campaign Response Rates 2010• Email to a house list averaged a 19.47% open rate, a 6.64% click-through rate, and a 1.73% conversion rate, with a bounce-back rate of 3.72% and an unsubscribe rate of 0.77%.• Direct mail: Letter-sized envelopes had a response rate this year of 3.42% for a house list and 1.38% for a prospect list.• Catalogs had the lowest cost per order of $47.61, just ahead of inserts at $47.69, email at $53.85, and postcards $75.32.• Outbound telemarketing to prospects had the highest cost per order of $309.25, but it also had the highest response rate from prospects of 6.16%.• Paid search had an average cost per click of $3.79, with a 3.81% conversion rate. The conversion rate (after click) of Internet display advertisements was slightly higher at 4.43%. 17
  18. 18. 18
  19. 19. Mobile Marketing and Purchase 19
  20. 20. Improving Offer Acceptance Rate: Algorithms to Personalize Offers• K-Means Clustering for clustering Users – Cluster users based on brand preferences and demographics – Most popular Clustering Algorithm• Logistic regression for finding the probability of accepting an offer• SVD (Single Value Decomposition) to reduce dimensionality of data and to reduce noise – Reducing the dimensions to a few improves performance and reduce accuracy – The noise reduction which happens when the dimensions are reduce helps to improve the accuracy of prediction 20
  21. 21. Logistic Regression for Click Prediction 21
  22. 22. How Does The Model Work?– Classification Algorithms learns from Examples in a process known as Training– Need Training Data and Decide on Training Algorithm • Choose between Logistic Regression and Google’s combined regression and ranking– Need to specify the input values (Predictors) and output values (Target) in the training data • Predicting Clicks probability is the Target variable • User and Item features are the input variables 22
  23. 23. Choosing Products for customer and Ordering Customer Details Click PredictionSale Items Model for Product Items Display Chosen Order 23
  24. 24. Conclusion• On the basis of our on-line surveys, face-to- face survey and analysis of studies done by others we conclude that the opportunity for a Marketing application based on Big data and Machine Learning is great. In a scale of 1-10 we rate this opportunity at 9 24