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  • 1. BDML Ecommerce
  • 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. 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. 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. 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. Value Potential of Big Data 6
  • 7. Pattern-Based Strategy Model 7
  • 8. Patterns for Competitive Advantage 8
  • 9. Examples: Zara (Retail Clothing) 9
  • 10. Major Appliance Retailer 10
  • 11. Enterprise Hadoop Solutions Rating Q1 2012 11
  • 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. 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. McKinsey Predicts the Magnitude of Big Data Potential Across Sectors 14
  • 15. How Big Data is going to change BI and Analytics – MIT Research 15
  • 16. Billion dollar idea 16
  • 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
  • 19. Mobile Marketing and Purchase 19
  • 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. Logistic Regression for Click Prediction 21
  • 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. Choosing Products for customer and Ordering Customer Details Click PredictionSale Items Model for Product Items Display Chosen Order 23
  • 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