Post on 15-Jun-2015
BDML Ecommerce
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
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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
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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
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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?
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Value Potential of Big Data
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Pattern-Based Strategy Model
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Patterns for Competitive Advantage
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Examples: Zara (Retail Clothing)
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Major Appliance Retailer
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Enterprise Hadoop Solutions Rating Q1 2012
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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
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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 doesn't require a large investment as the software is open source, and is available instantly through the Amazon Web Services cloud (Elastic MapReduce service)
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McKinsey Predicts the Magnitude of Big Data Potential Across
Sectors
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How Big Data is going to change BI and Analytics – MIT Research
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Billion dollar idea
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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%.
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Mobile Marketing and Purchase
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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
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Logistic Regression for Click Prediction
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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
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Choosing Products for customer and Ordering
Sale Items
Click PredictionModel for Product
ItemsChosen
Display Order
Customer Details
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
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