Making the Most of Customer Data


Published on

Published in: Technology
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Making the Most of Customer Data

  1. 1. Making the Most of Customer Data Srinath Perera Director, Research, WSO2 Inc. Visiting Faculty, University of Moratuwa Member, Apache Software Foundation Research Scientist, Lanka Software Foundation
  2. 2. 2 About WSO2 §  Global  enterprise,  founded  in  2005 by acknowledged  leaders in XML, web services technologies, standards and open source §  Provides only open source platform-as-a-service for private, public and hybrid cloud deployments §  All WSO2 products are 100% open source and released under the Apache License Version 2.0. §  Is an Active Member of OASIS, Cloud Security Alliance, OSGi Alliance, AMQP Working Group, OpenID Foundation and W3C. ๏  Driven  by  Innova=on   ๏  Launched  first  open  source  API   Management  solu=on  in  2012   ๏  Launched  App  Factory  in  2Q  2013   ๏  Launched  Enterprise  Store  and   first  open  source  Mobile  solu=on   in  4Q  2013  
  3. 3. 3 What WSO2 delivers
  4. 4. 4 Business Model
  5. 5. Outline §  Connected Business and Big data analytics §  Why use Analytics? §  Big Data Technologies from WSO2 §  BAM – Batch analytics §  CEP – Real time analytics §  Lambda Architecture to combine §  From your business to insights §  Understand the Customers §  Targeted Marketing §  Understand Competition and Market §  Optimize Operations §  Predict Outcomes
  6. 6. Adaptive Connected Business
  7. 7. Connected Business
  8. 8. Be Adaptive §  Capture business activity (identified by messages, transaction execution, and data state changes) and store data points for future analytics §  Deliver automated notifications to stakeholders and systems based on business activity, stakeholder accountability, and authority. §  Automatically adapt business process execution based on events and current conditions
  9. 9. Big Picture
  10. 10. Why Analytics? §  Because there is room to improvement, and you do not know where and how! §  Few Areas o Understand customers o Understand the Market and competition o Efficient Marketing o Optimize your operations o Predict outcome
  11. 11. Understand the Customers § Not all customers are equal (80/20%) o Bring different amount of revenue o Needs different things o Lives in different areas o Use your service at different times o Responds to different things
  12. 12. Marketing §  Old broadcast model of marketing o People are getting better at ignoring o People hate when you knocking on the door o Most eyeballs are at internet §  Market to people who are interested? Key is finding who is interested
  13. 13. Understand the Market and Competition §  What if we can? o Know how what market thinks (follow social feeds)? o Know what customers like and dislike? o Know who are unhappy? (e.g. find and react to churn)? o What subset of customers like our products?
  14. 14. World is inefficient §  About 50% of cooked food wasted §  About 30% vegetables and fruits wasted §  5% revenue on average lost to fraud, and 22% of cases are > 1M §  Most energy (e.g. lighting, mechanical) is wasted §  So much time lost waiting for things, cleaning up messes, finding things
  15. 15. Big Data Technologies
  16. 16. Collecting Data §  Data collected at sensors and sent to big data system via events or flat files §  Event Streams: we name the events by its content/ originator •  Get data through – Point to Point – Event Bus •  E.g. Data bridge – a thrift based transport we did that do about 400k events/ sec
  17. 17. Making Sense of Data §  Basic Analytics o To know (what happened?) o Statics (min, max, average, histogram … ) + visualizations o Interactive drill down §  Advanced Analytics o To explain (why) - Data mining, classifications, building models, clustering o To forecast – Regression, Neural networks, decision models
  18. 18. Dashboards and last Mile §  Presenting information o  To end user o  To decision takers o  To scientist §  Interactive exploration §  Sending alerts stevefaeembra/3604686097/
  19. 19. Big Data Architecture
  20. 20. Data Collection •  Can receive events via SOAP, HTTP, JMS, .. •  WSO2 Events is highly optimized version (400K events TPS) •  Default Agents and you can write custom agents. Agent agent = new Agent(agentConfiguration); publisher = new AsyncDataPublisher( "tcp://localhost:7612", .. ); StreamDefinition definition = new StreamDefinition(STREAM_NAME, VERSION); definition.addPayloadData("sid", STRING); ... publisher.addStreamDefinition(definition ); ... Event event = new Event(); event.setPayloadData(eventData); publisher.publish(STREAM_NAME, VERSION, event);
  21. 21. Business Activity Monitor
  22. 22. BAM Hive Query Find how much time spent in each cell. CREATE EXTERNAL TABLE IF NOT EXISTS PlayStream … select sid, ceiling((y+33000)*7/10000 + x/10000) as cell, count(sid) from PlayStream GROUP BY sid, ceiling((y+33000)*7/10000 + x/10000);
  23. 23. Complex Event Processor
  24. 24. CEP Query define partition sidPrt by PlayStream.sid, LocBySecStream.sid from PlayStream#window.timeBatch(1sec) select sid, avg(x) as xMean, avg(y) as yMean, avg(z) as zMean insert into LocBySecStream partition by sidPrt from every e1 = LocBySecStream -> e2 = LocBySecStream [e1.yMean + 10000 > yMean or yMean + 10000 > e1.yMean] within 2sec select e1.sid insert into LongAdvStream partition by sidPrt ; Calculate the mean location of each player every second Detect more than 10m run
  25. 25. Lambda Architecture
  26. 26. Applying Big Data Technologies
  27. 27. Understand the Customers §  Process transactions logs using Hive o Building a profile for customers o Identify key 20% that brings in most revenue o Identify what features and feature combinations they like most o Find how they reached you How? Can be done via basic analytics (Hive and Basic Stats)
  28. 28. Build a Profile for Customers §  Get them to register (gets you basic demographics) §  Track what they like, what they view? What they buy? §  Track how often they buy? Where he live (from client IP)? §  Follow their social feeds, gauge the sentiments, find what they like How? > 50% via basic analytics, rest need some NLP, finding similar items, classification etc.
  29. 29. Targeted Marketing 1.  Know your stats: know Leads => Sales conversion rate, and details about the pipeline. 2.  Analyze user profiles and target your activities (e.g. based on location, interests etc.) 3.  Tag campaigns and track the effect (Google Adv, workshops, events, email campaigns, even TV or paper adv) 4.  Find how activities affects Leads => Sales. 5.  Use the data for predictive modeling How? 1-4 with basic analytics + activity monitoring. #5 with advanced analytics
  30. 30. Understand the Market and Competition §  Know your current customers and opportunities are? Find the risk (e.g. predict Churn) §  Find which leads are most effective at conversion? §  What common sequences users do often? May be package it as a new product? §  Track social feeds for what users are saying. Track sentiments. Convert complains to praises by acting fast. How? 20% basic analytics and rest advanced analytics
  31. 31. Optimize Operations §  Instrument your operations pipeline. Know what happens, where resources spent? o Manufacturing pipeline o Sales pipeline o Marketing pipeline §  Do predictive maintenance §  Optimize your IT infrastructure §  Lookout for fraud! (often cost > 30%) How? 40% basic analytics and rest advanced analytics
  32. 32. Operation Dashboard §  Real time view of your business §  Visualizations that shows the bottom line at a glance. §  KPIs, thresholds and alerts §  Drilldown when there are problems (see Webinar “Gaining Operational Intelligence ‪with WSO2 BAM”) §  Different views for different roles
  33. 33. Predict Outcomes §  Plan the operations, look for risks. §  Use old data to predict outcomes. Fine tune and improve models. §  Do what if analysis, use that to drive your decisions §  Try to find predictions on key external factors (e.g. Oil and manufacturing companies invest on weather forecasts. )
  34. 34. Conclusion §  Analytics are important to you Business o Because there is lot of room to improvements, but you do not know where. §  The Big Data platform §  Applying Big Data technologies §  Understand the Customers §  Targeted Marketing §  Understand Competition and Market §  Optimize Operations §  Predict Outcomes
  35. 35. Questions?