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Analyzing Complex Behavior Graphs in Hadoop at Scale

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Employing traditional approaches to analyzing customer behavior graphs and event sequences requires data simplifications, generalizations, and segmentations that severely degrade prediction accuracy and can lead to the loss of valuable information.

But there’s an alternative approach that retains the fidelity of customer profile data, the full sequence of events data, and enables powerful business intelligence and predictive analytics. In this webcast, join Apigee’s Joy Thomas, Chief Scientist, and Sanjeev Srivastav, VP Data Strategy, as they explore the superiority of new methods of behavior graph analysis over the simplifications required for traditional data storage and classical predictive algorithms.

Join to learn:

• How shortcomings of traditional approaches make it difficult to build and analyze complex behavior graphs

• Why GRASP—graph and sequence processing technology on Hadoop presents a different and more effective approach to behavior graph analysis

• How descriptive analytics using GRASP uncover hidden patterns in historical customer journey data

• How Predictive analytics that use behavior graphs and Bayesian algorithms, along with machine learning, ensure model performance over time

Download Video: http://youtu.be/CwxvlgW9aZY
Download Podcast: https://soundcloud.com/apigee/analyzing-complex-behavior-graphs-in-hadoop-at-scale

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Analyzing Complex Behavior Graphs in Hadoop at Scale

  1. 1. Analyzing Complex Behavior Graphs in Hadoop at Scale
  2. 2. Today’s speakers Sanjeev Srivastav @ssrivastav Joy Thomas @JoyAThomas1
  3. 3. Apigee social channels © 2014 Apigee – For Public display 3 YouTube http://youtube.com/apigee Slideshare http://slideshare.com/apigee
  4. 4. Agenda • Customer behavior graphs • GRASP: an event model for customer interactions • GRASP for – the customer journey – predictive modeling © 2014 Apigee – For Public display
  5. 5. Customer behavior graphs
  6. 6. Why do we need behavior graphs? © 2014 Apigee – For Public display
  7. 7. Customer view: a journey © 2014 Apigee – For Public display 7
  8. 8. Understand each customer’s journey © 2014 Apigee – For Public display 8 siloed view customer journey
  9. 9. Identify common interactions and influences © 2014 Apigee – For Public display 9 customer journey common interactions & influences
  10. 10. Customer behavior © 2014 Apigee – For Public display 10 Behavior graph • sequence of events: – actions experienced and taken Social graph • links between people & activities – at a particular point in time Behavior graph Social graph
  11. 11. Why do we need new technology for © 2014 Apigee – For Public display behavior graphs?
  12. 12. Challenges with current technologies © 2014 Apigee – For Public display 12 • SQL is not efficient for sequences of events and quick counting of customer journeys • Custom algorithms are expensive • Various graph systems are oriented toward social graphs and computations of neighborhoods, “friend of a friend,” etc.
  13. 13. Event model for customer interactions: GRASP
  14. 14. GRASP • Graph and sequence processing - time-sequenced graph analytics on Hadoop © 2014 Apigee C– oFnofri dPeunbtilaicl –diAsplll aRyights Reserved 14
  15. 15. Model for user behavior Users act on nodes in a temporal sequence of events © 2014 Apigee – For Public display 0 1 2 5 3 2 5 0 USER PROFILE UserID: U56 Gender: M Geo: San Francisco Interests: bikes, fashion USER PROFILE UserID: U57 Gender: F Interests: news, finance Age: 35-40 NODE PROFILE Type: Content PageID: P100 Category: product review SubCat: mountain bike NODE PROFILE Type: Creative ID: Creative95 Category: VideoAd Advertiser: BikePros EVENT Type: PageView UserID: U56 PageID: P100 TimeSpent: 180 sec. Scrolls: 3 EVENT Type: AdView UserID: U56 AdID: Creative95 PlayTime: 30 sec. Rewinds: 1
  16. 16. Aggregated behavior graph © 2014 Apigee – For Public display 0 1 Impressions: 5 TimeSpent: 30 Clicks: 1 2 3 5 0 Combine 1 2 5 Impressions: 1 TimeSpent: 20 Clicks: 1 3 2 5 0 0 Impressions: 4 TimeSpent: 10 Clicks: 0
  17. 17. Event streams Store visits Emails Phone calls Purchases F, 35, Married Combined event stream © 2014 Apigee – For Public display 17 Event stream Purchase health insurance Offer for Health Insurance M,25, Married GRASP merges event streams and normalizes time relative to responses
  18. 18. User dimension © 2014 Apigee – For Public display Data representation • Data model: events & dimensions • Data structure: aggregated behavior • Graphs for events, not tables • Data access: API GRASP: graphs vs. tables 18 Event facts are represented as graphs, not tables SQL is not effective for sequence queries 1 2 3 4 0 Node dimension Events Data storage & computation • Distributed data structure on Hadoop • Computation using map reduce
  19. 19. Dual use of GRASP: customer journey & predictive models
  20. 20. How has this been used for solving customer engagement problems? © 2014 Apigee – For Public display
  21. 21. Dual use of GRASP for analytics Graph and sequence processing (GRASP) descriptive analytics GRASP Adaptive applications Segments manager, GQM Business user GQM Data Profiles Text Events Feature extraction Events Predictive & Data scientist Developer Custom app Developer Predictive Descriptive Developer A X Y C D B © 2014 Apigee 2013 C– oFnofri dPeunbtilaicl –diAsplll aRyights Reserved 21
  22. 22. Visualize customer journeys across all interactions • Examples: telco, healthcare, retail © 2014 Apigee – For Public display
  23. 23. Build behavior prediction models • Machine learning using path sequences © 2014 Apigee 2013 C– oFnofri dPeunbtilaicl –diAsplll aRyights Reserved
  24. 24. Relevant problems to solve © 2014 Apigee – For Public display 24 • Multi-channel customer event data for understanding customer journeys and predicting behavior • Modeling evolving customer behavior using updates to behavior graphs
  25. 25. Questions? Sanjeev Srivastav @ssrivastav Joy Thomas @JoyAThomas1
  26. 26. Thank you © 2014 Apigee – For Public display

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