1. ©2015 Apigee Corp. All Rights Reserved. Preserving signal in customer journeys Joy Thomas, Apigee Jagdish Chand, Visa

2. ©2015 Apigee Corp. All Rights Reserved. Overview Customers journeys and event data Customer Behavior Graph Queries on Behavior Graphs Predictive models on behavior graphs 2

3. © 2014 Apigee Confidential – All Rights Reserved Customer View: A journey 3 Consumers interact with enterprises through multiple channels at multiple points of time Each of these interactions is an event with a timestamp and the sequence of interactions is important

4. © 2014 Apigee Confidential – All Rights Reserved A graphical structure can identify common interactions and influences 4 Common Interactions & InfluencesCustomer Journey

5. Customer behavior graphs vs. social graphs 5 Behavior Graph Sequence of events: –Actions experienced and taken Social Graph Links between people & activities –At a point in time Behavior graph Social graph

6. ©2015 Apigee Corp. All Rights Reserved. Model for User Behavior Users act on nodes in a temporal sequence of events 1 352 2 5 0 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 seconds Scrolls: 3 EVENT Type: AdView UserID: U56 AdID: Creative95 PlayTime: 30 sec Rewinds: 1

7. ©2015 Apigee Corp. All Rights Reserved. Aggregated Behavior Graph (ABG) 0 1 2 3 5 0 1 352 2 5 Impressions: 1 TimeSpent: 20 Clicks: 1 0 0 Impressions: 4 TimeSpent: 10 Clicks: 0 Impressions: 5 TimeSpent: 30 Clicks: 1 Combine Characteristics Represents flow & behavior of all users Automated construction from event streams Information preserving Aggregated representation Permits drill-down Useful for reasoning about customer flows Count unique users at node/edge Aggregate metrics at nodes/edges Measure drop-offs on a path (funnel) Profile traffic at a node or edge Analyze flows for user segments

8. ©2015 Apigee Corp. All Rights Reserved. Examples of queries on Behavior Graph Count the number of users who went from A -> B -> C -> D Find the distribution of (Age, Gender) for the people who took the path P-> Q ->R Of all the females in California who went from C to D, what are the most likely nodes that they are likely to visit next Of the people who bought a computer 3 months ago and received an email offer for a discounted printer 1 month ago, what fraction of them have bought printers in the last month All these queries would be painful to express in SQL on a large event table 8

9. ©2015 Apigee Corp. All Rights Reserved. Predictive Analytics on Behavior Graphs Past behavior of consumers is the best predictor of future actions The behavior graph allows one to search for patterns of consumer behavior that are correlated with responses of interest Using the patterns we can build a Bayesian model to predict what users will do next Use the predictive model for recommendations, targeting and churn prediction 9

10. ©2015 Apigee Corp. All Rights Reserved. Comparison with other Machine Learning algorithms Most machine learning algorithms assume that the training data for a learning algorithm is in a form of a large table of examples, with responses in one column, and features in other columns, e.g. Logistic Regression, Random Forest, etc. These algorithms are designed for profile attributes such as age, gender, country, etc. To handle event data, the data scientist typically creates aggregate features out of the event data (e.g. total purchases over the last year, total purchases over the last month, etc.) The behavior graph allows the data scientist to automatically search over a large space of aggregates to use in the predictive model 10

11. ©2015 Apigee Corp. All Rights Reserved. Summary Event data should be treated differently from profile data A graphical data structure designed for event data can efficiently answer queries on event based patterns Event based patterns can be used to build predictive models for targeting, recommendations and churn prediction There is a need for a common query language to express queries for event data 11

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