1. Investigative analytics and derived data The example of customer acquisition & retention Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2 http://www.monash.com http://www.DBMS2.com

2. Me

3. The six things you can do with analytic technology Operational BI/Analytically-infused operational apps: Make an immediate decision. Planning and budgeting: Plan (in support of future decisions). Investigative analytics (multiple disciplines): Research and analyze (in support of future decisions). More BI: Monitor, to see when it necessary to decide, plan, or investigate. Yet more BI: Communicate what you’ve learned. DBMS, ETL, etc.: Support the other functions.

4. Investigative analytics Is the most rapidly advancing of the six areas...... because it most directly exploits performance & scalability. Investigative analytics = seeking (previously unknown) patterns in data

5. Investigative analytics technology Fast query  Persistent storage (any data volume)  RAM (10s -100s of gigabytes, or more) Fast analytics  Statistics/machine learning  Transformation/tagging  Graph

6. Cheap data (creation and/or acquisition) Logs Sensors Web/mobile/social Location Machine-generated data is subject to Moore’s Law

7. Key investigative analytics techniques Iterative query  Conventional  Visualization-centric Predictive modeling  Regression, etc.  Clustering, etc. Relationship analytics  Graph Intelligent transformation  Text  Log  See above …  … and that’s the punch line

8. Today's example application area Customer acquisition and retention, which Exploits most cool aspects of analytic technology Is needed by almost everybody In the interest of time, we'll focus on consumer-type customers (as opposed to complex organizations)

9. Business goals Best persuasion Most effective offer Identify & avoid undesirables

10. Major application examples Traditional marketing interaction  Call center decisioning  Website personalization  Outbound campaigning Personal outreach, determined by  Customer importance  Social media commentary

11. Analytic result wish list Ideal deal  Price  Special offer  No offer (fraudster, unprofitable) Best communication  Web/Mobile ad  Call-center script  Personal outreach And to support all that  Understand value of outcomes  Categorize/cluster targets to get best results

12. Key intermediate results Characterize person* Identify person* *Or household Trace personal relationships Correlate actions to outcomes Value outcomes

13. Kinds of data available Classical transactions ("actions") Records of "interactions"  Call center records  Weblogs Same stuff, other businesses  Credit card, etc.  Cross-site tracking Social media  What people say  Who they say it to Direct tracking  Census/address  Mobile location

14. Derived data You can’t keep re-analyzing all that in raw form …... so don’t. If you have one takeaway from this session, let it be the utter importance of derived data.

15. Example: Telco churn inputs Transactions Usage  Quantity/timing  Targets  Location? Complaint/contact  Direct (Email, call center)  Website browse Actual uptime/outages Offer responses  Telco offers  3 rd -party, inc. mobile External  Address/demographic  Credit card  Social media

16. Example: Telco churn derived data Normalized data  Parsed/sessionized logs  Text/sentiment highlights  Social network graph(s)  Web deanonymization  Household matching Scores and buckets  Demographic  Psychographic  Offer hotbuttons  (Dis)satisfaction  Credit/fraud risk  Lifetime customer value  Influence on others!

17. Best practices for derived data Evolving data warehouse schema Data marts  Physical or virtual  Inputs/outputs to “EDW” “Data science”  Research != production Multiple processing pipelines  Log parsing  Text  Predictive analytics  Generic ETL  Streaming “ETL”

18. Social conscience Like many other technologies, analytics can be badly misused Analytic use/misuse is a tough society-wide systems problem In a free society  Government has powerful tools for tyranny …  … but its use of those tools is sharply regulated Our expertise is needed to help define the regulations The data WILL be collected and analyzed … … so we need to be smart about regulating its use

19. For more detail ……

20. Curt A. Monash, Ph.D. President, Monash Research Editor, DBMS2 contact @monash.com http://www.monash.com http://www.DBMS2.com Thank you!