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Investigative analytics and derived dataThe example of customer acquisition & retention
Curt A. Monash, Ph.D.President, Monash Research
Editor, DBMS2
http://www.monash.comhttp://www.DBMS2.com
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.
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
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
Cheap data (creation and/or acquisition)
Logs Sensors Web/mobile/social Location
Machine-generated data is subject to Moore’s Law
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
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)
Major application examples
Traditional marketing interaction Call center decisioning Website personalization Outbound campaigning
Personal outreach, determined by Customer importance Social media commentary
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
Key intermediate results
Characterize person* Identify person*
*Or household
Trace personal relationships Correlate actions to outcomes Value outcomes
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
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.
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 3rd-party, inc. mobile
External Address/demographic Credit card Social media
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!
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”
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