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Actionable analytics with mongo db mongophilly-2011

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  • 1. Actionable Analytics Mongo Philly 2011Sheraton Society Hill
    Robert J. Moore
    CEO, RJMetrics
    April 26, 2011
  • 2. What We’ll Explore
    My Background (Who is this guy?)
    Metrics & Developers
    Storing the Right Data
    Six Key Metrics
  • 3. What We Won’t
    A Commercial for RJMetrics
    An In-Depth Technical Review
    A One-Way Lecture
  • 4. Who is this Guy?
  • 5. Robert J. Moore
    Finance and Computer Science
    Venture Capital Industry
    Transition from Deal Sourcing to Data Analysis
    Exposure to Tech Orgs of Amazing Companies
    RJMetrics
    Technical co-founder and CEO
    Hosted business intelligence
    Providing access to deep insights for online SMBs
  • 6. Metrics & Developers:Perfect Together
  • 7. Developers Have Power
    Historically: power over product, progress, timelines…
    In the age of data: access to information
    Modern leaders “manage by metrics,” making those with access gatekeepers to success
  • 8. A Growing Divide
    As data sets get larger, they get farther out of reach of non-technical data consumers in the enterprise
    Excel isn’t enough
    Access isn’t enough
    SQL isn’t enough!
  • 9. A Gift and A Curse
    Developers become a key part of the business
    New technology can raise barriers before it lowers them
    Things get lost in translation
  • 10. Embrace the Power
    Know “what” and “why”
    Invest time in understanding the motivation behind data-related requests
    You will save time and add value in the long run
  • 11. The Data
  • 12. Good Practices
    A database can be both functional and well-suited for analysis (or warehousing)
    Overwrites are usually a bad idea
    Enforce consistency/cleanliness
    Timestamps are our friends
  • 13. Common Themes
    Every business has its own unique needs
    Most operational data has common themes:
    Entities (users, customers, visitors)
    Actions of Value (purchases, logins, interactions)
  • 14. The Metrics
  • 15. 1. Long-Term Engagement
    Focusing on “total registered users” or “total customers” is a common trap
    What happens to these users over time?
    What is your “Active” base?
    This is a common input to valuations
  • 16. 1. Long-Term Engagement
  • 17. 2. Repeat vs. First-Time Actions
    Digging deeper, we differentiate between newcomers and repeaters
    Acquisition vs. retention
    Helps separate biases from #1 caused by explosive new user growth
  • 18. 2. Repeat vs. First-Time Actions
  • 19. 3. Time Between Actions
    Actual magnitude can vary wildly by industry
    Ultimately, it’s the relative numbers that are interesting
    Does your product/service have “addictive” properties
  • 20. 3. Time Between Actions
  • 21. Bias Warning
    Always consider the timeframe of the data you’re examining, especially when looking at metrics involving time
    Why might “average time between purchases” for newer customers look different than for older ones?
  • 22. 4. Repeat Action Probability
    The “subsequent action funnel”
    Historically speaking, once someone has done something once, what is the chance they’ll do it again?
    Calling this a “probability” assumes it incorporates enough history to be representative of the long-term behavior of the population
  • 23. 4. Repeat Action Probability
  • 24. 5. Customer Lifetime Value
    A key “actionable” metric
    Informs marketing spend
    Influences retention strategy
    Multiple Definitions
    Lifetime Revenue (“Value So Far”)
    Expected Lifetime Revenue
    Lifetime Gross Margin (“Contribution”)
  • 25. 5. Customer Lifetime Value
    Segmentation Opportunities
    Which segment are performing well?
    Demographics
    Geographics
    Acquisition Sources
    Behavioral Characteristics
    Time-based Cohorts
  • 26. 6. Cohort Analysis
    The venture investor’s favorite slide
    Incorporates everything we’ve discussed
    Engagement
    New & Repeat Actions
    Timing of Events
    Repeat Frequency/Probability
    Lifetime Value Accumulation
  • 27. 6. Cohort Analysis
    Pulling the data
    Associate every event with two timestamps:
    The timestamp of the event
    The “cohort timestamp” of the user responsible (this can be a registration date, first action date, etc) – the value of this field will not change from record to record for the same user
    Break the users into “cohorts”
    Yearly
    Quarterly
    Monthly
    Weekly
    Daily
  • 28. 6. Cohort Analysis
    Pulling the data (ctd)
    Study these “cohorts” side-by-side, with their “ages” on the x-axis instead of actual calendar dates
    This allows you to study how different customer cohorts have interacted with your site over time
    Are newer cohorts stronger or weaker than older ones?
  • 29. 6. Cohort Analysis: Traditional
  • 30. 6. Cohort Analysis: Relative
  • 31. 6. Cohort Analysis: Relative
  • 32. 6. Cohort Analysis: Cumulative
  • 33. 6. Cohort Analysis: Avg/Member
  • 34. 6. Cohort Analysis: Avg/Member
  • 35. Conclusions
  • 36. Conclusions
    As the data grows, so does its importance and so does the power of its keepers
    Design with future analysis in mind
    Always understand the “why” behind requests and you’ll save time in the long run
  • 37. Plugs
    Twitter:
    @RJMetrics@robertjmoore
    Visit our Website:
    http://www.rjmetrics.com/
    E-Mail Me:
    rmoore@rjmetrics.com
    We are hiring!
    http://www.rjmetrics.com/jobs