Actionable Analytics Mongo Philly 2011Sheraton Society Hill<br />Robert J. Moore<br />CEO, RJMetrics<br />April 26, 2011<b...
What We’ll Explore<br />My Background (Who is this guy?)<br />Metrics & Developers<br />Storing the Right Data<br />Six Ke...
What We Won’t<br />A Commercial for RJMetrics<br />An In-Depth Technical Review<br />A One-Way Lecture<br />
Who is this Guy?<br />
Robert J. Moore<br />Finance and Computer Science<br />Venture Capital Industry<br />Transition from Deal Sourcing to Data...
Metrics & Developers:Perfect Together<br />
Developers Have Power<br />Historically: power over product, progress, timelines…<br />In the age of data: access to infor...
A Growing Divide<br />As data sets get larger, they get farther out of reach of non-technical data consumers in the enterp...
A Gift and A Curse<br />Developers become a key part of the business<br />New technology can raise barriers before it lowe...
Embrace the Power<br />Know “what” and “why”<br />Invest time in understanding the motivation behind data-related requests...
The Data<br />
Good Practices<br />A database can be both functional and well-suited for analysis (or warehousing)<br />Overwrites are us...
Common Themes<br />Every business has its own unique needs<br />Most operational data has common themes:<br />Entities (us...
The Metrics<br />
1. Long-Term Engagement<br />Focusing on “total registered users” or “total customers” is a common trap<br />What happens ...
1. Long-Term Engagement<br />
2. Repeat vs. First-Time Actions<br />Digging deeper, we differentiate between newcomers and repeaters<br />Acquisition vs...
2. Repeat vs. First-Time Actions<br />
3. Time Between Actions<br />Actual magnitude can vary wildly by industry<br />Ultimately, it’s the relative numbers that ...
3. Time Between Actions<br />
Bias Warning<br />Always consider the timeframe of the data you’re examining, especially when looking at metrics involving...
4. Repeat Action Probability<br />The “subsequent action funnel”<br />Historically speaking, once someone has done somethi...
4. Repeat Action Probability<br />
5. Customer Lifetime Value<br />A key “actionable” metric<br />Informs marketing spend<br />Influences retention strategy<...
5. Customer Lifetime Value<br />Segmentation Opportunities<br />Which segment are performing well?<br />Demographics<br />...
6. Cohort Analysis<br />The venture investor’s favorite slide<br />Incorporates everything we’ve discussed<br />Engagement...
6. Cohort Analysis<br />Pulling the data<br />Associate every event with two timestamps:<br />The timestamp of the event<b...
6. Cohort Analysis<br />Pulling the data (ctd)<br />Study these “cohorts” side-by-side, with their “ages” on the x-axis in...
6. Cohort Analysis: Traditional<br />
6. Cohort Analysis: Relative<br />
6. Cohort Analysis: Relative<br />
6. Cohort Analysis: Cumulative<br />
6. Cohort Analysis: Avg/Member<br />
6. Cohort Analysis: Avg/Member<br />
Conclusions<br />
Conclusions<br />As the data grows, so does its importance and so does the power of its keepers<br />Design with future an...
Plugs<br />Twitter:<br />@RJMetrics@robertjmoore<br />Visit our Website:<br />http://www.rjmetrics.com/<br />E-Mail Me:<br...
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Actionable analytics with mongo db mongophilly-2011

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Actionable analytics with mongo db mongophilly-2011

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

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