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