Actionable Analytics Mongo Philly 2011 Sheraton Society Hill Robert J. Moore CEO, RJMetrics April 26, 2011
Nov 01, 2014
Actionable Analytics
Mongo Philly 2011Sheraton Society Hill
Robert J. MooreCEO, RJMetricsApril 26, 2011
What We’ll Explore
• My Background (Who is this guy?)
• Metrics & Developers
• Storing the Right Data
• Six Key Metrics
What We Won’t
• A Commercial for RJMetrics
• An In-Depth Technical Review
• A One-Way Lecture
Who is this Guy?
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
Metrics & Developers:Perfect Together
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
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!
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
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
The Data
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
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)
The Metrics
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
1. Long-Term Engagement
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
2. Repeat vs. First-Time Actions
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
3. Time Between Actions
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?
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
4. Repeat Action Probability
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”)
5. Customer Lifetime Value
• Segmentation Opportunities– Which segment are performing well?– Demographics– Geographics– Acquisition Sources– Behavioral Characteristics– Time-based Cohorts
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
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
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?
6. Cohort Analysis: Traditional
6. Cohort Analysis: Relative
6. Cohort Analysis: Relative
6. Cohort Analysis: Cumulative
6. Cohort Analysis: Avg/Member
6. Cohort Analysis: Avg/Member
Conclusions
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
PlugsTwitter:@RJMetrics@robertjmoore
Visit our Website:http://www.rjmetrics.com/
E-Mail Me:[email protected]
We are hiring!http://www.rjmetrics.com/jobs