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
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
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
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
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?
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
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