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:rmoore@rjmetrics.com

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