Actionable analytics with mongo db mongophilly-2011

Actionable Analytics Mongo Philly 2011Sheraton Society Hill
Robert J. Moore
CEO, RJMetrics
April 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: Cumulative

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

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

Actionable analytics with mongo db mongophilly-2011

by Dwight Merriman on Apr 28, 2011

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Actionable analytics with mongo db mongophilly-2011 — Presentation Transcript