A presentation about the analytics principles and frameworks that can be applied to drive growth at software companies.

1. The Analytics of
Growth Hacking
A peek behind the kimono
Jack Mardack
Head of Growth
Chartcube
Thanks for the invite, Mark!

2. Agenda
To share some perspectives and experiences from my domain.
• Unique beasts are hard to measure
• The Mall Metaphor: intent and measurement
• The Magic Garden Metaphor: the power of the funnel
• The Four Quadrants of User Data
• Components of an Analytics Stack
• Prezi: Activation, Retention, and the Agency of Content
• Prezi: ETL Automation and Scale
• Analytics of Product/Market Fit

3. Your Speaker
Twitter: @2hp

4. Unique Beasts are hard to Measure
• Software applications are each totally unique measurement spaces.
• Meant to do different things, functionally.
• Built from scratch by different people with different technologies.
• “Measure everything!” is impossible and fails to expose the right data.
• What do you measure
when there’s no
common anatomy?
• Whatever you choose
will be totally
arbitrary.

5. The Mall Metaphor
What would you measure if you decided you
wanted to get more people to your store? How?
• # ppl. who come in your door?
• # of ppl. who come in the mall?
• How ppl. get to you (in the mall)?
• Where else they go in the mall?
• How people get to the mall?
• Coupons were the first cookies!
Measurement

6. The Magic Garden Metaphor
First you see things this way.
Then you see them this way.
A funnel is arbitrarily deciding to relate two events. Then optimizing.
Possible Solutions:
a) Put up a sign
b) Build a path
c) Install teleporting
phone booths

7. The Four Quadrants of User Data
1. How did you
get here?
2. Who are
you?
3. What have
you done?
4. What do you
think?
Domain
Marketing
(Acquisition)
Marketing/Pro
duct
Product Product/Resea
rch
Data sources
3rd party trackers*,
your own
collection
Forms & surveys,
in-product
behaviors, your
own detection*
3rd party loggers*,
your own logging
NPS, surveys,
interviews, ML*
Data types
Campaign names,
identifiers
Geo, lang, your
made-up segment
labels
Events, identifiers,
objects, metadata,
statuses
Scores, indices

8. Components of an Analytics Stack
Multi-platforms creates data heterogeneity,
fractionates the full user record
Homogenization step is an aggressive
“blend” process that makes “platform” a
column and unifies user identity
Computation (ETL) gives full control
over calculation of metrics and other
computationally-expensive values
Combines the “push” power of dashboards for
everything, plus the “pull” power of querying to
answer new questions

9. Prezi: Activation, Retention and the Agency of Content
1. We expose the steps to activation (as a
dashboard) and see there’s a big dropoff at
editor entry, which we correct with product
mechanics and other tactics.
1.
2. Because we have a4 values
for all users, we can do
correlative analyses and confirm
the agency of content on
activation. We start to use
content all over the funnel.
2.
3. Confirm lift in retention out to 8
months.

10. Prezi: ETL Automation & Scale
1. >10M events per day
2. >250MB log data per day
3. >350 metrics per day
Anatomy of a Computationally-Expensive Metric
Daily Activation Rate:
• List user ids for yesterday’s signups
• List subset of user ids who have >0 prezis
• List all those prezi ids
• Loop through all prezis in set (each prezi is
itself an event set with n events) and look
for desired sequence
• Write “a(n)” metric score to each user in
table based on the longest sequence found
1.
2.
3.

11. Analytics of Product/Market Fit
What is P/M Fit? Target group of people using the product in the way you intended.
• Create an experimental framework that
allows you to follow the performance of
differently targeted cohorts.
• Note that product use performance and
monetization performance may diverge.
• The fit you get may not be the fit you
wanted.

12. Chartcube Demo
app.chartcube.com

13. Thank you!