The Analytics of Growth HackingA peek behind the kimono

Jack MardackHead of Growth

Chartcube

Thanks for the invite, Mark!

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

Your Speaker

Twitter: @2hp

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.

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

The Magic Garden MetaphorFirst 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

The Four Quadrants of User Data1. How did you get here?

2. Who are you?

3. What have you done?

4. What do you think?

Domain Marketing (Acquisition)

Marketing/Product

Product Product/Research

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

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

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.

Prezi: ETL Automation & Scale

1. >10M events per day2. >250MB log data per day3. >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.

Analytics of Product/Market FitWhat 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.

Chartcube Demoapp.chartcube.com

Thank you!