[GrowthHacker Conference '16] Annabell Satterfield Senior PM, Growth at BitTorrent: Engaging Customers To Find Your Next Big Growth Opportunity

February 18, 2016
Annabell Satterfield
Annabell Satterfield @als355

Core
Value
Product
Growth
Product = delivery
device for the core
value
This includes
acquisition funnels,
engagement mechanics
*Consumer products

Core
Value
Product
Growth
Growth-driven
product culture
*Andy Johns
Foundational strategy
set with data
Beyond features
Early deposits to
“compounding growth”
*

Pic of client team

Agenda
Processing data > insight with
Bucketing
Screening + Tradeoff decision modeling
Quantify utility
A/B test to maximize profit
In order to:
Prioritize next steps
Internationalize
Prioritize (paid) features
Choose prices
Getting from data > insights > action

Warning:
Outcomes here are real.
Most data here is not.

Master framework to get from Data > Insights > Action

Work backwards.
1. Define the question
Question

Work backwards.
2. Define outcome
Outcome

Work backwards.
2. Define outcome
3. Is what I need out there? Go get it.
Data

Work backwards.
2. Define outcome
3. Is what I need out there? Go get it.
4. Process the data
Process

Let’s take this to the streets

Case 1- Next steps
Data: Survey Process: Bucketing

Question
“What features will you build to drive paid app revenue?”

Question
“What features will you build to drive paid app revenue?”
Among these, what will drive the most paid app revenue:
price testing, adding features, building awareness?

Outcome
Awareness
Price
Features
#1
#3
#2
Ranked opportunities

Outcome
Audience:
Core users, top geos
for paid product
Pay for stuff
Awareness
Price
Features
#1
#3
#2

Data
Does this data exist? No.

Data
Does this data exist? No. Let’s go make it!

Data
Does this data exist? No. Let’s go get it!

Data

Process
Price
Features
Awareness
#1
#2
#3

+92% paid app revenue lift

Let’s take it to the next level.

Insert Page # Here
Case 2- Expanding internationally:
Data: External Databases, Product Data Process: Screening + Tradeoff

Question
Which geos and languages should we prioritize?

Outcome
#1 Country 1- Language
--
#X Country X- Language
Highest-potential… based on our goals.

Data
Does this data exist? Yes! Let’s go get it!

Data
Address these elements:
Market size
Likelihood to purchase/monetize

Data
Choose your data based on those elements:
Market size: Installed Android user base (all apps),
Install base for the BitTorrent Desktop clients
Likelihood to monetize: Relative value of mobile adverts

Process
Choose a weighting for each:
Market size: Installed Android user base [3],
install base for the BitTorrent desktop clients [4]
Likelihood to monetize: Value of adverts by geo [2]

Step 1- Screen
Min Criteria 1
Min Criteria 3
Min Criteria 2
Meets
all 3

Step 1- Screen
Mobile ad Value > $X
eCPM
Android User
Base >ZMM
BitTorrent
Desktop Users
>YMM
*Fake list

Step 1- Screen
Android User
Base >ZMM
BitTorrent
Desktop Users
>YMM
*Fake list
Mobile ad Value > $X
eCPM

Step 2- Prioritize
(Decile x
(1
..
(10
Android User Base
Weight)
3)
..
3)
Top
90%
..
10%
=
3
..
30
=
=
x
x

Step 2- Prioritize
(Decile x
1
..
10
Android User Base
Weight)
3
..
3
+
Ad value
Top
90%
..
10%
+
Desktop Users
=

Step 2- Prioritize
(Decile x
1
..
10
Android User Base
Weight)
3
..
3
+
Ad value
Top
90%
..
10%
+
Desktop Users
=
Value
68
..
90

And… do your due diligence
Think about re-adding:
Brazil
Russia
India
China
...others?
Ordered by value…*
#1 United States- English: 90
#2 Canada- English: 89
#3 Spain- Spain Spanish: 70
#4 S. Korea- Korean: 68
…
*Fake list

Spain +93% w/w
Brazil +43%
Italy +32%
Russia +23%
Installs in high-potential geos targeted for growth with
translation:

Case 3- [Paid] Feature Prioritization
Data: Survey Process: Quantifying Utility (+ ROI analysis )

Question
Will more users be willing to buy Pro if we released more features?
Which ones?

Outcome
Feature #1- Utility value of X
Feature #2- Utility value of Y
--
Feature #N- Utility value of Z

Outcome
Feature #1- Utility value of X
Feature #2- Utility value of Y
--
Feature #N- Utility value of Z
Impact of new feature on likelihood to buy
Audience:
Core users, top geos
for paid product
Pay for upgrades

Remember this survey?
#2 Opportunity

Process
Calculate weighted value of each feature…
http://bit.ly/GHC2016

Calculated weight of each Pro feature:
Feature X: 5.78 Ranked #1
Feature Y: 5.59 Ranked #2
Feature Z: 5.20 Ranked #3
10+ features considered and prioritized with data… priceless

Outcome…
Battery-Saver: +47% Daily paid Revenue
Auto-shutdown: +20% Daily paid Revenue
uTorrent Android- top 5 grossing (for paid
upgrades] in its category in 71 geos
including US, UK, CA, AU*
Happy paying users
* App Annie, April 2015 (before moved to desktop team)

Sum
Case 4- Pricing
Data: Experiment Process: A/B testing

Question
What is the profit-maximizing price point for the paid app?

Outcome
In the United States,
$39.95- Profit per user
--

Process
Experimentation..
… essentially, pretotyping*
*Via Alberto Savoia, pretotyping.org

BitTorrent, Inc. |
Optimizely…
Only difference
is price

BitTorrent, Inc. |
Track to
actual sale

BitTorrent, Inc. |
For each:
[(units x price)
-
(units x COGS)]
impressions

Outcome…
Hit desktop team’s 2013 paid revenue goal
No change to product

Data>insights>action framework = a great habit to build.

These are not substitutes for experiments or MVPs.
They inform experiments.

Don’t let anything keep you from being data-driven where it counts.

Qualitative data can fill in the gaps where other data ends.

Bucketing
Quantify utility
In order to:
Prioritize next steps
Internationalize
Prioritize (paid) features
Choose prices
These tools are multifunctional

Bucketing
Quantify utility
You can also:
Isolate problems with feedback
Prioritize B2B features w/survey
Develop keep/kill feature list
Pretotype unbundled paid
products
More…
These tools are multifunctional

Product + Growth

Don’t let anything keep you from being data driven where it counts.

Q & A
Slides:
http://bit.ly/GHC2016

Bingo
Talking to users
Open-ended surveys
OPR (OP Research)
Product data
Databases
Product data
Surveys*
Experiments
OPR
Talking to users
Databases
Experiments
Pretotypes
Surveys*
Databases
*term used loosely
Choose data based on what you understand about your problem…

Paid Feature Prioritization- how to calculate weighted value…

Raw data

Sum

Divide

Multiply

[GrowthHacker Conference '16] Annabell Satterfield Senior PM, Growth at BitTorrent: Engaging Customers To Find Your Next Big Growth Opportunity

[GrowthHacker Conference '16] Annabell Satterfield Senior PM, Growth at BitTorrent: Engaging Customers To Find Your Next Big Growth Opportunity

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[GrowthHacker Conference '16] Annabell Satterfield Senior PM, Growth at BitTorrent: Engaging Customers To Find Your Next Big Growth Opportunity