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How To Build a Growth
Team From Scratch 
Thibault Imbert
Head of Growth | Adobe Spark
What is Spark?
Adobe Spark
What is Spark?
Pros Market Expansion
What is Spark?
Creative Cloud
If everyone is doing it, no one really does
We all care about
Growth!
What we saw
We all care about
Growth!
What we saw
Release/Launch
We all care about
Growth!
What we saw
Release/Launch
Dammit, let’s
ship this!
We all care about
Growth!
What we saw
Release/Launch
Dammit, let’s
ship this!
Revenue
Activation
Retention
Acquisition
We all care about
Growth again!
Any ETA on the new
instrumentation?
PM/PMM
We have these
features for the June
release first
This will have to go on
the backlog
EM
Product
First attempt
First attempt
This just does not work
How do you create a Growth team?
1
It starts with organic retention
You can’t grow a crappy product
So if you don’t have product market fit
It is too early to think about Growth, solve a problem first
Are people using your product?
Don’t be prisoner of the north star metric
Beware of vanity metrics (Downloads, Signups, MAUs)
Look at 1-mo RR
2
Start small
You probably won’t be given a team directly
Team up with one member or two
Look for low hanging fruits
Early wins are worth more than a pitch
3
Get executive sponsorship
“A demo is worth a thousand meetings”
The same applies to Growth
It is about a long term Growth strategy
It will be like running a marathon
You will never be done
It’s not about hacking
It’s just good marketing
If an experiment fails, you are still learning
Inconclusive result is not learning
4
Find the right people
Mindset > Skills
Driven by results, not tech
Promote small experiments vs big projects
Customer empathy
Be colocated
5
Buy before build
Test value before building
Go 3rd Party
After you hit the limitations, go homegrown
Chat
Chat Landing pages
Chat Landing pages Heatmap
6
You need your own engineers
Growth Product
We have code ready
we need to checkin
We have this
experiment we would like
to run!
PM
Engineer
Who are you? Let me
see your code
Great! Do you
have resources to make it
happen?
PM
Engineer
Second attempt
Why are you in my code base?
Why do you get to take short cuts?
We are already planning on doing this!
That’s not how we work!
One word: Trust
7
A squad, not a team
Engineers still part of the product team(s)
But on a Tour of duty
Easy org setup
Engineers familiar with product codebase
Squad composition
PM PD EM EngineersDS
Engineer Engineer
Growth Product
Hey Irina, can you
review my PR?
Sure, let me check!
8
Don’t ideate, look for opportunities
Go back to the data
Focus on impact and ROI (ICE score)
If successful, what’s the projected impact?
Conversion, open rates,
notifications are good candidates
9
Then you will hit the ceiling (local maximum)
Changes need to be made inside the product
Don’t bring more water to a leaky bucket
Improving Activation reduces CAC (Cost of Acquisition)
Better Activation drives retention and ultimately conversion to paid
When the right time, reinvest that money saved in Acquisition
10
Now it’s time to own the entire funnel
The Growth team now needs to focus on the entire funnel (ARRR)
Acquisition, Retention, Referral, Revenue
Split into mini-squads focused on each lever
90 day sprint focused on one metric to move
11
Never forget qualitative research
Queries and funnels provide directions
They don’t tell you the why
Power is in quant + qual
12
Have a process
Experiment Pipeline
(lever, hypothesis, ICE
prioritization)
Experiment Pipeline
(lever, hypothesis, ICE
prioritization)
Experiment Plan
(control vs variant, baseline,
metric)
Experiment Pipeline
(lever, hypothesis, ICE
prioritization)
Experiment Plan
(control vs variant, baseline,
metric)
Backlog
(sprint planning, link to
experiment)
Experiment Pipeline
(lever, hypothesis, ICE
prioritization)
Experiment Plan
(control vs variant, baseline,
metric)
Backlog
(sprint planning, link to
experiment)
Ship Meeting
(stakeholders signoff)
Experiment Pipeline
(lever, hypothesis, ICE
prioritization)
Experiment Plan
(control vs variant, baseline,
metric)
Backlog
(sprint planning, link to
experiment)
Ship Meeting
(stakeholders signoff)
Ship
(soft launched)
Experiment Pipeline
(lever, hypothesis, ICE
prioritization)
Experiment Plan
(control vs variant, baseline,
metric)
Backlog
(sprint planning, link to
experiment)
Ship Meeting
(stakeholders signoff)
Ship
(soft launched)
Experiment
Results Review
(what did we learn)
2 experiments examples
Background:
We have a secret Facebook Group called Spark Insiders, we have had a challenge recruiting users easily and
most importantly truly active users.
Hypothesis:
We believe that by elevating our most active users to Spark Insiders we can build an automated system to
recruit users automatically. This will result in higher quality users with higher engagement on the FB group. We
also believe that making these users Insiders will boost their return use and we can learn what are the traits of
an engaged user.
KPIs: # New Spark Insiders, Group activity/engagement
iOS: Spark Insiders Recruitment
iOS: Spark Insiders Recruitment
Custom landing page for opt-in
Custom landing page for opt-in
VIP email
Secret group with community of engaged users
Engagement, before and after the recruitment campaign
Before
After
0.76 shares per user/day 0.97 shares per user/day
27% increase
iOS: Spark Insiders - Impact on shares, last 60 days
Understanding power users
What features do they use in the first week?
How often do they open the app?
What time of day do they use the app and on which days?
What sources were they acquired from?
Was it an ad, a promotional email to the chain’s customer base, or some other place?
What devices do they use?
What is their demographic background, including age, income, and more?
What other apps do they use?
Emails collected used as Facebook Lookalikes Audiences
A Lookalike Audience is a way to reach new people on
FB who are likely to be interested in your business
because they're similar to your best existing customers.
Ads for acquisition can be created targeting audiences
“similar” to the power users you collected from within
the app.
http://bit.ly/2shVlzf
Background:
Our current M1 retention is 25%, we have to learn from users not returning enough on what we can do better
for them.
Hypothesis:
We believe that we can retarget lapsed users and ask them what we can improve to make Spark better for them.
We tried to do that manually, we now want to automate this to see if we can get feedback automatically
without any manual work.
KPIs: # Surveys completed, Quality of feedback provided
Spark Web: Lapsed User(Has not returned in the past 14 days)
Spark Web: Lapsed User(Has not returned in the past 14 days)
Top 1: Users are happy with the
product, just not using the product at
the cadence we expect.
Top 2: More product features.
Top 3: More control/flexibility/variety.
Top 4: Performance issues.
Top 5: Android.
Spark Web: Lapsed User(Has not returned in the past 14 days) : Results
“There are tons of platforms and sites out there vying for attention. How
can you stay top of mind for me? Online events like chats, featuring
more user content and alerting them, etc. Haven't used you only
because I forgot about you.”
Key quotes: Marketing
“Is this free if I own the Adobe Suite? Is there an educator discount? Do
you love people that need your awesome program to make the world a
better place? Do you offer affiliate programs so that I can work the cost
of it off by making more awesome videos and telling people to use the
program because it is the best. Bottomline I like the product just unable
to afford it right now. Thank you”
Key quotes: Awareness
Web: Lapsed User - Impact on shares, last 60 days
0.22 shares per user/day 0.22 shares per user/day
No impact
Control Treatment
Summary
Start with organic retention
It’s not about hacking
Hire the right people
Buy before build
A squad, not a team
Focus on one thing first
Move to product changes
Own the full funnel (ARRR)
Data does not tell you why
Have a process
2 great books I recommend
Thank you!
Follow me at @thibault_imbert
for the slides.

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Growth Hacking Conference '17 - Antwerp

  • 1. How To Build a Growth Team From Scratch  Thibault Imbert Head of Growth | Adobe Spark
  • 3. What is Spark? Pros Market Expansion
  • 5. If everyone is doing it, no one really does
  • 6. We all care about Growth! What we saw
  • 7. We all care about Growth! What we saw Release/Launch
  • 8. We all care about Growth! What we saw Release/Launch Dammit, let’s ship this!
  • 9. We all care about Growth! What we saw Release/Launch Dammit, let’s ship this! Revenue Activation Retention Acquisition We all care about Growth again!
  • 10. Any ETA on the new instrumentation? PM/PMM We have these features for the June release first This will have to go on the backlog EM Product First attempt
  • 12. This just does not work
  • 13. How do you create a Growth team?
  • 14. 1
  • 15. It starts with organic retention You can’t grow a crappy product
  • 16. So if you don’t have product market fit It is too early to think about Growth, solve a problem first
  • 17. Are people using your product? Don’t be prisoner of the north star metric Beware of vanity metrics (Downloads, Signups, MAUs) Look at 1-mo RR
  • 18. 2
  • 20. You probably won’t be given a team directly Team up with one member or two Look for low hanging fruits Early wins are worth more than a pitch
  • 21. 3
  • 23. “A demo is worth a thousand meetings”
  • 24. The same applies to Growth
  • 25. It is about a long term Growth strategy It will be like running a marathon You will never be done It’s not about hacking It’s just good marketing
  • 26. If an experiment fails, you are still learning Inconclusive result is not learning
  • 27. 4
  • 28. Find the right people
  • 29. Mindset > Skills Driven by results, not tech Promote small experiments vs big projects Customer empathy Be colocated
  • 30. 5
  • 32. Test value before building Go 3rd Party After you hit the limitations, go homegrown
  • 33. Chat
  • 36. 6
  • 37. You need your own engineers
  • 38. Growth Product We have code ready we need to checkin We have this experiment we would like to run! PM Engineer Who are you? Let me see your code Great! Do you have resources to make it happen? PM Engineer Second attempt
  • 39. Why are you in my code base? Why do you get to take short cuts? We are already planning on doing this! That’s not how we work!
  • 41. 7
  • 42. A squad, not a team
  • 43.
  • 44. Engineers still part of the product team(s) But on a Tour of duty Easy org setup Engineers familiar with product codebase
  • 45. Squad composition PM PD EM EngineersDS
  • 46. Engineer Engineer Growth Product Hey Irina, can you review my PR? Sure, let me check!
  • 47. 8
  • 48. Don’t ideate, look for opportunities
  • 49. Go back to the data Focus on impact and ROI (ICE score) If successful, what’s the projected impact? Conversion, open rates, notifications are good candidates
  • 50. 9
  • 51. Then you will hit the ceiling (local maximum)
  • 52. Changes need to be made inside the product Don’t bring more water to a leaky bucket Improving Activation reduces CAC (Cost of Acquisition) Better Activation drives retention and ultimately conversion to paid When the right time, reinvest that money saved in Acquisition
  • 53. 10
  • 54. Now it’s time to own the entire funnel
  • 55. The Growth team now needs to focus on the entire funnel (ARRR) Acquisition, Retention, Referral, Revenue Split into mini-squads focused on each lever 90 day sprint focused on one metric to move
  • 56. 11
  • 58. Queries and funnels provide directions They don’t tell you the why Power is in quant + qual
  • 59. 12
  • 62. Experiment Pipeline (lever, hypothesis, ICE prioritization) Experiment Plan (control vs variant, baseline, metric)
  • 63. Experiment Pipeline (lever, hypothesis, ICE prioritization) Experiment Plan (control vs variant, baseline, metric) Backlog (sprint planning, link to experiment)
  • 64. Experiment Pipeline (lever, hypothesis, ICE prioritization) Experiment Plan (control vs variant, baseline, metric) Backlog (sprint planning, link to experiment) Ship Meeting (stakeholders signoff)
  • 65. Experiment Pipeline (lever, hypothesis, ICE prioritization) Experiment Plan (control vs variant, baseline, metric) Backlog (sprint planning, link to experiment) Ship Meeting (stakeholders signoff) Ship (soft launched)
  • 66. Experiment Pipeline (lever, hypothesis, ICE prioritization) Experiment Plan (control vs variant, baseline, metric) Backlog (sprint planning, link to experiment) Ship Meeting (stakeholders signoff) Ship (soft launched) Experiment Results Review (what did we learn)
  • 68. Background: We have a secret Facebook Group called Spark Insiders, we have had a challenge recruiting users easily and most importantly truly active users. Hypothesis: We believe that by elevating our most active users to Spark Insiders we can build an automated system to recruit users automatically. This will result in higher quality users with higher engagement on the FB group. We also believe that making these users Insiders will boost their return use and we can learn what are the traits of an engaged user. KPIs: # New Spark Insiders, Group activity/engagement iOS: Spark Insiders Recruitment
  • 69. iOS: Spark Insiders Recruitment
  • 70. Custom landing page for opt-in
  • 71. Custom landing page for opt-in
  • 73. Secret group with community of engaged users
  • 74. Engagement, before and after the recruitment campaign Before After
  • 75. 0.76 shares per user/day 0.97 shares per user/day 27% increase iOS: Spark Insiders - Impact on shares, last 60 days
  • 76. Understanding power users What features do they use in the first week? How often do they open the app? What time of day do they use the app and on which days? What sources were they acquired from? Was it an ad, a promotional email to the chain’s customer base, or some other place? What devices do they use? What is their demographic background, including age, income, and more? What other apps do they use?
  • 77. Emails collected used as Facebook Lookalikes Audiences A Lookalike Audience is a way to reach new people on FB who are likely to be interested in your business because they're similar to your best existing customers. Ads for acquisition can be created targeting audiences “similar” to the power users you collected from within the app. http://bit.ly/2shVlzf
  • 78. Background: Our current M1 retention is 25%, we have to learn from users not returning enough on what we can do better for them. Hypothesis: We believe that we can retarget lapsed users and ask them what we can improve to make Spark better for them. We tried to do that manually, we now want to automate this to see if we can get feedback automatically without any manual work. KPIs: # Surveys completed, Quality of feedback provided Spark Web: Lapsed User(Has not returned in the past 14 days)
  • 79. Spark Web: Lapsed User(Has not returned in the past 14 days)
  • 80. Top 1: Users are happy with the product, just not using the product at the cadence we expect. Top 2: More product features. Top 3: More control/flexibility/variety. Top 4: Performance issues. Top 5: Android. Spark Web: Lapsed User(Has not returned in the past 14 days) : Results
  • 81. “There are tons of platforms and sites out there vying for attention. How can you stay top of mind for me? Online events like chats, featuring more user content and alerting them, etc. Haven't used you only because I forgot about you.” Key quotes: Marketing
  • 82. “Is this free if I own the Adobe Suite? Is there an educator discount? Do you love people that need your awesome program to make the world a better place? Do you offer affiliate programs so that I can work the cost of it off by making more awesome videos and telling people to use the program because it is the best. Bottomline I like the product just unable to afford it right now. Thank you” Key quotes: Awareness
  • 83. Web: Lapsed User - Impact on shares, last 60 days 0.22 shares per user/day 0.22 shares per user/day No impact Control Treatment
  • 85. Start with organic retention It’s not about hacking Hire the right people Buy before build A squad, not a team Focus on one thing first Move to product changes Own the full funnel (ARRR) Data does not tell you why Have a process
  • 86. 2 great books I recommend
  • 87.
  • 88. Thank you! Follow me at @thibault_imbert for the slides.