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Growth hacking. Hypothesis testing. Feature prioritization framework

Ani Aghababyan

Product Manager at SFL

+ 8 years | web, mobile

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Growth hacking

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Definition

Growth hacking is all about using innovative techniques to dramatically increase your traffic, conversion rate and user base with minimal expenditure—and thus grow your company quickly

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Growth team

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Growth hacking

PRODUCT

GROWTH

MARKETING

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Growth hacking

Before

  • Single person
  • Engineer
  • Magic

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Now

  • Growth team
  • Variety of skills
  • System

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RUNNING A LOT OF EXPERIMENTS

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Timing

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Timing

Days left to run �experiments �215 days�=�43 weeks

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Intensity

1 hypothesis per week

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< 30% hypotheses being approved

= 13 growth hacks per year

= 2-5% growth

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Intensity

5 hypothesis per week

= 65 growth hacks per year

= 130% growth

< 30% hypotheses being approved

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Scientific approach. Hypothesis

A specific, testable prediction. �It describes, in concrete terms, what you expect will happen in a certain circumstance.

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Growth hypothesis�Action [X] will increase metrics [N] with [Y] value because of [Z].

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Where do the hypotheses come from

Case studies

Examples

Advises

Researches

Reports

Ideas

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Hypotheses

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Hypothesis generation

Action

On plan selection page change “Buy now” button color from red to green

Expected result

Payment page conversion will increase by 10%

Justification

Bottle neck, conversion rate increase on this layer will significantly affect our target metrics + I love color green

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ICE Score - Estimation of hypothesis

ICE Score

  • Impact
  • Confidence
  • Ease

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ICE Score

Impact�1 - 10

The potential gain from the feature, presented in terms of business/strategic objectives

�1 : No impact on revenue (or other target business metric)�2 – 5: Minimal impact on revenue (or other target business metric)�6 – 8: Definite impact on revenue (or other target business metric)�9 – 10: Significant impact on revenue (or other target business metric)

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ICE Score

Confidence�1 - 10

The level to which the team feels the feature is understood/scoped and possible to do�

1 – 3: High risk (many unknowns and little supporting evidence about the potential feature) �4 – 7: Medium Risk (good information is available, but the blueprint for execution is still unclear)�8 – 10: Low/mitigated risk (plenty of customer feedback and data points backing the feature)

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ICE Score

Ease�1 - 10��The degree of complexity involved in completing the feature (in simple terms – how long it will take to finish)��1 – 2: One month or more�3 -5 : One to two weeks�6 – 7: Less than a week�8 – 10: Less than one day

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Score and Prioritization

Score�I + C + E = XX (Sum of impact, confidence and ease)

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Team member

1

2

3

…

Impact

8

8

8

…

Confidence

7

5

8

…

Ease

2

7

8

…

Score

17

20

24

…

Priority

61

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Other frameworks

  • RICE scoring model
  • Weighted scoring
  • MoSCoW
  • Affinity grouping
  • Buy-a-Feature
  • Value vs. Complexity
  • DACI
  • Quality Function Deployment (QFD)
  • Kano model
  • etc...

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Iconic Growth Hacking Examples

  • Every time a friend whom you invited to Dropbox created an account, �both you and your friend would get 250mb of extra space. Cost 500mb.
  • Paypal paid users to sign up. Every time a friend you referred �created an account, both you and your friend would received $10.
  • Airbnb. By improving the photos of listing on their site, they dramatically�increased the number of bookings. Reverse engineering API to cross �post Craigslist
  • YouTube implemented the ability for users to easily embed videos �wherever they wanted.

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Thank you!

Ani Aghababyan

ani.aghababyan@sflpro.com

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