Hacking Growth: How Today's Fastest-Growing Companies Drive Breakout Success

by Sean Ellis

be a remarkably reliable means of measuring whether customers love a product or not.

How disappointed would you be if this product no longer existed tomorrow? a) Very disappointed b) Somewhat disappointed c) Not disappointed (it really isn’t that useful)

The companies that grow the fastest are the ones that learn the fastest. The more experiments you run, the more you learn. It’s really that simple.

Each bit of learning acquired leads to better performance and better ideas to test, which leads to more wins,

Many of the leading growth teams regularly run 20 to 30 experiments a week, and some run many more. Early stage start-ups might be able to launch only one or two tests per week and then steadily build up to a higher volume, while later stage start-ups and large, established firms might well be able to start out with a much higher number.

THE GROWTH HACKING CYCLE Recall that the stages of the process are: data analysis and insight gathering, idea generation, experiment prioritization, running the experiments, and then returning to the analyze step to review results and decide next steps, in a continuous loop.

each turn through this cycle should be completed on a consistent interval, preferably in one or two weeks

The cycle is managed by a one-hour weekly growth team meeting to review results and agree on the next week’s ...

The growth lead should then ask the data analyst to share the results of the initial analysis done, and the growth lead should present the key growth levers, the North Star metric, and the area of focus or objectives for the team.

The team should then set the goal for the volume and tempo of experiments to launch each week; i.e., how many tests they think they can reasonably manage to design and implement.

the team will need to uncover what the aha moment is for people who are using the app,

and what it is about them and their usage that differs from those who don’t.

NUMBER OF INSTALLS × NUMBER OF MONTHLY ACTIVE USERS × NUMBER OF PURCHASERS × AVERAGE ORDER SIZE × REPEAT PURCHASE RATE = AMOUNT OF GROWTH

They determine that their North Star should be monthly revenue per shopper.

to build a large base of highly active shoppers who make substantial purchases on a regular basis.

With the aha moment in hand, the data tracking in place, the core metrics identified, and the team assembled, it’s time to kick off the high-tempo growth hacking process.

PLAYBOOK STARTS HERE

Finnair

Discovery Optimization

Simple way of phrasing the role of exp

Journey thinking

After data, conduct a survey to dig deeper on motivation of behaviors

Survey: pop up at behavior esp when indicating confusion

The one thing survey also attached to behavior

! Case data before survey Who didn’t use x who used service - Bucket of people by behavior

59% sign on with Google

The Learn flow

Loyalty game: Gabe Zuckerman Status Access Power Stuff

Triggers in news and notifications: keep a group outside them as control.

BJ Fog categories of triggers ***

Capitalize on the value of ‘stored value’

Retention: initial, medium and long term

Important about Retention

Problem with non retention onboard

Cohort and analyzing retention: what behavior can be related to retention, or higher revenue? THATS THE KEY QUESTION ***

Hook model: the continuous rewards for using the service

The how to of retention, cohort, rewards etc.

Rewards m: some of the most habit forming rewards are not tangible, but aesthetical, social and só in.

Incentive-Market Fit

BoomTrain: machine learning personalization

Promise upfront the upcoming development! Hook

No churn between announcement of coming soon and premiere

A development process of ongoing onboarding, where users go through a LEARNING CURVE

Ramp up After a feature is learned/used/mastered, you introduce a new one A table can be done for that FEATURE INTRODUCTION /FIRST USE /MASTERY Singing up / Did you know you can sign up? / Try now this / You rock! Now you can do this

Journey as first thing to do MONETIZATION FUNNE from now onwards

IMPORTANT FORMULA FOR CUST SEGMENTATION

Types for cohort

PRICE SURVEY, ultimately

Persona/Pricing Fit What customers are willing to pay for a certain price (range)?

********** VALUE METRIC