clever, original, or inventive
will use software, databases, API’s, and related tools to grow a startup
must understand technology very deeply to be successful
is on the lookout for system weaknesses which will allow growth
growth should be on the mind of every person working in the company that wants to grow
If at least 40% of your existing users wouldn’t be “very disappointed” if your product disappeared then you don't have enough product-market fit. This basically means that your product doesn’t solve enough of a pain. It isn’t adequately loved by the users, and the team needs to focus on product more than growth.
Define actionable goals
Implement analytics to track your goal
Leverage your existing strengths
Execute the experiment, Write down your hypotheses before you execute an experiment, Do not be naive about the resources needed to run the experiment, Do not get discouraged by the initial results, Learn from success and failure
Optimise the experiment
Get visitors, The 3 P's of getting visitors, Pull them in, Push them in, Product
Retain users, 1. Identify trial users most likely to buy, 2. More rapidly onboard new users, 3. Identify and engage users at risk
eg. how many new subscriptions you sell per day, how many people cancelled their subscription per day
have a quick way of seeing it, share it within the c ompany
how many people are brought to your product because of the existing users
Is a portion of your users based on when they signed up.
Like cohorts, but instead of segmenting the users based on their signup date, you base the group on other segmenting factors.
Is multi-armed bandit testing is the best kind of A/B test? Bandit testing is a continuous form of A/B testing that always send people towardthe best performing options. In essence, the experiment never ends.
It’s important that you know the CAC for each channel because it can very greatly. Also, once you know the CAC per channel then you know how much you can spend on that channel, or if you should spend anything on that channel.
Segments come in handy when calculating LTV because you might discover that certain segments of your users have a much higher LTV than other users. This will also affect the CAC that you are willing to pay for those specific segments.
eg. Google Analytics
Can provide answers to: - Do people who use feature X have a higher LTV? - Do users in segment Y have higher engagement with feature Z? - And almost anything else you can dream of.
eg. KISSmetrics, Mixpanel
eg. for mobile apps