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aha moment. This is the moment that the utility of the product really clicks for the users; when the users really get the core value—what the product is for, why they need it, and what benefit they derive from using it. Or in other words, why that product is a “must-have.”
Can you identify an aha moment that users love?
Must-Have Survey begins with the question: 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) d) N/A—I no longer use it
Interpreting the results is simple enough; if 40 percent or more of responses are “very disappointed,” then the product has achieved sufficient must-have status, which means the green light to move full speed ahead gunning for growth.
a set of additional questions on the Must-Have Survey will help to point you toward your next steps: What would you likely use as an alternative to [name of product] if it were no longer available? I probably wouldn’t use an alternative I would use: What is the primary benefit that you have received from [name of product]? Have you recommended [name of product] to anyone? No Yes (Please explain how you described it) What type of person do you think would benefit most from [name of product]? How can we improve [name of product] to better meet your needs? Would it be okay if we followed up by
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You’re looking to get at least a few hundred responses to the first question to be a reliable guide for this kind of survey. If you don’t have a large enough base of beta users to get about that number, you should be relying more on customer interviews instead,
Somewhat ironically, it’s best if you target the survey at active users rather than those who have gone dormant.
One caveat: the Must-Have Survey isn’t recommended much beyond the stage of determining whether you’ve achieved core product value. For one thing, once your growth has taken off, it’s not a good idea to even suggest to your customer base that the product might be discontinued by asking them how they’d feel if it were no longer available.
once you have moved past this early diagnostic stage, your surveying and testing of the quality of the customer experience can and should become progressively more refined and your assessments more quantitative in nature,
MEASURING RETENTION The second measure to use in assessing whether or not you’ve achieved must-have status is your product’s retention rate,
most mobile apps, for example, retain just 10 percent of their audience after one month, while the best mobile apps retain more than 60 percent of their users one month after installation.
Sitting in an office with your smartest lieutenants and a whiteboard to hash out ideas for improvements may feel like exactly the right way to solve the problem, but trust us, that instinct is a head fake. It’s essential that you instead talk to users (on a deeper level than achieved through the aforementioned survey) to understand what the true objections and barriers are to your product’s success.
three key methods, all of which should be employed in concert. Additional customer surveying, including interviews and getting out in the marketplace to talk to customers and prospective customers; Efficient experimental testing of product changes and messaging; A deep plunge into analysis of your user data.
engineers know how to implement product changes and to set up experimental tests within the product;
data analysts know how to dig deeper into user behavior,
by focusing on a core group of users, it was able to deftly fine-tune the product with many ongoing improvements that its users loved.
four questions that the Twitter team asked users who had gone dormant and subsequently returned: (1) Can you tell us why you signed up in the first place?; (2) What didn’t work for you? Why’d you bail?; (3) What caused you to come back and try it again?; and (4) What worked this time?
focus groups to be largely ineffective and time consuming,
most growth teams have adopted the practice of a minimum viable test (MVT), the least costly experiment that can be run to adequately vet an idea.
changing the language from “Sign Up for Free Trial” to “See Plans and Pricing” netted 200 percent more sign-ups.26
Optimizely and Visual Website Optimizer
balance between big, moonshot bets and incremental improvements that lead to consistent growth.
event tracking. Most analytics platforms allow you to identify key events within your system
the more you test, the more data you have to analyze, and the more data you analyze, the more patterns are bound to emerge.
YouTube started as a video dating site, pivoting to be the home for all video online only once the founders saw that users weren’t only uploading video profiles to find dates, but rather sharing videos of all types.
Josh Elman and the team designed a whole new first-time-user experience aimed at getting people to follow 30 Twitter users as quickly as possible. They implemented a feature that made suggestions of people to follow a primary part of the sign-up process, making recommendations of specific accounts based on the interests that users chose while signing up, such as recommendations about celebrities and athletes they might be interested in.
the aha moment is so critical to building up a strong foundation for all further growth,
Facebook, Twitter, and Pinterest even treat these new user experiences as different products from their main product offering, and have dedicated teams of designers, product managers, engineers, and growth leads just to perfect this one user experience.
If they were truly practicing growth hacking, they would have run a test to determine whether or not their assumption was true.
Creating an aha moment and driving more people to it is the starting point for hacking growth. The next step is to determine your growth strategy.
what your growth levers are and whether they are the right ones to achieve desired results—before you move into high-tempo testing of growth ideas.
The first step in determining your growth strategy and figuring out where to focus is to understand which metrics matter most for your product’s growth.
what Johns dubbed a company’s fundamental growth equation. This is a simple formula that represents all of the key factors that will combine to drive your growth; in other words, your core set of growth levers.
For eBay the formula is: NUMBER OF SELLERS LISTING ITEMS × NUMBER OF LISTED ITEMS × NUMBER OF BUYERS × NUMBER OF SUCCESSFUL TRANSACTIONS = GROSS MERCHANDISE VOLUME GROWTH
Johns even created this equation for Amazon to illustrate the value of these formulas:6 VERTICAL EXPANSION × PRODUCT INVENTORY PER VERTICAL × TRAFFIC PER PRODUCT PAGE × CONVERSION TO PURCHASE × AVERAGE PURCHASE VALUE × REPEAT PURCHASE BEHAVIOR = REVENUE GROWTH
The way to determine your essential metrics is to identify the actions that correlate most directly to users experiencing the core value of your product,
Reducing the complexity of your business operations down to a basic formula is immensely helpful in allowing the growth team to focus on the right signals in this vast sea of data noise.
what’s commonly called in the growth hacking community the North Star metric.
one, key metric of ultimate success that all growth activity is geared toward.
Some in the growth community refer to this one key metric as the One Metric That Matters, while others call it the North Star.
to determine your growth equation and establish your North Star metric, of course the prerequisite is the ability to both gather data on customer behavior and measure product performance and the results of experiments.
“instrumentation.” Much as an airplane can’t fly without instruments providing information about altitude, air pressure, and wind speed constantly being monitored, without the right data at your fingertips, your growth team will be flying blind.
player—often you’ll be able to readily identify a glitch in how it’s working: what product designers refer to as a usability problem.
it’s so valuable to take the time to create reports that vividly illustrate your progress as it relates to your growth levers and your North Star metric—using what are generally referred to as dashboards.
constant visibility to the numbers the team is responsible for increases their ability to positively impact the metrics they’re responsible for significantly.
numerous tools to create sharp data visualizations, from simple tools designed for small start-ups such as Geckoboard and Klipfolio, to enterprise solutions such as Tableau and Qlik Sense and dozens more.
Too many reports are nothing more than “data puking,”
cohort analysis, which is dividing your customers or users into distinctive groups by a common trait.
correlation analysis,
