In part one, we discussed what Growth Hacking is all about – the process. Growth hacking is a novel approach, a novel way of thinking, and a novel concept. It is predicated on anticipating mistakes, expanding on them progressively, and constantly redefining the possibilities. And all at a furious rate. Doesn’t that sound like a lot of uncertainty? Yes, it is!
Fortunately, Sean Elis, Ryan Holiday, Neil Patel, and a number of other growth gurus have considered how growth hacking as a method might be taught to others and established several must-haves that I’d like to share with you in this chapter.
Growth hacking is teamwork
Growth hacking is unquestionably a collaborative effort. Of course, someone must always bring in the method and keep an eye on the growth process. However, doing it alone makes you appear quite ancient. Growth hacking breaks down functional team borders by bringing together people and ideas from all current teams and attempting to execute them. These „cross-functional teams“ not only tear down visible departmental boundaries, but also mental barriers and blockades. Because this is the only way to combine the different participants‘ abilities and knowledge and create new ways.
Data drives growth hacking.
Growth hacking teams are frequently tasked with delivering quick and measurable results. However, before results can be delivered, a method, or hypothesis, must be developed and tested. Which data is relevant is determined by the data that is currently available. Web stores will contain different data than mobile apps.
It also depends on how much attention has been taken to track user data thoroughly (and, of course, in compliance with the DSGVO). The more specific interactions that have previously been tracked, the more mature and comprehensive the working hypotheses will be.
It’s critical to remember that every assumption is just that: an ASSUMPTION! Only what can be confirmed with validated data is trustworthy, and thus safe.
Data can be generated in both qualitative and quantitative methods. A modest, yet very early start-up, for example, will not be able to conduct a comprehensive A/B test (only makes sense from approx. 1000 users). Nonetheless, a young startup can do verbal interviews with its first 10 or 20 users. Whether via phone or questionnaire.
Growth hacking requires testing and evaluating ideas.
Markets have never been more competitive than they are today. Startups‘ life cycles are measured in months rather than years. Being quick is consequently essential for growth hacking. Ideas must be generated as soon as they are tested. Prototyping is the crucial word in this situation.
The goal is to quickly construct a workable prototype of the intended improvement, link it to quantifiable characteristics (metrics), and bring it live.
After the previously set time period (often 1-2 weeks), the success or failure of the prototype is evaluated using metrics data, and a decision is made as to whether the prototype should be followed up on, changed, or abandoned. Data-driven.
Growth hacking necessitates collaboration and creative thinking. The concepts must be validated using metrics data. To collect data, tests are put up that run for a predetermined amount of time. The precise form or scope of the tests is determined by the capabilities of the respective company.