How do you come up with time-frames for when growth strategies should be executed?
Is it always “move fastest,” or do you plan around certain timely events? I can imagine how much this decision-making changes per project. So maybe tell us of a project you can explain in detail, of how you chose a special time ‘when to execute’ and why :)
I’m talking about the specifics of a growth strategy too, as most have a bunch of moving parts. How do you pin-point the best times for each part to go into motion? @DesignerDarius
I’m going to focus on tech-enabled Startups with my response. A lot gets written about how to execute specific growth tactics. Generally they are in the form of paid and unpaid campaigns or on-page or in-app optimization of an activity. What I don’t think gets enough attention is how to consider bigger picture implications before defining time-frames for what, when, how, to execute and why. Forgetting the bigger picture can have you ‘moving fast’ in a direction (aka busy) or worse, ‘moving slow’ or not at all in the right direction at the right time. If you want to just look at time-frames, skip to ‘Agile Growth Hacking’ below.
I start with a general mental model when thinking about growth that has three characteristics, then adapt from there:
1) Balancing financial and human resources available to do Growth experiments with founders even investors desires to achieve milestones, metrics, and validating hypothesis key to growing the business.
- I recommend a Lean Startup filter to chart 90-day planning cycles.
- Investors, if you have them, like metrics.
2) Aligning Growth strategies with your Product Development team
- The Right Way To Ship Software
- It’s a ‘Marketing Problem”, The product is actually good — The Engineer’s Myth
3) Agile Growth Hacking
- Weekly Sprints: 1–2 stand-ups and a rhythm of getting things done.
For example: Thursday planning for following week; Friday reporting; Monday standup for the week.
- Weekly reporting of new experiments that includes leading indicators like bounce rates and funnel metrics*
- Paid Experiments: between 2 days — 2 weeks
- Non-paid Experiments: between 2 weeks — 8 weeks
- On-page or in-App: between 1 week and up to 12 months if you’re doing cohort analysis.
- When an experiment is validated it gets moved into an ongoing activity until its usefulness has expired (ex. milestone achieved) or is trumped by a better tactic.
* search ‘marketing funnel metrics’ on Google
Any comments and suggestions welcome.
as first seen on https://wiselike.com/mitchell-posada/
follow me at @mposada and likely see some of the experiments in action