There’s two divergent approaches to learning, sometimes referred to as top-down or bottom-up.
Top-down begins with broad context, such as the history of programming languages, the advent of objects and classes, and eventually zoom in to modern languages, syntax and functions.
Bottom-up works inversely; maybe you’re given a Python sandbox, a hello world, and a cheat sheet. You explore what you can do and build on those low-level operators to create foundations that frame wider context.
Effective learning is always a mix of both. Everyone learns somewhere on the spectrum, maybe bouncing around depending on motivation and experience.
With respect to your goal of learning marketing, you’ve identified your path of study: strategy. And just like your SaaS product was created out of an understanding of functions and needs in a space, so too should your research explore the functions available to your business and how they’ll affect your pipeline.
Simplified: you have a tool, and since you have revenue, you know people want it. Your goal is to find questions to ask and answer them, and use that feedback to make decisions regarding your marketing strategy.
-What is my biggest source of traffic to my site? Where do they spend the most time? What causes them to click buy? How can I use these data to optimize my funnel?
-What is my total marketing cost divided by the number of paying users (CPA)? What’s their expected lifetime value? What do other products in my space cost?
-What do my customers and affiliates describe my product as? What problems disappear once they subscribe? How does this translate to value for decision makers? Do my enterprise sales represent 80% of MRR or 10%? Should I use multiple approaches to entities of different scale?
The questions you’re asking are the right ones. And like any other field, they multiply and become more specific - but if you bear in mind the top-down goal (spend less, charge more) of a business, things will crystallize quickly.
Top-down begins with broad context, such as the history of programming languages, the advent of objects and classes, and eventually zoom in to modern languages, syntax and functions.
Bottom-up works inversely; maybe you’re given a Python sandbox, a hello world, and a cheat sheet. You explore what you can do and build on those low-level operators to create foundations that frame wider context.
Effective learning is always a mix of both. Everyone learns somewhere on the spectrum, maybe bouncing around depending on motivation and experience.
With respect to your goal of learning marketing, you’ve identified your path of study: strategy. And just like your SaaS product was created out of an understanding of functions and needs in a space, so too should your research explore the functions available to your business and how they’ll affect your pipeline.
Simplified: you have a tool, and since you have revenue, you know people want it. Your goal is to find questions to ask and answer them, and use that feedback to make decisions regarding your marketing strategy.
-What is my biggest source of traffic to my site? Where do they spend the most time? What causes them to click buy? How can I use these data to optimize my funnel?
-What is my total marketing cost divided by the number of paying users (CPA)? What’s their expected lifetime value? What do other products in my space cost?
-What do my customers and affiliates describe my product as? What problems disappear once they subscribe? How does this translate to value for decision makers? Do my enterprise sales represent 80% of MRR or 10%? Should I use multiple approaches to entities of different scale?
The questions you’re asking are the right ones. And like any other field, they multiply and become more specific - but if you bear in mind the top-down goal (spend less, charge more) of a business, things will crystallize quickly.