A lot gets written about the importance of understanding the business context but rarely do I see anything written about how to do it. As I’ve progressed in my career, I’ve learned that getting up to speed quickly is essential to delivering value with data.
In this issue I will explain a simple framework I have developed and refined over time to get 80% of the way in 20% of the time.
The best part of this framework is its simplicity but don’t let that fool you. There’s a lot more nuance behind it. And the more you use it the better you will get at understanding the business context and the easier it will become to deliver value with data.
Interested? Let’s get to it.
By the way, if you want to get even deeper into the business context, I suggest you learn about the competitive landscape of your company. The best tool for that is the Wardley Map which I’ve written about in a previous issue you can find in the archives.
The framework consists of three pillars:
1. The business model and its unit economics
2. The demand side (aka why are customers searching, buying and using the product)
3. The supply / operations side (aka how the company fulfills this demand)
Business model and unit economics
The business model is usually the first thing you learn. It's already public information so you can learn this even before you join a company.
For example the key metrics of a SaaS business can be learned in a single (amazing) article by David Skok called SaaS Metrics 2.0. VCs are more than happy to teach this stuff because they want more businesses like that to invest in.
The business model describes how the company captures the value it creates or said more simply “how they make money.” There will likely be a lot of nuance in any one particularly business, such as “usage based pricing (e.g. buy 1000 credits for $100]” or “user based pricing (e.g. $30/user/month)” both of which fit into the SaaS model.
The unit economics will play a big role in determining the viability of the business so that’s another area to understand deeply. I’m not going to dive into it here because it’s a vast topic, but suffice it to say that calculating CAC and the payback period are the key metrics that power a SaaS business model.
Demand
This is where you want to understand things from the customer's point of view. What are they looking for? Why are they searching for products like the one your company provides?
I'll give you an example that's not SaaS related just to broaden your horizons a bit.
When I worked for TripAdvisor one of the most popular things we offered were tours / experiences. These ranged from a "2-hour Italian cooking class" to an expansive "3 day wine tour and tasting experience in southern France."
What's the demand here? Obviously when you're traveling you want to experience and do as many interesting things as possible. TripAdvisor relies on local suppliers to provide these experiences because its business model is a marketplace. So any product features that helps customers find and book interesting experiences from trusted suppliers will obviously do well.
One of the key questions to understand in this phase is whether demand is generated or simply captured and amplified.
Generating demand often means creating a new category/niche in the market or repositioning a product into another market, which is really difficult because it requires educating the market about your company’s value. This explains why so many software companies are stuck in finding product market fit.
And that’s why many companies choose the easier path of capturing existing demand. So when someone searches for something on Google (e.g. "what to do in Italy") they are likely highly motivated to buy.
It’s often the job of marketing to figure out how to capture this demand as cheaply as possible and for that they need high quality data. A lot of the questions here revolve around which channel is best for providing highest quality leads at the lowest possible price. Hence the opportunity for you as a data professional to create value.
Supply / Operations
Upon clicking on a paid ad or organic search result leads come to our landing page. How they proceed from here is incredibly important. The goal here is to improve customer experience and help them onboard into the product as smoothly as possible.
Defining what a good customer experience looks like is another huge the opportunity for you as a data professional to make massive impact. Here you can start to draw up the so called customers journey (I prefer to call it “the value chain” but that’s just me)
Being able to trace a customer through the "value factory," figuring out what potential bottlenecks get in the way and how to make the process smoother (and faster) is a high ROI data activity.
Do you want to know what tool I prefer to use for this? That's right.....a metrics tree :D Obviously not a super detailed tree, but enough to provide some guidance on what metrics are missing, what reports are most useful and so on.
Did you find this issue helpful? Should I elaborate more on this topic?
Let me know in the comments or by replying.
Until next time.
In our D2C analytics work, I've seen how connecting unit economics to customer behavior transforms decision-making. Your three pillars approach cuts through complexity and focuses on what matters. The demand generation vs. capture distinction is especially relevant for our clients who struggle to measure marketing effectiveness. Would love to see you expand on identifying which metrics truly deserve dashboard real estate versus vanity metrics.
This was exactly what I needed to read about.
I’m part way through a Masters in data science and projects always require “understanding the business context” and “proposing a business case” when working with a dataset and building ML models. That’s the part I find most challenging because it’s outside my context.
More please!