How I realize the importance of a data stack as a founder

Hello makers,

As indie makers, we often focus on building products and marketing, only to forget the key to growth: data.

In this post, I will share with you how I realize the importance of a data stack for my product and why a non-tech maker should know it.

Context

In my previous start-up Habitify and Summerian, I had a hard time growing it after it has reached $21k/month. Later on, I found out that I only had a piece-meal marketing strategy based mostly on my intuition. I had tons of data stored in Firebase, but I never knew how to utilize them. Lol.

So I searched around and found this book that helps a non-tech maker like me understand the complex world of data analytics. Most importantly, it helps me understand which tool is most suitable for my startup to employ, and how to build a simple analytics stack for both me and my marketing experts to use.

The Analytics Setup Guidebook by Holistics | Business Intelligence and Data Infrastructure

Getting started with a simple data analytics stack

What I love about this book is that it's really well-written (with humor!) AND it's free. Yup. I found it launched on Product Hunt so probably it's some kind of exclusive community offer.

Second, it is comprehensive and easy-to-understand for no-code people like me. It looks like the team behind this book develops a business intelligence tool so they share a lot of insights and best practices in the market. (check out Chapter 3 where they mentioned a real-life case of the business-analyst conflict!)

Knowing the big picture and its components help me decide what's the best fit for my startup's budget and infrastructure. Previously, I found it really hard to decide which tools or processes to use, simply because there are too many of them on the internet.

From the book, I realized that there are tools that can centralize all my data (landing page, in-app, ads...) into one place and combine them all together to have a general view of my user's journey. To do that I have to start setting up data warehouse, using the ETL process (gotta ask my engineer co-founder on this), so on, and so on...

My basic stack looks very simple: It has a data source, a simple data warehouse (pay on the go), and a tool to help me query and mix-and-match the data to build reports.

Early results

And so I tested and landed on Holistics to centralize and monitor data for my product dbdocs.

By combining the landing page visits (tracked with Snowplow) and the actual registration count (tracked in-app), I can see how changes on the landing page can affect the registration rate and retention.

For example, after I interviewed my early adopters, I realized many of them were concerned about the app's privacy. So I implemented password protection feature and promoted it on the front landing page.

To my surprise, the conversion rate has gone up significantly from 1.38% to 2.83%!

Above is a very simple example of how a data stack can help me. If you'd like to know more about how I set it up, you can drop me a line at @Alanng!

Conclusion

To sum it up, as a maker, understanding the whole data analytics stack of your product is vital. The main reason is that building and maintaining such stack will take effort and money, and if you find out it doesn't work for you, you'll have to do everything from scratch.

So it is important for us to choose a data stack that is simple enough (by that I mean "cheap") but is scalable so in the future we can reuse it.

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Note: The downside of this book is that it is not written for a complete newbie to the industry. Since I'm not from a tech background, it took me quite a while to research all the terms and definitions. But overall I think it's worth it!