Why? 💭

While most companies/organizations have Analytics for their website and/or app, many do not use the data to inform their decisions. Our extensive experience with 100+ clients over 10 years running a web/app development agency, working in several startups and advising many more, is that people are not naturally data-driven. Even the larger enterprises with a data team do not pay enough attention to insights.

The most successful companies/organizations listen to the data and take action.

Note: we don't advocate dogmatically following data and ignoring what people say/think. We favour a blended approach that is guided by data and humans with empathy for the people using the product/service.

Who? 👥

This guide is for busy people who don't have time to read a whole book on Analytics; of which there are many good ones we have read and can recommend. 📚

Cui Bono? (Who Benefits?)

All members of the organization benefit from being aware of the Analytics because it gives realtime insight into the product usage.

A short list to guide your thinking:

1. Product Owners/Managers who need to understand how people are using their product/service.
2. Founders/Entrepreneurs seeking clarity on usage and friction points.
3. Executives needing stats to inform their decisions.
4. Developers/Engineers needing to know which features are used or not.
5. Customer Service needing to know where people are getting stuck when using the product/service.

What? 📊

A complete beginner's guide to Open Source Analytics that anyone can follow regardless of statistical knowledge or industry experience.

So What? 🤷‍♀️

Knowing exactly how people are using your digital product/service or website is the difference between success and failure. We cannot say definitively why 65% of businesses fail ... but we know one thing for sure: highly successful companies like Google, Amazon are hyper focussed on their Analytics. All the executives, product owners and builders know exactly which OKR they are focussing on and exclude everything else.

Counterpoint: we cannot rule-out confirmation or survivorship bias, meaning we may ignore companies that failed while being somewhat data-driven. e.g: the businesses that failed also listened to data, just the wrong data. 💭

When? ⏳

Get started today in less than 5 minutes by speed reading part 2 of this guide.
Then decide how far you want to take your Analytics journey.

How? 👩‍💻

This repo is divided into 3 parts:

1. Deploy: deploy Plausible Analytics "Community Edition" on your chosen infrastructure; in our case DigitalOcean.

   Note: the instructions use Docker so any competent DevOps person can deploy it in ~20mins on any Cloud provider (e.g: AWS, Azure, Google Cloud, etc.) by following the detailed step-by-step instructions.

2. Navigate: a guided tour of the Analytics interface to understand everything that we can learn from it.
3. Take Action: Sample actions that can be taken based on the insights we learn from our Analytics.

Any Plausible-related information can be found in the plausible folder, where you can get quickly get started on setting up a Plausible CE instance and a Next.js website, both on localhost. You'll also learn how to deploy Plausible to DigitalOcean!