How do you track the impact of your startup experiments?

Think of it like a science experiment. Start with a hypothesis or an outcome you would like to validate. The challenge I see a lot of technical founders do is they just look at numeric data but not qualitative data.. and don't talk to real people behind the clicks. Causation and correlation are important to differentiate. Doing blind user testing, asking customer or little things like making a customer use your product in-front of you can show you where the blindspots in your understanding and results might vary. What are their assumptions, what language do they use, etc. Building a good product is empathy and active listening. This includes talking to real people and not just hiding behind a screen and measuring clicks in a silo.

- Run several experiments with low budgets on couple marketing channels - have metrics defined to measure success ROAS, CAC/LTV, Payback period - When you found profitable channels -> double down and scale fast

Here is an example of minimal viable product MVP goals that can be tracked in an experiment: 1. User Acquisition: The goal could be to acquire a specific number of users within a given time frame. 2. User Retention: The goal could be to test the engagement and retention of users with the product. 3. Market Fit: The goal could be to determine if the product meets the needs of the target audience. 4. Revenue Generation: The goal could be to test the potential revenue streams for the product. 5. Scalability: The goal could be to test the scalability of the product, especially if it involves technology or infrastructure. It's essential to define clear, measurable goals to evaluate the viability and potential success of the product.

The primary purpose of startup experiments is to validate your assumptions about your product, market, and customers. By tracking the impact of these experiments, you gather crucial data to guide your decision-making and avoid squandering resources on ineffective tests. Instead, focus on those generating the most value, thereby maximizing returns on your experimental investments.

One thing I found helpful is agreeing on which channels to experiment with and track from the get-go. Instead of running multiple marketing campaigns in 6 different channels, pick the top 3 where your audience is hanging and start experiment. Double down on those that are most successful.

Often times we jump to conclusions on what we think we should be measuring as it pertains to engagement. We do not dig deeper to identify the actual problems that are potentially causing other problems and how to solve for the root causes and measure success around those solutions. When it comes to measuring success and defining what you want to track here is a method I use: 1) first identify what’s going wrong before you choose what’s going right. 2)significant drop off on website 3 Then ask questions on why that could be going wrong. Is the load time too long? Is the interface distracting? Is a pop up interfering? These are just examples 4) figure out your goals (decrease load time) and then figure out what your tracking

What you track depends on your experiment design, hypothesis, and key metrics. Having a precise hypothesis allows for measurable outcomes. For example, if testing a new feature, a hypothesis could be, "Introducing feature X will boost user engagement by Y%". Here, user engagement becomes your key metric, quantifiable through various metrics like time spent on site or retention rates.

Em uma startup focar no que realmente importa e ter cuidado para não se apegar a métricas de ego, que podem te levar ao precipício exemplo X usuarios e 0 receita por simples ego. No livro startup enxuta ( Lean startup ) capitulo 7 tem um bom texto sobre isso. O que mensurar e pautar todas as hipóteses com isso.

Eu gosto muito de usar uma base propia em SQL para construir dashboards utilizando a ferramenta metabase. Ele é um bom data view e grátis, da para construir bons gráficos, analises.

Choosing the right tracking tools and methods is critical. Platforms like Google Analytics, Mixpanel, or Optimizely provide invaluable data collection and visualization capabilities. Depending on the experiment, different tools might be needed to track varying aspects, such as web traffic or user behavior. Consider how to monitor your control group - those not exposed to the experiment - to establish the relative impact and statistical significance of your test.

The timing of your tracking can significantly affect your data's reliability. Monitor your impact before, during, and after your experiment to capture any changes attributable to your intervention. Establish a reference point by tracking your control group before the experiment. Record your impact throughout the experiment and continue monitoring for a sufficient period afterward to detect any long-term effects.

An experiment not learned from is as good as one never done. The most efficient way to think of your startup decisions is as chess moves - one step after the other, but always in the context of the bigger picture. Without reflecting on past decisions, your next step is as good as a random throw in the dark. To learn and improve means to improve your chances of a bullseye - which is the end goal.

Check out how UBER tested its MVP Uber was born out of difficulty finding taxis the traditional way. And that was the problem Uber was focused on in the first stage. UberCabs was an iPhone app meant to connect the users with cab drivers and offer them a way to make payments via a credit card. The MVP version offered only these features on an initial level. The fancier features were added only after the app received significant downloads signalling its validation. At first, they handled all the orders manually. Uber offered drivers to accept and manage orders on their own. Only 3 out of 10 agreed, giving a start to the no. 1 taxi app in the world. Uber started with just 3 cars and validated its idea before its competitors could.