From Zero to $10M ARR: Crazy idea, need your feedback 🙂

Welcome back to issue #3 of the Zero to $10M ARR newsletter. Things are moving quickly at Helply, which gives me a lot to update you on. Today, we’ll be covering: - A public vote to help decide the future of my new company Helply - A mini lesson on how to think about Retention metrics - A quick masterclass on storytelling for SaaS founders ​ So, let’s get into it! Let’s have a vote (and raise the stakes…) What if I made EVERY aspect of building my new SaaS Helply open to the public, in real time. That means every slack channel, meeting, internal doc and dashboard completely open and viewable by the public 24/7. Would you want this? To cast your vote, head over to my LinkedIn post today. If I get 500 likes on [today's LinkedIn post]() - I’m doing it. Now, let’s dive into what this will actually mean… Everyone in B2B is running a Founder Led Marketing / build in public playbook (or is thinking about it). This means they’re sharing metrics, data and takeaways on an intermittent basis in a vulnerable and open way. But, I’m thinking of doing something a little different: Truly open sourcing the entire build in public journey for Helply. Truman show style. This means making every single thing that happens at Helply publicly accessible and archived in real time. What does this look like? - Every meeting recorded and posted - Every strategy and internal doc public and available as a template for you to use at your startup - All reporting dashboards viewable in real time - Same goes for: financials, slack channels, notion docs, etc. ​ Full, radical transparency. Everything out in the open. Every single thing that pertains to Helply becomes completely public, with real-time view access to anyone and everyone at all times. And, made completely available (and templated) for you to use at your own startup. Most importantly, it would be organized in a way that is easily searchable and value driven for other founders. This is taking building in public to another level. It would be the first time in history a startup has documented and publicly shared its entire journey in real time right from the start. The Goal: To provide blueprints and share the process of building a B2B SaaS startup with other B2B founders all over the world. From A to Z. If Bryan Johnson is the most measured man in history. I want to become the most publicly transparent startup ever created. Right now this is a thought (one I’m extremely serious about and actively considering). But the question becomes: Do you want this? Would you prefer the intermittent TL;DR where I share updates, data, financials and spare you the nitty gritty (imo, the day to day “nitty gritty’ is where all the juice is, but that’s just my opinion)? I’m happy to do either, my goal is to provide maximum value. What do you think? Is this worth during? Would you care to see it? Let me know in the comments of [this LinkedIn post](). If I get 500 likes I’m doing it. A mini lesson on how to think about Retention metrics Today I wanted to focus on one crucial aspect of choosing a retention metric I see so many companies fumbling. Frequency Analysis. Frequency is at the heart of setting a retention metric. If you get this right, congratulations you’re on the right track. If you get it wrong, it will silently kill your company. Let’s dive in. Why Frequency Matters Choosing the correct frequency for your retention metric aligns your measurement with how users naturally interact with your product. I want to emphasize the term “naturally”. Natural usage frequency is critical. If the frequency is set too high or too low, it can lead to misleading insights and wreck your engagement and retention strategies. In the simplest terms, a daily active user (DAU) metric might not make sense for a product that users typically interact with weekly. On the other hand, a monthly active user (MAU) metric could be too infrequent for an app meant to be used daily. Quick and Dirty How-To Guide for Frequency Analysis 1. Understand Natural Usage Patterns Before setting a retention metric, you need to have a hypothesis for how often your users naturally interact with your product. This involves: - For example, when Pinterest started they thought users might use the platform for browsing and pinning ideas for specific projects like home decor or event planning (a more intermittent usage). ​ It turned out, the vast majority of usage was on a weekly usage basis done by non-professionals for general discovery and inspiration. That’s a bit of a 180. ​ - Quantitative Data: Analyze usage data to identify patterns in relation to the core actions for you products. ​ For Pinterest as an example, they would look at frequency of key actions such as pinning, repinning, and viewing boards. This is the data that will validate (or invalidate) your qualitative insights. ​ 2. Hypothesize the Natural Frequency Based on this research, you will now create a hypothesis for the natural frequency of user interactions. ​ Going back to Pinterest as an example, you might hypothesize that the natural frequency of pinning activity is weekly because users often seek inspiration periodically rather than daily. 3. Validate with Quantitative Analysis Next, validate your hypothesis. How do we do that? I think histograms are a really powerful tool for data analysis relating to frequency. What is a Retention Histogram? A retention histogram is a graphical representation that shows how often users engage with a product over a specific period. ​ The Y-axis typically represents the number of users, while the X-axis shows the number of days they’ve been active within that period. By analyzing the distribution of user activity, you can identify patterns that inform your retention strategies. So, here’s what you do: - Create a Frequency Histogram: Plot a histogram showing how often users perform the core action (e.g., pinning) over a set period (e.g., 28 days). ​ The Y-axis should represent the number of users, while the X-axis shows the number of days they’ve been active. ​ - If you see a significant concentration of users towards the right end of the X-axis (e.g., 20-28 days), it indicates that a large number of users are active almost every day. ​ Minor peaks at intervals such as 7, 14, 21 days indicate that users are engaging periodically, perhaps weekly. ​ If the histogram shows peaks around the lower end of the X-axis (e.g., 1-5 days), it indicates that users typically engage only a few times a month. ​ 4. Adjust Based on Findings If your analysis reveals a discrepancy between your hypothesis and actual user behavior, adjust your metric accordingly (and be grateful you did this exercise). For example, if you hypothesized a weekly frequency but find users engaging more on a bi-weekly basis this is a big deal. Not only do you need to reconsider your retention metric to align with this behavior but you have to think about your entire engagement strategy (what could be more annoying than a product you use once a month sending you engagement emails twice a week). 5. Set the Retention Metric Once validated, set your retention metric based on the confirmed natural frequency. For Pinterest (again, because they’re an example we all know…), this might translate to measuring Weekly Active Pinners (WAP) – users who pin at least once a week. (P.S if you like this mini-lesson and want me to go deeper into setting core metrics for you startup, LMK. I think this is one of the most mission critical startup activities). Real-World Example: Pinterest’s Weekly Active Pinners By aligning their retention metric with the actual natural usage of their users (weekly active usage is the sweet spot for an engage Pinterest user), they can approach their user engagement in a way that is “just right”. This focus allows them to: - Optimize Features: Tailor features and content to encourage weekly engagement. - Personalize User Experience: Send targeted notifications and recommendations based on weekly interaction data. - Improve Long-term Retention: By understanding and catering to the natural rhythm of their users, Pinterest can implement strategies that keep users engaged over the long term. Final Thoughts Choosing the right retention metric is all about understanding of user behavior and natural usage frequency for your product. If you get usage frequency right, then you can align your metrics with natural usage patterns, which has a downstream impact on your retention and engagement strategy, i.e the most important aspects of running a company. Storytelling You might not be Stephen King, but storytelling is at the heart of everything we do as founders and operators. Whether it’s top of funnel marketing, activation flows, feature updates, pitching stakeholders, product management (or any other part of you company). You are telling a story. I found this awesome one-pager with every storytelling framework in existence in one place. Hopefully you find this as valuable as I did. ​ Until next time! Alex CEO & Founder, [Groove](=) & [Helply]()​ P.S. I’ll also be posting on LinkedIn seven days a week, 365 days a year. I’d love to hear your feedback on the new newsletter in the comments of my latest post. Check out [today's LinkedIn post]() where I'm holding a vote on whether or not to make every aspect of building Helply completely available (and templated) for the public viewing and usage by other founders. ​ Let me know what you think in the comments. I read and reply to every single one. ​ Don't want to hear from us? You can [unsubscribe here](.