What’s the right North Star Metric (NSM) for my new role at Signal Sciences? In this post I evaluate different candisdates and their pros and cons. One of the challenges with introducing a NSM at this stage in the company’s lifecycle (5 years in) is that some of the data that I would need to introduce a new metric would not be available back to the company’s origin.
Some candidate metrics (in broad categories) that I’m thinking through now:
- Revenue (or some variant like ARR/MRR) – one of the challenges with using revenue as the NSM is that for a sales-led GTM as is typical of B2B SaaS, revenue is a lagging indicator of product market fit, or even GTM Fit. And the longer the sales cycle, the longer the lag. This makes the feedback loop very challenging to manage as it becomes unnecessarily long and there are many variables that can drive revenue besides what’s going into the product.
- Pipeline – this is a leading indicator of revenue but I feel is more driven by marketing effectiveness and demand generation rather than the actual product, especially in a GTM model that is sales led. If the GTM motion is more product or even marketing led this seems like it would make sense as an area to optimize but seems a bit removed from anything I can influence as a PM other than to providing marketing with positioning/talk points.
- infrastructure Usage (i.e. traffic volume, requests, requests per second, hours)- Usage of a product can be a good metric, but for infrastructure centric products (like CDN or WAF), usage isn’t the best metric since I don’t really have much of an impact on overall usage consumption of my infrastructure. If, for example, Fox live streams the Super Bowl using my infrastructure, I’ll see a spike in usage. However, I have no control as a PM over when the Super Bowl occurs, how many people are going to watch it, or whether or not my customers buy licensing rights. Using Usage as a NSM seems a bit dangerously close to a vanity metric:
- Product Adoption – Product adoption (i.e. attach rate, penetration rate, # of net adds, etc) is a metric that I have used before as a NSM. The reason that I like adoption as a metric is because it is something that is broad enough to apply to multiple products, features, and categories. Also, it is often easy to measure since most features are behind user configurations or feature flags. Finally, it does feel a bit like a metric that product management can actually influence through optimization of on boarding flows, off boarding flows, ease of use improvements, and sales enablement.
- Consumer/User usage – (MAUs, DAUs, time on site) these metrics are essential for consumer properties or ad supported businesses where engagement with the product is directly linked to monetization or retention. However I struggle to connect this type of metric to Infrastructure products which are typically set it and forget it type of experiences, and where a good experience is where users don’t actually need to use any UX or Portal. That said, I suspect that increased user usage of these types of portals is actually a positive thing, since users are making it part of their workflow and process.
- Events/ milestones (signed up, turned on, turned off, first install, etc). These metrics seem to be helpful in that they are related to “aha!” Moments in a product. And the more of these a user experiences, the more likely they are to convert / less likely to churn. This is just a hypothesis, I don’t have any data to support this and it also requires a degree of maturity in data analytics capabilities such as event tracking that is closely linked to the product.
- Customer Churn – this feels like the right metric to measure retention for b2b SaaS. Typically I’ve seen this measured in number of customers or net adds/losses per month. However I was I introduced to a new concept recently of Revenue retention / churn which is the expected revenue that a customer will pay you in the next period as a percentage over the current period.