Main illustration: Michelle Kondrich
The SaaS industry is full of advice on the perfect product metrics to gauge your users’ activation, engagement, and interactions. But how do these concepts translate into real product improvements?
If you don’t know exactly what questions you want answered, these product metrics leave you blinded by the very data you hoped would open your eyes. So how do you go from simply plugging your numbers into cookie cutter formulas to answering key questions about your product and business, and providing data that can inform product team decisions?
What are product metrics?
Product metrics are data measurements that businesses use to evaluate the success of a product and determine how customers are engaging with it. Popular metrics like churn rate and conversion rate inform product strategy and help various company stakeholders to understand a product’s value.
Not all products are the same
In the early days of the analytics team at Intercom, our tracking mostly consisted of typical SaaS company finance metrics, such as the conversion rate of our customers from trial to paid, and monthly recurring revenue. These metrics, and others like customer acquisition cost (CAC) and customer lifetime value (LTV) are essential to understanding the overall health of a business and its strategy.
“Without additional metrics focusing on user experience, the analytics team will miss out on crucial inputs to decisions the product team is making”
But they fall short for product teams because they don’t answer all of the questions that are important to them, like, “Do customers find this feature valuable?” or “How simple is our product to use?” Without additional metrics focusing on user experience, the analytics team will miss out on crucial inputs that the product team can use to inform their decisions.
We started by reading about what other companies had done. We found that a lot of the “best practice” advice on product analytics was useful for introducing concepts, but that the rules weren’t uniformly applicable. So much depends on the type of business you’re running. A $99 B2B SaaS app will define engagement very differently to an e-commerce website.
The right product metrics start with the right questions
A central value of the analytics team at Intercom is “start with the right question.” If we just applied well-worn frameworks blindly, we would be starting with someone else’s question, not our own. And if the metrics these frameworks produce don’t start with the right question, they don’t influence how a product is built or the direction a business takes.
These metrics become false proxies that might look good on paper, but provide a false roadmap and won’t give you real insight on where to take the product next or what to improve upon.
“Most people use analytics the way a drunk uses a lamppost, for support rather than illumination.”
– David Ogilvy, founder and advertising executive
What’s more, a single set of metrics to serve an entire company becomes less and less effective as the company grows in size. Teams tend to diverge in terms of the metrics they care about. Although they all may share a common high-level mission, they contribute to the mission in different ways and so their success must be measured differently. The growth team is focused on engagement in one part of the product; the marketing team on an entirely different part.
Finding the right questions takes collaboration
There are numerous useful metrics that analytics team can provide to product managers so they can more effectively measure product success. Here are a few examples of engagement metrics used by SaaS companies:
- Average time to convert a user from a trial to a paying user
- Percentage of users who used a certain product feature
- Average number of times a user performed a key action
- Average number of key actions taken per session
Where most teams trip up is they don’t know how to find the right questions to answer with their metrics. Deciding on those questions requires a collaborative partnership between analyst and product development team, instead of the more traditional stakeholder-resource relationship.
To guide this partnership, we took inspiration from Google’s HEART framework, which gives advice on defining product metrics that follow from product goals (Happiness, Engagement, Adoption, Retention, Task Success). For example, here’s a few questions we ask our product teams to help us understand their goals so that we can help them define meaningful metrics:
- If we imagine an ideal customer who is getting value from our product, what actions are they taking?
- What are the individual steps a user needs to take within our product to achieve a goal?
- Is this feature designed to solve a problem that all our users have, or just a subset of our users?
3 touchpoints to determine your metrics
To help our product partners answer these questions, we use product usage concepts that, over time, have become well understood and agreed upon throughout the company. These terms can be directly related to key points during a customer’s journey within our product:
- Intent to use: The action or actions customers take that tell us definitively they intend to use the product or feature.
- Activation: The point at which a customer first derives real value from the product or feature.
- Engagement: The extent to which a customer continues to gain value from the product or feature (how much, how often, over how long a period of time).
Armed with these simple concepts, we can look to answer questions like the ones posed above. The next step is to look for signals that are specific to the product or feature which map to these concepts. We have found that open collaboration with people across our product teams – managers, designers, researchers, and engineers – yields many useful signals that we can use to develop impactful product success metrics.
As long as you’re asking the right questions, you’re going to get valuable insights that you can act upon. In short, start with the problem, not with the data.
Developing your key product metrics
Much like our philosophy of “ship to learn”, defining product success metrics is just the beginning. To ensure their success, they need to be advocated for, communicated, and even critiqued by a range of teams. Just like the metric measures the success of the product, we must measure the success of the metric.
Are the metrics giving us a true picture of product success? Are they influencing how we think about the product? Are they motivating the team who build the product? These are questions an analyst must constantly ask of the metrics they produce, advocate for, and report on.
“Using a common, consistent way of working means anyone in the organization can easily understand any product metrics, what they mean and why they are important”
This collaborative approach to metrics definition has led to a much more seamless relationship between analytics and product teams. Equally, using a common, consistent way of working means anyone in the organization can easily understand any product metrics, what they mean and why they are important. This means changes to the product can be informed – or even led by – the insights gained from exploring the data framed by these metrics.
Example: How data informed the design of our Articles feature
A great example of this is our knowledge base product, Articles. Our analytics and product teams partnered on this project from idea right through to launch. Metrics were decided upon at the start, using the simple, well-understood questions-first approach outlined above. This meant that early betas of the product already had a robust set of metrics in place for us to test our assumptions.
We hypothesized that it was important to understand how long it took a customer to get from actively creating articles (showing intent to use) to getting their customers’ eyes on those articles (activating) efficiently. If customers could see the value from the product quickly, they would be more compelled to convert from a trial to a paying user and less likely to churn.
Beta customers’ interactions with early versions of the product indicated it was taking customers a long time to reach this point. As a result, the product was simplified to allow for a more seamless experience. At launch we could see improved time-to-activation across the board, and this had a positive effect on overall conversion of customers from trial to paying.
We are continuing to explore and iterate on data-informed design process. While we’re always looking for areas to improve, starting with a partnership-based approach has put us on a good trajectory towards becoming a truly data-informed organization.