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There’s a reason the second meaning of doing things ‘clinically’ refers to doing something with the utmost precision, efficiency, and detachment-medical practitioners are known to be precise. In the same vein, ascertaining your product’s health is the primary goal behind product analytics, and it’s one that should be done with the same precision, efficiency, and detachment as a surgeon.
From this perspective, product analytics can also be seen as a dual process: one that is diagnostic, helping you figure out what’s wrong with your product, and one of optimization: striving to make your product the best it can possibly be in line with your company’s goals.
So, what is product health?
Well, like your body’s health, it basically boils down to being an agglomeration of particular data points.
These data points can be seen as similar to the numbers you’d use to see if you were sick (such as your WBC count) or the ones you’d use to optimize your health for (for example, testosterone and thyroid levels).
Now, one crucial distinction in this analogy is the fact that while everyone would at least roughly define physical health the same way, for products, this is not true.
The relative importance of different metrics depends on the kind of product you are building, and as such, will be self-defined.
Furthermore, at a base level, every product focused company’s focus boils down to maximizing their product’s health, because their product’s health is essentially an indirect metric for their bottom line.
Clearly, then, product health is of paramount importance, which means metrics ought to be optimized towards this goal.
Data is plentiful-and increasingly so. The question is, how do you utilize it?
You need to be asking better questions.
As a rule of thumb, asking 5 why’s while solving a product problem works well. It may sound simple, but it’s crucial that you know your data hierarchies well enough to be able to do this.
Similar to how your doctor might operate when employing diagnostic tests, the framework you ought to follow when looking at metrics should be similar to Matryoshka Dolls-you know, those Russian ‘dolls in dolls’ you might have seen as a kid?
Ultimately, every result or metric has its own cause, which in turn, has its own. If you’re able to find the root cause of your problem using your focus metrics, great!
However, metrics are simultaneously both goals in themselves, and instruments that serve a higher goal.
So, more often than not though, you’ll likely need to do some deeper digging, for which you’ll need secondary and tertiary metrics.
Your focus metric might serve the purpose of the company’s success, and secondary metrics would serve the purpose of achieving the focus metric, and so on.
From top to bottom, here’s how that could look:
Just like doctors measure your body's vital signs such as blood pressure or heart rate, product managers often track primary metrics like conversion rates. This metric represents the proportion of visitors to your website or app who take a desired action, like signing up for a newsletter or making a purchase.
Now, let’s say your resting heart rate-or your churn, is a bit high.
Why?
If people aren’t sticking to your product, it’s reasonable to assume this is because they don’t like something about it-so ask them!
CSATs can provide insights into why customers are churning by just directly telling you if customers are happy or not.
This is a composite metric that provides insights into how actively and meaningfully users are interacting with your product or service. It is designed to go beyond binary metrics like daily or monthly active users and deliver a more nuanced understanding of user behaviour.
Gaining a granular understanding of how users interact with your product is a direct way to understand why and/or what they don’t like, which would then inform you of your churn.
This measures the extent to which your users are leveraging the different features of your product. The assumption here is that the more features a user engages with, the more value they are likely to derive from your product, leading to higher engagement. Improving feature adoption requires a thorough understanding of your users' needs, effective onboarding mechanisms, and continual and accessible education about new or underused features.
This measures the length of time a user spends interacting with your product during a single session. If users spend more time with your product, it typically means they are more engaged, which would lead to less churn.
This quantifies the percentage of customer issues or complaints resolved in the first interaction. A higher FCR generally leads to higher customer satisfaction as customers appreciate swift and efficient solutions to their problems.
NPS gauges the loyalty of your customer relationships by asking customers how likely they are to recommend your product or service to others. A higher NPS generally correlates with higher customer satisfaction. Improving NPS could involve addressing common customer complaints, improving product quality, or investing in excellent customer service.
In conclusion, product health analysis is much like a detailed medical check-up. It demands precision, efficiency, and a deep dive into layered metrics. It starts with a comprehensive look at primary metrics, akin to a physician checking vital signs. Then, it moves to the subtleties of secondary metrics, reflecting customer satisfaction and engagement, just like a specialist examining specific symptoms.
Finally, it digs into the granular aspects of tertiary metrics, like a thorough lab analysis that looks at the micro-level elements affecting the broader health picture.
The Matryoshka doll analogy is a great way to visualize this layered approach to metric analysis, with each doll representing a part of the in-depth, interconnected stages of product health analysis. All this meticulous evaluation is directed towards one goal: maximizing product health to ultimately bolster the company's bottom line.
This 'nested thinking' approach is one we recognize the value of-and have made a fundamental part of our product. Using our Magic Canvas, you can interact with your data in natural language, with a sequential flow mimicking how you might analyze data through layers.
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