Why data analytics is an important part of Product Management?
We live in a world where data is so ubiquitous that the saying “data is the new oil” is accepted worldwide. Data has become a part & parcel of our lives and bread & butter for so many companies.
For those preparing for PM interviews, have you ever wondered why is it asked to estimate the ‘number of tires sold in a day’ in product interviews? Or to define the success metrics of a product? This is because data analytics is an integral part of product management and a candidate needs to fare better on such skills to become a good product manager.
PMs deal with data in different forms and levels on a daily basis. In the ocean of data, they need to fish for the relevant information and insights that can be used to make better decisions. Though data analytics is required at every stage, let's look at some of the important components of product lifecycle where good analytical skills come in handy:
- Defining the important metrics
Be it a small feature or a large full-fledged product, a Product Manager has to define the key metrics to measure its success. Common metrics such as ‘Daily active users’ or ‘Churn rate’ would have been coined by some product managers at some point in time to measure how their product was performing.
Similarly, when you are working on a product or a feature, you have to define the success metrics for the use-case you are catering to. To define these metrics, understanding metrics in general and the creation of metrics is important.
- Analyzing the key product KPIs
After defining the key metrics or KPIs for your product or feature, it is important to track them over the time period specified during metric formation. Tracking and storing the data of these metrics won’t be helpful unless a Product Manager understands how to interpret it and put it to good use.
The use cases vary from checking ‘where the users are dropping in the funnel’ to ‘identifying more opportunities of generating revenue.’ The data of the KPIs is a goldmine waiting to be discovered by a skilled PM.
- Evaluating A/B or multivariate test results
These tests are an integral part of product management as they help to make data-backed decisions and understand what works best for the product. The results of these tests might or might not be straight forward and the PM has to make sense out of the data. Data speaks volumes in such tests and provides information that might get overlooked in general cases. So, it is important not only to understand how to create these tests but to evaluate them as well.
- Designing an algorithm
I am not talking about writing the pseudo-code for an algorithm. But, to define an algorithm — be it the ‘surge pricing for Uber’ or ‘recommendation engine for Netflix’, a PM has to select the parameters that would be part of it. ‘Defining the correlations of the parameters’ or ‘assigning weights to the parameters’ are a few examples of the kind of work that is done. Having a good analytical bent of mind helps a PM in providing the right set of inputs and logic to the developers.
- Prioritizing the features
Yes, you can follow your hunch to select the features you want to design but I rely on data to help me with the prioritization. I use the RICE framework coined by the product managers at Intercom. Such frameworks not only require the ability to collect the right data but also use it to back your instincts, thus making a sound choice.
Thus, it can be said that Data is the one true friend of a product manager. It not only helps in the decision-making but makes it easier for the PM to convince the relevant stakeholders including partner teams, clients, or the senior management regarding the decision. So, instead of running away from it, you should embrace it. By doing so, you can count on the fact that data will help you make the right choices, influence without authority, and hence, make you a better product manager.
