By Manash Bhattacharjee,
Director, Product Development and Innovation,
Mastercard

 

 

Who, what, where, when, why and how, are some of the important questions that product managers, developers and designers are constantly trying to answer.

As a product leader, using data optimally to make product design and development decisions is critical. I am on a constant quest to learn about things such as the best combination of quantitative and qualitative analysis, measuring the right things and using data as an integral part of the product development process.

What can we use data for?

  • To learn about user pain-points, behavior and needs
  • To understand product feature usage and patterns
  • To make efficient tradeoffs
  • To build relevant success metrics

In some digital organizations, the management or the product team focus on one specific metric that counts, for example:

  • Facebook mobile app – Metric: Daily Active Users/Monthly Active Users
  • Google Maps API – Metric: Volume of calls
  • PayPal – Metric: Checkout conversion ratio

‘One metric that matters is a great strategy when you are trying to drive the entire cross functional team towards a shared goal.

While I agree with the one metric approach, a single metric typically falls short of giving a complete picture. I usually employ a combination of metrics for measuring product success and iterating through features design and development. These include:

  1. Awareness
  2. Activation
  3. Usage
  4. Retention
  5. Monetization

Let say you are a Product Manager (PM) at Amazon Pharmacy and you recently launched the first version of the product in the market – then tracking awareness metrics is extremely crucial for the success of the product. Examples of awareness metrics are Page Visits, Bounce Rate, CTR etc.

Activation metrics can identify whether the user finds the product or feature proposition interesting, useful or exciting enough to set up a profile and/or complete one meaningful engagement. If your awareness metrics are high but activation metrics are low -> it can indicate that the user is not finding the initial proposition exciting enough and the PM should invest in improving the core feature of the product.

Similarly, Usage and Retention metrics helps the PM to measure the engagement levels of the productfeatures. Example – Uber has an average Driver partner retention rate of 70% in 6 months and 50% in 12 months. As a PM of Driver Experience, I would be focused on creating or enhancing features which improve the retention rate for driver partners. Some of other popular retention metrics are Daily Active Users(DAU) or Monthly Active Users(MAU).

Monetization is a key metric for  thePM. Facebook uses monetization metrics like Average Revenue per DAU/MAU. Sometimes, management wants to focus on a specific metric and as such, a short term/long term trade off may need to be made.

For example, suppose Instagram wants to increase Average Revenue per DAU/MAU by increasing the number of ads per page, thereby potentially reducing user engagement and DAU. In such a situation, a PM can use a series of A/B testing to identify the recommended approach.

In summary, a data driven approach can be really useful for answering questions like “Who is our user and what are they looking for?” “What should we build?” “How are they going to use it?” and “Where should we launch?” However it is important to find out which are the right metrics for your team! 

Manash is a product leader, mentor and speaker in the fintech environment. As Director of Digital Product and Innovation at Mastercard, Manash is focused on developing highly disruptive, rapidly evolving platforms, products and technologies, with the goal of bringing positive change to the world.

Manash brings in a global perspective having lived and worked in New York, London, India, Singapore and Manila. Manash has filed 30+ patents and loves to share his experiences through speaking engagements in digital product, design and innovation focused events.