In late February this year, we collaborated with NUS Statistics and Data Science Society to host a workshop on Product Analytics. During the workshop, we covered the what’s, why’s and how’s of analytics, and the basics of Tableau. This is what went down.

1. Introduction to Product Analytics

We started off by introducing the participants to the basics of product management and analytics. Concepts such as the product life cycle, optimisation and user experience were introduced. With case studies such as Spotify and Netflix, we also briefly explained how these companies made use of product analytics to improve and grow their businesses.

2. Key Concepts and Techniques

Next, we went over some key techniques used in the analytics process, with a particular focus on the AARRR framework - acquisition, activation, retention, revenue and referral. Also covered in PM1101E, these “pirate or funnel metrics” are widely used by PMs, marketers and growth hackers alike to further understand the user behaviour of digital products such as mobile and web applications.

With regards to the AARRR framework, we also discussed some real life case studies like Grammarly, so that participants can better understand the significance of product analytics. For instance, Grammarly uses trends, badges and personal records to engage their existing customers, ensuring the retention metric is well-met when it comes to using the AARRR funnel to optimise their user base and sales.

3. Overview of Analytics Tools

We then introduced the participants to some of the most common industry tools for analytics, such as Python, Google Analytics and Power BI.

In terms of programming languages, many product managers and analysts alike would stick to your typical tools including R, Python and SQL. The latter two in particular, are widely used in the industry, with Python offering easy-to-use libraries such as Pandas for data wrangling, as well as Matplotlib or Seaborn for data visualisation. SQL is also an essential tool for querying a database, especially when required to filter out the data you need for your use case from a huge data lake or data warehouse.

External tools such as Google Analytics and Mixpanel are also used widely as no-code complementary tools, especially when there is a need to track specific metrics such as page views and individual user tracking.

4. Creating Dashboards with Tableau

Lastly, we had a hands-on session with Tableau. Following the live demo by Nicholas from SDS and with the facilitation of Henry and Harry from Product Club, our participants made use of the provided dataset to create a dashboard.

In addition, as part of the analytics process, they learnt to plot various visualisations - including treemaps and area charts as an alternative to your typical bar and line charts, geographical maps as well as cohort analysis to measure the gradual retention rates by specific time periods - either by quarter or month. Below is the end result of the dashboard created consisting of the above-mentioned charts after compiling them together!

Conclusion

Thank you to NUS SDS for working with us to conduct this product analytics workshop! If you’re keen to attend future workshops and events on product management-related topics, be sure to keep up with our Instagram and Telegram channel. 

Enjoyed our article on our Product Analytics with Tableau workshop? As an extension of this workshop, NUS SDS will be organising a similar Streamlit for Prototyping workshop - led by Harry. Sign up here today: bit.ly/nussds-streamlit

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