Before working as a product Data Analyst the majority of my data roles supported marketing. You may think supporting product is the same as any other division but I can tell you from experience that’s not the case. Today I’d like to discuss my experience transitioning to a product data analyst role and how working with product made me a better data analyst.
Learning curve
Unlike universal marketing concepts such as SEO and SEM, each product is different and has a learning curve for you to understand what the product is for and how users interact with it. Allocate extra time you’ll need for onboarding to learn user flows and the systems used for event tracking. When I started as a product analyst, it took weeks to learn how to effectively use the product analytics software and figure out the event names that mapped to each screen in the user flows.
User experience
Supporting product gives you an opportunity to try it before applying for the position. Having an interest in the product or industry will help you be a more effective analyst because you can relate to the user experience and account for that in your analysis. I’m currently a product analyst for a mobile app and before my interview I downloaded the app to try. This allowed me to suggest improvements during my interview to show my proactive research into the company’s product beforehand. As a product analyst, I can use the app myself to evaluate new features and relate to the user experience.
Mobile versus web
Even if you’ve been a product analyst before there are additional considerations for analysis when you support a mobile product.
- Platform – When we think of mobile it’s just a phone. However, from a product standpoint, a company develops an app for an Android or iOS platform. My company has Alexa skills for the app that becomes another platform for analysis. An app can also be developed for tablets like the iPad that is yet one more platform. Features can be developed on one platform first to assess user feedback. As a product analyst, I have to remember the features by platform because it may impact my analysis. Platform and feature availability are just a couple of items you need to think about in a product analysis.
- Constraints -A website change just needs to be deployed by engineers after the code has been approved. Mobile developers have to work on a release schedule and all changes must be done by a specified date to be submitted to Apple and Google for review before updates are available in the app store. A bug fix can’t be updated as quickly for an app as it can be for a website. Account for delays if you’re waiting for a code change to be deployed and prioritize your tasks accordingly.
Data volume
User interactions with a product generate large volumes of event data. Imagine every open, click, and input you make on your phone or web page getting recorded and saved. As a product analyst, I’m often asked what feature usage impacts user retention and premium conversion. I have to make my queries as efficient as possible because I’m querying from billions of rows of data and every mistake I make means more time I lose before finishing my analysis. Learn about query optimization to leverage indexes or partitions and estimate more time to complete your analysis when pulling from a large volume of data.
A/B Testing
A common KPI for product is user retention and A/B tests are often used for product changes to measure impact. A product can have many teams supporting different parts of the product all running experiments at the same time. Be aware of other experiments running that can impact your experiment KPIs and expect a large portion of your time to be involved with product experimentation and measurement. Make sure you understand A/B testing concepts before becoming a product analyst because it’s very likely you’ll be involved with evaluating tests.
Segments
KPIs normally vary by the division and business you support. Conversely, product have common KPIs that don’t change across companies such as user retention, daily active users, and churn rates. A product goal can be to drive user retention with new features or product enhancements. I never used to look at KPIs by user segment but it’s common in product analytics to segment users by new versus existing to assess if a product change impacts behavior for all users or just a single segment. If only a single segment is impacted product managers may test further enhancements to see if retention improves. A product analyst is involved in these discussions and evaluates these subsequent experiments to measure the user retention impact.
Funnel analysis
It’s common to look at product interactions with a funnel analysis. For example, a sign up funnel can have multiple screens prompting a person for information before account sign-up is complete. More screens increase friction and can cause account completion rates to drop. In each step of the funnel we evaluate the drop-off rate to identify if any part of the funnel needs improvement. Prior to supporting product I never needed to run a funnel analysis. Now any product KPI investigation requires me to consider changes in a funnel that could impact the KPI. For example, if new users suddenly dropped I would need to check if there were product changes to the sign up flow in addition to looking at other factors that could’ve caused new users to drop.
Conclusion
Working with product has made me a better data analyst. Instead of looking at a common set of factors as I did in marketing, I’ve learned to consider how funnel changes and user experience might have influenced product results. I improved my communication skills by working closely with product managers on experiment design and product initiative sizing. Because of constant experiments to improve the product I’ve had many chances to practice explaining results and telling stories with data. It was not a seamless transition switching to a product data analyst role but it’s definitely a worthwhile experience to try and I hope you’ll find it makes you a better data analyst too.
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