Product Teams Need More Than Traditional Analytics

When Google Analytics first launched a decade ago, it was a game changer for digital analytics. Suddenly, you were able to understand high-level user trends, like attribution and referral sources, faster and more accurately than ever before.

Traditional analytics tools like Google Analytics are accessible but limited to counting page views and clicks, while more sophisticated tools like Adobe come with a steep learning curve which causes teams to move slowly from data bottlenecks and long analyst request queues. A digital-product focused company needs a tool that analyzes important user outcomes—like activation and retention. Traditional analytics alone is simply not equipped to handle the complex analytics demands of today’s digital product teams and the speed they need to move.

Ruben Ugarte, Founder of Practico Analytics

Ruben Ugarte knows the difference between product and traditional analytics better than most. For the past four years he has operated an agency, Practico Analytics, that works with product companies to help them set up and get the most out of product analytics tools. Over the years he has seen companies grow increasingly interested in the long term effects of campaigns—looking at retention rather than just conversion. And, while product companies want more powerful and flexible analytical tools, they’re wary of anything that seems too complicated to set up.

The case for contemporary product analytics

Product companies today serve thousands—if not millions—of users. As a result, they’re capturing, storing, and analyzing large volumes of user-level data points. Product analytics tools allow for robust, flexible analyses that can be customized for the needs of individual products.

“Modern product companies need to understand the behaviors of their users and use that data to build a culture of experimentation based on facts and not opinions,” Ruben explains. “A company that is able to consistently run experiments based on data will likely outperform other companies that are trying to guess at what their users want.”

Without a tool to analyze user behaviors, teams are limited to anecdotal evidence based on customer stories and intuition based on what feels right. Essentially, they’re relying on qualitative data and educated guesses. As Ruben puts it, “qualitative data isn’t wrong, but it’s only one part of the answers companies need.”

The more advanced and customizable your product analytics tool, the better. Every digital product is different and it’s worth taking the time to come up with detailed taxonomy that instruments event and properties in a way that works for your product.

Ruben suggests that companies, “spend time understanding their data taxonomy. Detailed taxonomies allow companies to better define what data they should be capturing to better understand their users. We are now seeing more tools that can do really cool things with your data but it needs to be structured properly. A good data taxonomy is the “secret sauce” and prerequisite to all of this.”

A data-driven approach applies to more than just analysts

It’s a common misconception that analytics should be isolated to just the analysts. That couldn’t be further from reality. Everyone at a product company benefits from data in one way or another.

Take customer support teams. “Customer support teams can look at the profiles of individual users to try and understand what issues they are running into and provide better support,” Ruben explains. When a customer issue surfaces, product data can shed light on the specific issue a user is facing. That data gives the customer support team all the context they need for fast, effective problem-solving.

Sales teams can also use product data to their advantage. Lead data, leveraged well, can create a more personalized experience and drive conversion. As Ruben puts it, “sales teams can use the data to inform their conversations and ask relevant questions based on what a prospect has done with their product.”

Even finance teams can use product data. Ruben shares, “I’m starting to see finance teams use this data to better understand potential acquisitions. This is similar to how venture capitalists would look at certain metrics to understand product adoption.”

What a modern analytics stack looks like

When building out your analytics stack, flexibility and the ability to customize it to your product’s needs are key. Ruben explains that compared to traditional analytics like Google Analytics and Adobe, product analytics tools are “much more flexible for understanding what your users are doing (by name, email, user id), and have been designed to reflect modern problems such as funnels, user retention by cohorts and using machine learning to find trends in your data.”

Ruben recommends using these six tools for a comprehensive analytics stack:

1. Segment.com for data abstraction and as a CDP
2. AttributionApp for attribution
3. Branch.io for mobile attribution, deep links and much more
4. Amplitude for product analysis and user behavior
5. AutopilotHQ for email, SMS and in-app communication
6. Amazon Redshift for a data warehouse

Key takeways

If you’re not using product analytics, chances are that you’re relying on traditional analytics tools or anecdotal evidence for your product decisions. Neither is ideal. Rigid traditional analytics solutions won’t help you fully understand what’s going on with your product, while anecdotal observations will riddle you with biases and incomplete information that prevent you from making well-informed decisions.

And, the need for product analytics exists at every stage of development. Even in your earliest days, running and analyzing experiments can make all the difference in building a better product from the get-go. As your product matures, you’ll have more users (and more user segments), more in-product events, and therefore much more data to analyze.

The reality is that product analytics is a useful tool that can’t be substituted. It can help teams from engineering to customer support deliver better work. It can help you problem-solve. It can help you better understand user behaviors to learn how and why users interact with your product in the ways that they do. No matter what, your product can always be made better with product analytics.