Not long ago, the best way to witness firsthand how the general population interacted with your product was to assemble a random sample of potential users and stick them in a room while you observed from behind a two-way mirror. That, or conduct an in-home study, asking users to keep a journal of how and why they use your product. The process was time consuming and often reactive, with studies being conducted once products hit the market. While this research could turn up valuable feedback for future offerings, the time delay made this type of user analysis ineffective for rapid, iterative product development.
As a process, product development itself has grown into a discipline in its own right. Today, there are many more digital products than there were ten years ago, and many, many more than there were twenty years ago. Twenty years ago, digital products were simple websites that were little more than linked documents. Over time, the web became richer and more interactive, and handheld devices like smartphones and tablets emerged as entirely new platforms and with this, whole new categories of digital products surfaced: products to act as personal assistants, helping you manage your fitness, finances, love life, relationships, play games, organize meetups, locate lost items, find the highest rated local coffee shop, and more.
Like the advent of seafaring vessels made the world’s oceans accessible to us, the birth of smart phones, smart TVs, and wearable devices has opened up vast new possibilities for innovative products. The opportunities were huge, the barriers to entry were relatively low, but these were global markets where the winning product tends to take the lion’s share of that market. It might only be inches that separate those products that win from those that lose, and in this environment, any tools or methodologies that can help systematically improve the product development process became critical. It’s no wonder, then, that product analytics has become a necessary, mission critical business component. Companies widely regarded as disruptors, like Spotify, Facebook, and Airbnb, are known to use data as an integral part of their product development process to amazing ends.
Product management has always been difficult because it exists within the overlap of customers, business, and engineering, requiring those inhabiting the space to be well versed in the languages of all three domains. Blending technical understanding with psychology, user experience design, and marketing knowledge, it is up to product managers to take holistic ownership of a product, fully understanding its users, what need it fulfills, and how it can continue to be an asset. Adding analytics to the set of domains that product managers should be expert on is a big ask, given the breadth of territory they already encompass. However, as we’ll argue in this upcoming series on product analytics, it is quite necessary.
Gone are the days of having access to a limited set of tools, capable of answering only the most rudimentary questions about how a product is used. Today we have the unparalleled ability to track every interaction that every user has with each product and product variation. Thanks to advances in collection tools and software, we can identify where those interactions are successful, where they are not, and to what order of magnitude. We can survey those users to understand their motivations and satisfaction levels, run tests to measure precisely how changes to a product alter their behavior, and closely monitor how these changes vary by different user types at different points in their user journey. This is a new world, both for data analytics and product management. Data has opened up new opportunities to help businesses and their product teams better understand their users and how they engage with a product, as well as how improvements to the product drive changes in behavior, satisfaction, and commercial outcomes.
In this environment, the product teams that are the most adept at using data strategically, tactically, and systematically as part of their development process have a distinct competitive advantage over those that do not (or do so less well). Data and product analytics have become integral parts of the product development process. And, because the lifecycle of a digital product is vastly different from a physical one, the ability to update, reconfigure, and evolve a digital product allowing it to persist indefinitely so long as it serves the business and the users, advanced product analytics help product teams move past only measuring the impact of past and current changes into creating hypothesis about what should come next. This space, using analytics to help chart your future, is still wide open with many avenues unexplored and we’re only just beginning to see what’s possible.
A comprehensive look at how the world's top companies use data to develop digital products
Product analytics, at its core, is looking at empirical data to determine if your product is the best version of itself, or on the right track to be. Much like product management, product analytics has matured beyond being an afterthought or relegated to the long list of things your business will “eventually” do; you may not be leveraging product analytics now, but you can bet the success of your next major release that your competitors are. Just because companies and product teams are proactively using product analytics, however, doesn’t mean they’re doing it right. As product analytics is still a new area, there isn’t the same breadth and depth of resources around the topic as there is around something like marketing or web analytics, older disciplines that benefit from years of rigorous testing and trial and error. This series on product analytics aims to change that.
Our users have a wide range of what they think is possible in the analytics world and an equally diverse set of philosophies around how to use product analytics. We’re going to take what we’ve learned about product analytics at Snowplow, from setting up elaborate collection pipelines and working with highly sophisticated users as well as developing our own digital products, and use that experience to help expand the knowledge base around product analytics. This is a large conversation to have; we’re going to demonstrate how companies that use data in their product development are the ones who outcompete the market and thrive, closely examine the organizational processes and culture necessary to make data part of your product development cycle, and explore the challenges many businesses face in setting up and using product analytics.
We see digital analytics in product management as one of the most exciting new industries and with this series, we hope to publish useful resources and practical guides on the role of data, overviews of tools, and specific techniques from which product managers, analysts, and anyone on the product team can derive value. If your product team isn’t optimized around analytics, you’re handcuffing your leadership. Don’t fall into the same traps caused by lack of awareness around user behavior that send countless product teams down misleading development paths, ultimately causing their products to fade into obscurity. Subscribe to our newsletter to be notified as we publish each post in this series and join us as we dissect how to get product analytics right.