No matter what industry or role you’re in, you know the importance of being data-driven. Gone are the days when teams could feel confident relying on “gut feel” to make important choices about the future of their product. Data—both qualitative and quantitative—is now the guiding force for making important product and business decisions.
To meet their data needs, companies are increasingly turning to product analytics—business intelligence software that captures and exposes usage patterns from digital products like web and mobile applications via event tracking, event properties, and event and property grouping. But not all product analytics solutions are created equal. As you decide which is best for your business or organization, here are a few key considerations to keep in mind.
Will it cause an engineering headache?
Though many analytics solutions claim to work “out of the box,” the reality is that a large number require costly engineering time and resources to properly configure and implement. This results in wasted time and money, an inflated total cost of ownership, and most distressingly, the siloing of data.
Product teams should look for a low-code solution that lets them get up and running fast. Pendo, for example, offers retroactive analytics that start providing historical data about product features from the moment you tag them, in contrast to other solutions that require engineering-heavy instrumentation before providing insights.
Companies should also seek out solutions that will let them democratize data across the organization. In order for a business to become truly data-driven, the insights its analytics solution provides have to be easily accessible. If only those with a high level of technical savvy can make use of them, what’s the point? Less technical users should always be able to work independently with data without having to bring in developers, and a company’s choice of analytics platform needs to allow for that.
Analytics data is only as powerful as what you can do with it
Many analytics solutions may be great at providing data, but when it comes to what you can actually do with that data, they start looking much less impressive. Sure, it’s great to have all these insights, but shouldn’t the same solution that provided them also make it easy to take action on them?
That’s why the best product analytics solutions go beyond mere analytics to optimize the full product experience. They let you seamlessly turn those data-driven insights into concrete optimizations. For instance, Pendo allows you to take relevant behavioral data and instantly integrate it into your in-app guidance and notification strategy. You can then target specific user segments and customize your support based on the actions they take.
In other words, analytics doesn’t exist in a vacuum. It’s one (crucial) part of a larger process, and the best solutions let teams build on the insights they provide accordingly.
The value of quantitative and qualitative data
Usage and other quantitative insights are necessary but not sufficient to optimize your product experience. Any comprehensive product analytics solution should offer you both behavioral data and feedback collection capabilities. At the end of the day, your users are human beings. Understanding how they feel about your product and what it offers should play a crucial role in both your business strategy and product roadmap. The best solutions offer feedback capabilities that help make users themselves part of the change process.
Ultimately, when it comes to analytics, there’s no point in going with a point solution. Choose a comprehensive platform that lets you easily access all the data you need, take the right next steps based on it, and build a better product—and a stronger business—in the process.
Ready to learn more about how Pendo equips you with robust analytics to take the right actions? Check out the comprehensive set of features it offers product teams to create the best digital experiences here.
