How data helps us design our products
“How can we collect more data?”
“Let’s look at the data.”
“It would be great to get some data around how users are using feature x.”
These are utterances we hear at Envoy quite often, and everywhere it’s becoming ever more common with companies collecting and consuming more and more data. Our goal, however, has not changed too much — we want to identify clear trends in the data that can help us make better product decisions about what to build, how to best build it, and for whom. As we grow with our product teams, becoming multi-product, exploring more complex use-cases, and modernizing ever more offices, data becomes increasingly important and increasingly challenging to scale. In this post, we share some best practices we’ve found and lessons we learned.
Analyzing usage is key
Unsurprisingly, staying abreast of how your users are using your product is critical to success. Keeping up with usage allows you to identify problems faster, recognize changing trends in a timely manner, and prioritize features. Data helps keep our product teams honest and makes sure the right things are being built and results are tracked and monitored.
Developments in technology has really helped close the gap between us and our users. At Envoy, we use tools such as Heap and Fullstory to be able to meet our customers right where they using our product, in their environment. This not only helps eliminate bias from our user sessions, but also saves us time. We’re able to replicate the results of traditional UX research sessions for a fraction of the hours and cost, and scale as our number of users grow. Casting a wider net also helps us identify new use cases and workflows previously unanticipated. We can then spend more of our time understanding the findings to build empathy for use cases and pain points to help prioritize our roadmap.
Having a hunch
We’ve found product usage analytics has helped us think through a hunch. We often have hunches about how users get maximum value out of our product. It could be something reactive such as how successful adoption of a new feature is. It more often is forward-looking such as whether a series of actions or behaviours lead to surfacing more value. In times like this, it is less intuitive to get existing data to make a decision. But we’ve found that clustering users to find those who exhibit the hypothesis, or in some cases a correlated proxy action, gives us enough insight to set a direction. Hypothesis testing like this allows us to iterate quickly and choose a fork in the path, even if the path remains unclear.
What data cannot do
On the other hand, we are carefully pessimistic about what data cannot do as well. Data cannot innovate your product for you. Being data-driven is great for corroborating intuition, iterating quickly, identifying problems promptly, and making difficult decisions with no clear direction. It can optimize your current product and take you to a ‘local maximum’, but it will always be blind to the ‘global maximum’. Ultimately, to create the better office experiences of the future, a deep empathy for our users, the problems they are trying to solve, and a brilliant team of innovators are required to be successful.
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