If you’re just starting out, remember it’s an iterative process to capture and understand the data that’s most relevant to improving or refining your business or product. Start by talking to your engineering team and asking them what types of tracking or reporting is possible. Ask if any off-the-shelf analytics tools or platforms make sense for your product. Even if you’re not ready to use the data yet, it’s good to set up a baseline understanding of tracking so that you have a place to start when you’re ready.
Infuse data into your everyday work environment. Make this information a regular part of team meetings, project briefs, and goal setting discussions. Start with basic, easy to understand numbers like web traffic, conversion rates, or whatever is relevant to your team. The goal is to familiarize your team with this information to make it common knowledge. It’s critical to share and discuss the larger business context in which these numbers exist and how they relate to the work your team is responsible for. The more discussion you have about these numbers, the more nuanced and savvy your team will become.
Create an environment where diverse perspectives constantly analyze your data and its context within the business. It’s important to understand how your data relates to customer goals across the organization. To gain the most value from these perspectives, foster a team culture of cross-functional communication. Invite engineers to design meetings. Similarly, invite designers to technical meetings. Encourage them to share and explain their challenges. Bring junior people into strategic conversations and be supportive — as their insights may not yet be constrained by the corporate culture.
When using customer insights to develop product enhancements, it’s not always clear which metrics or data points are most relevant. Start by writing assumptions using tools like the Jobs To Be Done (JTBD) or the Demand-Side Sales framework. This will help you create hypotheses about how your product or service fits into people's lives and what outcomes they’re trying to achieve. Ask the members of your team who control your data pipeline how they might help you test these assumptions by using the data that’s currently being collected. This may lead to adjusting the data pipeline and/or refining the assumptions. Pilot your product enhancements and monitor how they correlate with your assumptions and ultimately impact your data.
It’s important to keep questioning the accuracy of your data. To have a healthy discussion around data accuracy, zoom out and map it against a customer journey or marketing funnel so that you can benchmark against other (preferably independent) data points. Identify points along the journey/funnel where you have a high confidence in the accuracy of the data. Calculate conversion rates from one stage of the journey/funnel to the next and debate with your team any data points that defy your expectations. Keep an open mind and remember that we naturally apply personal bias and anecdotal references that don’t always represent the majority of our users.