Knowing how customers use your product is valuable information—and often not as obvious and straightforward as you think. This kind of consumer-use information can create opportunities for significant cost saving and product evolution.

For our purposes, a consumer is any entity using your product. That could be a human or an API, for example.

It’s important to understand how customers use your product, so that your team’s resources can be directed toward the most important aspects of your product. You don’t want to send your team on a mission to perfect a feature that nobody uses, while your most important consumer-critical features languish in the backlog.

So how do you find out how customers use your product in reality? Here are a few tips:

Don’t Believe Only What You Hear

You will gain many valuable insights from talking to human consumers, but remember they may not give you the full story. Accessing the right people can be difficult, and, even if you get access, people are subjective and, well… human!

People make mistakes and assumptions. sentiment and mood all play a part, and therefore inaccuracies are inevitable. Objectivity is required to determine actual usage patterns, and this is where rudimentary data capture can help you (and provide a very useful entry level data science upskilling opportunity for your organisation).

Data Capture Architecture

Think about designing your product to capture key data pertaining to real-world consumer behaviour. As a stakeholder you should be able to analyse this data easily to determine, for example, the most common consumer journeys, the number of times a particular event happened, the consumer time spent performing the task, etc. Ideally, this data capture should be scoped out and defined at the start of the design process to ensure the product architecture supports it natively. If it’s done properly, building in even a limited number of valuable data capture points at the early product design stage should mean that your product data capture capability is extensible and therefore more data capture requirements can be easily accommodated as they present themselves.

Actionable Metrics

Once you have the data (and please ensure that your enterprise data hub is correctly architected for future scale and also that your data is well formatted and “clean”), you must evaluate the data and take appropriate executive action. This business-level decision-making process is far simpler when supported by objective data. A question on whether a feature has reached “end-of-life” may be based on the material lack of consumer usage and not someone’s opinion that it has.

Building rudimentary data-capture and analysis capabilities into your product will help you understand product usage patterns and will enable smart executive decision making.

A very common real-world example of how this approach to consumer usage–based prioritisation can add value is as follows:

• A consumer of your product can achieve the same desired outcome via multiple paths through the application, but the path to achieve this outcome that your team is testing could be the path most rarely used—leaving the most commonly used path completely untested. This is not good.

The Importance of Learning How Your Product is Used

Appropriate data capture and analysis can help to objectively identify the important consumer-centric aspects of the product and enable intelligent business decisions based on facts and evidence. Your team will also thank you (a lot!) for telling them where they need focus their efforts…

An important indirect benefit of a project such as this is the up-skilling opportunity it presents for your development organisation to deliver a non-(production-)critical data–focussed project that will break the ice in terms of your organisation’s data-monetisation journey—a journey that I assume/hope you are on!