A few days ago, I needed to find the addresses of two Metra train stations. So I went to the brand new Metra website and clicked something like “Find a station”. A text box and map appeared, so I typed in the city I wanted: Lake Forest. The map zoomed to Lake Forest, California. Great, right? Not exactly. Metra service only runs in Illinois and some parts of Wisconsin and Indiana, so the result was useless to me.

I ended up having to navigate through a list of stations to find the right one. That’s a shame, because Metra has all the data they need to improve their results dramatically. All they need to do is look at what they know about their customers, and then make certain assumptions about what those customers need. In my case, people can only ride Metra in a few states. Thus, they could filter the search results to only include cities and states where Metra has stations.

I’m sure there are many other examples of this across numerous industries. And the same approach should work in each scenario. Just think about who your customers and other stakeholders are, and write down what you know about them based on historical data or other sources. Then, when you have attributes that are obvious enough to be considered safe assumptions, try integrating those into your business rules and customer-facing applications. Chances are that task completion rates will increase, and customers will reward you with increased spending and long-term loyalty.