Avoid These Common Anti-Patterns

As you manage the cycles of continuous discovery, be sure to avoid these common anti-patterns.

Overcommitting to an opportunity. Throughout your discovery, you will uncover opportunities that are important to your customers that you won’t be able to deliver on. We saw this in Mina’s story at Simply Business. It’s quite possible that someone else will solve late payments for small-business owners. It may even be a Simply Business competitor who does so. However, given the context in which Mina’s team was working—what her company was asking of her and what she was learning from her customers—it turned out not to be the right opportunity for her right now. This doesn’t mean her team can’t return to it later down the road.

We also saw this come up in Victoria’s story. Her team had the resources to address the need in a specific way—with CarMax generalized data, not vehicle-specific data. When they learned that generalized data wasn’t sufficient, they chose a new target opportunity instead of continuing with an opportunity that they knew they weren’t in a position to address right now.

One of the hardest challenges with opportunity selection is identifying the right opportunity for right now. However, a round of assumption tests should help you assess fit quickly. These stories are a good reminder of why we want to run quick tests rather than overinvest in the best tests.

Avoiding hard opportunities. Some teams interpret continuous delivery to mean continuous delivery of easy solutions. Quick wins have a time and a place in our work. If we can deliver impact this week, we should. However, many of the opportunities we uncover will take time to address adequately. Don’t confuse quick testing and iterative delivery with easy solutions. You saw in Chapter 11 that, at AfterCollege, we were able to find a quick test of a hard solution. Before we invested months into building a robust machine-learning solution, we started with a crude approximation that we could prototype in a few days.

We also saw this mindset in Victoria’s story. When her team learned that they had tapped as much of the potential as they could out of generalized data, they didn’t shirk away from vehicle-specific data. They started to lay the groundwork for those types of solutions by working with other teams. In the meantime, they worked another opportunity in parallel. This allowed them to both deliver impact now and lay the groundwork for even more impact in the future.

Drawing conclusions from shallow learnings. As you learned in Chapter 2, discovery requires strong critical-thinking skills. Otherwise, it’s easy to draw fast conclusions from shallow learnings. We saw this in Carl’s FarmLend story. Once hearing that customers valued picking up the phone to talk to their loan officer, Carl’s team could have abandoned their digital-engagement strategy. But instead, they asked the harder question, “How can we reconcile our business need with our customers’ needs?” And as a result, they found an opportunity where their customers did want to engage digitally, and they used that opportunity to grow their digital relationship with their customers. They did the work to uncover the depth behind their shallow learning. Their customers did value their relationship with their financial officer. But they were also willing to do plenty of research on their own. Carl’s team worked to tease out these nuances and were rewarded for it.

Giving up before small changes have time to add up. While you do want to measure the impact of your product changes, don’t expect to see large step-function results from every change. Oftentimes it takes a series of changes to move the needle on our outcome. We saw this in Amy’s and Jenn’s story at Snagajob. Every time they solved one problem, it opened the door to the next problem. But they kept at it and, over time, had a big impact on their desired outcome.