Lagging Outcomes

Julie’s product team wanted to increase how customer-centric they were. But they were having a hard time creating a measurable outcome for this goal. Even if they could define a way to measure "customer-centric," they knew it would take multiple release cycles over a year before they’d have meaningful changes.

A common issue with outcome goals is that they’re lagging indicators. The outcome you’re after might be a long way down the road. It might take several iterations to do right. These things often end up off a team’s goals because it’s hard to figure out how to measure a meaningful outcome in the timespan the goal covers. This especially happens with quarterly goals or OKRs.

You need a leading indicator. A great form of a leading indicator is an input metric. Measure something that shows you you’re doing the right activities that are likely to eventually lead to the long-term outcome you’re after.

Sales teams do this all the time. It’s how they build sales forecasts and execute them. Not every customer you call will buy, but if you’re closing deals with one-third of qualified leads, then talking to 30 qualified leads should lead to 10 sales.

Those sales might take months. Every sale depends on factors far outside the sales team’s control. So how do they know if a change in pricing, process, or positioning is effective? They use the leading indicator of conversations with qualified leads. If the process change caused sales reps to have 50 weekly conversations instead of 30, then chances are their change worked. Input 50 weekly conversations and instead of 10 future sales, you’re on track to close 16 deals.

To become more customer-centric, Julie’s team decided the first step would be to improve how they react to customer needs. They defined a leading indicator of the number of customer touchpoints the product managers had every week. They knew that by talking to customers more often, they would be faster to recognize the needs of their customers.

They also decided that getting early feedback from customers on new features would increase the adoption rate of those features. Setting a goal of an increased adoption rate on those features would be easy, but Julie was concerned that this metric could be met even if the team didn’t develop a feedback habit. She opted for a proxy metric as a leading indicator. The team set a goal to run two experiments per week and iterate based on feedback for each experiment. This input metric might not create an increased adoption rate, but it would change the team’s habits in a way that was aligned with improving feature adoption. Julie knew that creating an experimentation culture was key to driving product adoption over time.

The key to input metrics is to figure out what is the fuel that drives your business and turn that fuel into a metric. For that sales team, the fuel is conversations with qualified leads. The more they have, the faster they go. For Julie, it was experiments. The higher the experiment velocity, the more customer adoption they saw.

These input metrics measure a behavior change. They’re an outcome. Your product managers were only running one experiment a quarter before, but the outcome you created is that they’re running every week.

Using input metrics can expand the range of what you can use as outcomes. This can make it easier to bring long-term thinking into your goal setting.