Never Stop Asking “Why?”
Remember when you were a kid and the most interesting question in the world is “why?” It’s one of the ways children learn about the world, by being permanently curious. The conversations that go on and on until a parent finally gives up and says “because I said so.” Remember how frustrating that was as a child?
“Because I said so” is still a bad answer. But as adults we often don’t delve deeper in the same way we did as children.
It’s the start of the new year. In many companies that means there are new priorities coming down. New lists of features. New demands for work. And a lot of them lacking the deep dive into “why?” that they need.
Without delving deeper into answering “why?”, our ranking of work is going to be subjective at best.
I recently had a discussion about improving a dashboard in our UX. Several stakeholders wanted us to put effort into design and development. I asked why.
“Because we don’t think it’s good enough.”
OK…I don’t disagree. But that’s pretty nebulous. So, I asked why it isn’t good enough.
“People can’t manage their apps and services well enough”
Again, I don’t disagree. But, so what? I asked why that was important.
“If people can’t manage their apps and services easily, they don’t add extra functionality. They don’t create new services.”
Seems to make sense. But, again, why is that important?
“If users have more interconnected services, their lifespan as a customer increases.”
OK…now we’re getting somewhere. Our real aim is “we want to make creating and linking services something that users want to do more”. And we have a good business reason behind that, too. It reduces our user churn.
If we didn’t delve deeper, our target would be “make the dashboard better”. And the only measurement would be a subjective one.
Now we have a more detailed aim, we can come up with more specific solutions. We want to make creating and linking services something that users want to do.
Let’s say that our analytics suggest a slow search function puts people off creating services. The biggest drop off in usage is when people wait more than 3 seconds for search results. Our average search result time is 4 seconds.
That’s the basis for a genuine hypothesis. We can expand that with more study of analytics. And we can make some educated guesses. We might end up with;
“Reducing our average search time by 25% will lead to a 10% increase in the number of services a user creates.”
And, with enough user data already stored, we could make a good guess the business impact.
“A 10% increase in the number of services a user creates will mean a users stay with us an average of 3% longer.”
Now we have a measurable piece of work (reduce search time by 25%). We have a hypothesis of the impact on users. And a hypothesis on the business impact.
(I’m making these specifics up to demonstrate the point, in case anyone is hoping to pick up any secrets!)
Those specifics are the vital pieces of information we need to make business decisions. They help us rank the importance of work.
- We can scope the specific work. That will give us a good idea of the time / cost of making that change.
- We can compare that time / cost with our predicted results and business benefits. Is there a return on investment?
- Even if there is a return on investment, is it the best option? Is there something that takes less work, but with a similar impact? Or is there another piece of work that will have more impact? Do that work first.
And, if we do this work, we can measure the results. We can tell whether it was successful in the way that we predicted. Or our hypothesis was wrong. If it is wrong, we’ve still learned something by disproving it.
None of that is possible unless we keep asking “why?”. If we don’t, then we’ve got a vague “make something better”. We don’t have any objective measures of success or failure. And no way to determine if this is more or less important than another piece of work. We won’t prioritize well. And we won’t learn how to get better.