Using Hypotheses to Make Better Decisions at Scale

We talk about assumptions and hypotheses a lot when we’re developing product strategy and making decisions on what to build. Whether you’re creating something new or rethinking an existing product, you have to make some bold choices about what include or omit, what to spend time building and what to postpone or neglect entirely.

Ideally, these decisions would be based on validated user feedback. The world isn’t ideal, so we have to guess. That’s where a hypothesis comes in. A hypothesis is an intelligent, articulated guess that is the basis for taking action and assessing outcomes.

In a recent article, Jeanne Ross talks about how Seven-Eleven Japan empowered their store managers to develop hypotheses about which products to stock based on sales data from the previous week.

“…companies must experiment to learn both what is possible and what customers want. Most companies are relying on empowered, agile teams to conduct these experiments. That’s because teams can rapidly hypothesize, test, and learn.” 
– Jeanne Ross

A failure is only a failure when you don’t learn from it. Hypotheses give you a framework to experiment with ideas that has learning built in. By deciding what you’re going to test and how you’ll measure the outcome, you’re less likely to try something willy-nilly and not learn when it fails.

“Leaders in companies that want to seize digital opportunities are learning through their experiments which strategies hold real promise for future success. They must, in effect, hypothesize about what will make the company successful in a digital economy. If they take the next step and articulate those hypotheses and establish metrics for assessing the outcomes of their actions, they will facilitate learning about the company’s long-term success. Hypothesis generation can become a critical competency throughout a company.” – Jeanne Ross

We use a tool created by Allissa Briggs called the Experiment Grid to describe what will be tested, document our assumptions, and define how we’ll measure the outcomes. This structure helps the entire team understand what’s happening, and facilitates discussion when the experiment is done.

I recommend you read the full Article on MIT Sloan Management Review and think about how you can use the Experiment Grid to help guide your product decisions.

And let me know if you’d like some help.