LEARNING LAUNCH

CHALLENGE AND SELECTION:

A learning launch is a carefully designed experiment or prototype designed to  test the key underlying value-generating assumptions of a potential new-growth  initiative. In contrast to a full new-product roll-out, a learning launch is a learning  experiment conducted quickly and inexpensively to gather market-driven data to  determine whether a growth idea has enough merit to warrant detailed investigation  with the commitment of further time, people and resources.

APPLICATION:

Learning launches are a methodology for testing ideas to determine which  qualify as business opportunities. Because they should occur quickly and be low  cost, learning launches have also been described as “placing small bets fast.”  Unfortunately, some executives have interpreted that phrase as advocating the  pursuit of small revenue opportunities, which is not the case. Most good growth  companies are looking for growth opportunities to be scaled and result in needle- movers. “Small bets fast” is a testing procedure, however, and not a strategy  decision.

INSIGHT AND APPROACH:

A learning launch is a business-world application of the scientific method; it is a  way of thinking that relies on generating and testing hypotheses. It starts with  hypothesis-generating by asking a what-if question, which allows you to identify a  new possibility to act on. That possibility is your hypothesis — basically an educated  guess about something you think is likely to be a good idea. That’s the creative part.  Then to test your hypothesis, you specify what scientists call the boundary  conditions. In other words, you ask: Under what conditions would that hypothesis,  in fact, be a good idea? Or — worded differently — what would need to be true in  order for my idea to be a good one? So, you bring to the surface the assumptions that  lie under your hypothesis, and then you go find the appropriate data with which to  test them. That is the analytic part.

If it turns out that your assumptions are not true, and your hypothesis is disproved,  then you should take the new data and go back and use it to restate the hypothesis  better. And then you test that new and improved hypothesis. It is an iterative process  that you cycle through continuously, learning something new each time that allows  you to develop a better hypothesis for the next pass.


LEARNING LAUNCH CHALLENGE AND SELECTION : A learning launch is a carefully designed experiment or prototype designed to test the key underlying value - generating assumptions of a potential new - growth initiative . In contrast to a full new - product roll - out , a learning launch is a learning experiment conducted quickly and inexpensively to gather market - driven data to determine whether a growth idea has enough merit to warrant detailed investigation with the commitment of further time , people and resources . APPLICATION : Learning launches are a methodology for testing ideas to determine which qualify as business opportunities . Because they should occur quickly and be low cost , learning launches have also been described as “ placing small bets fast . ” Unfortunately , some executives have interpreted that phrase as advocating the pursuit of small revenue opportunities , which is not the case . Most good growth companies are looking for growth opportunities to be scaled and result in needle- movers . “ Small bets fast ” is a testing procedure , however , and not a strategy decision . INSIGHT AND APPROACH : A learning launch is a business - world application of the scientific method ; it is a way of thinking that relies on generating and testing hypotheses . It starts with hypothesis - generating by asking a what - if question , which allows you to identify a new possibility to act on . That possibility is your hypothesis — basically an educated guess about something you think is likely to be a good idea . That ’s the creative part . Then to test your hypothesis , you specify what scientists call the boundary conditions . In other words , you ask : Under what conditions would that hypothesis , in fact , be a good idea ? Or — worded differently — what would need to be true in order for my idea to be a good one ? So , you bring to the surface the assumptions that lie under your hypothesis , and then you go find the appropriate data with which to test them . That is the analytic part . If it turns out that your assumptions are not true , and your hypothesis is disproved , then you should take the new data and go back and use it to restate the hypothesis better . And then you test that new and improved hypothesis . It is an iterative process that you cycle through continuously , learning something new each time that allows you to develop a better hypothesis for the next pass .