Experiment when you experiment (Toolkit idea)


Thesis: You can learn more when you innovate if you adopt a few concepts from the scientific method.

In my experience, plenty of people in the American Theatre innovate frequently. Whether the subject is artistic style, theater technology, seating plan, or any of dozens of other things, our field is well stocked with people who are willing to try new approaches.

However, and again, this is only based on what I see myself; many of those innovations are undertaken in a fairly haphazard fashion. Many theatrical enterprises operate on such tight resource budgets that there is a perceived obstacle to approaching innovation in a structured way. Somebody has what feels like a great idea. She implements that idea on a particular project. If the new idea is perceived as having improved the outcome of that project, the innovation might be repeated on a future project that is in some way similar; but it might equally well be forgotten.

Implementing a few of the key concepts of the experimental method can help someone focus on an innovation and extract more of the available value from that innovation, and it needn’t take a great deal more time or effort. Here’s a little recipe for turning a “Let’s try to do it this way this time,” into an experiment.

First, when you are making a change, you are doing it because you believe that the change will in some way lead to a better outcome than not making the change. That belief is the core of the hypothesis – the statement of what you are hoping to achieve and learn by carrying out the experiment. Ideally, this is in some numerically verifiable structure that will be easy to evaluate, but don’t feel chained to that at first.

Example: “If I encourage/harass everyone in the cast, crew, and company to vigorously promote our upcoming show on his or her Facebook timeline then we will see 150 (an arbitrary number chosen to represent enough of a benefit that it will have been worth the hassle of doing the innovation) more people attend as compared to our last show. As a side benefit, making this effort and seeing the results from it will increase everyone’s satisfaction with participating in the show.”

Second, come up with a few things you can measure during and after the execution of the innovation to learn whether or not your hypothesis was correct. Ideally, you will design your hypothesis so it relies on things you already measure as part of your operations. If your experiment requires you to measure something you wouldn’t otherwise measure, make sure it is something you want to know and that it won’t be too burdensome.

Example: “We had 800 people attend the last show. The experiment will be a success if at least 950 attend. As to peoples’ senses of satisfaction, I’ll ask folks at the strike party how they think their promotional efforts affected their feelings about the show. I’ll keep a pen and index card in a pocket and just mark down tick marks for ‘better’ or ‘worse.’ At the end of the evening, if my card has twice as many betters as worses, I’ll feel like I succeeded in that way.”

Third, carry out your innovation and collect the information you decided to measure. Now those of you who paid close attention back in high school science classes will remember that full on science would want you to do the show twice, once with the innovation as the experiment and once without the innovation as a control. That’s crazy talk. You’re still going to get useful information about your innovation idea using some prior production or the average of a few prior productions in lieu of an exact copy control.

Fourth, evaluate the numbers you’ve collected against the values predicted. Decide whether your hypothesis has been supported by the experiment. If it has, you can then think clearly about whether to incorporate that innovation into your routine. Because you’ve approached the whole process in a more organized way it will probably be easier for you to remember key parts of the experience and repeat valuable innovations in the future.

Also, borrowing these bits from science will serve as a reminder that even if your hypothesis was disproven, you haven’t wasted your time. You’ve learned something. The particular approach you tried under the circumstances didn’t deliver what you’d hoped. Having engaged in all this in a systematic way will help you understand whether your hypothesis was really wrong – meaning you’ve found an idea you can cross off your list and not worry about any more – or whether you executed your idea poorly – meaning you might try it differently on a future project.

Finally, if your community of supporters includes people of a scientific bent, seeing you apply these methods will encourage them to treat you more like a grown up and increase their faith that you know what you are doing.

I encourage you to define a hypothesis, set metrics, evaluate those metrics, and evaluate your hypothesis the next time you want to try something new. My hypothesis is that you will find your innovative efforts deliver greater rewards when you turn innovation into experimentation.

  • February 2, 2013