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Doing experiments – how to create Hypothesis and validate your Experiments

by Veronika Goncalves

Lean Change Management Model

Lean Change Management is a book written by Jason Little, which shows how to implement successful change through examples of innovative practices that can greatly improve the success of change programs.

Jason talks about the Lean Change Management Cycle, which consists of 3 big parts: Insights, Options and Experiments.

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Let´s first look at a brief description of it:

Insights: It´s very important to understand the current state of the organization, before you can plan any change. In order to do that, there are several tools, assessments, and models you can apply to understand the current position. The Lean Change Management book describes many practices to collect Insights.

Options: When you gain enough Insights to start with the planning, you will need Options. Options have a cost, value and impact. Options usually include one or more hypotheses as well as expected benefits. These hypotheses are then turned into Experiments.

Experiments: Now it’s time to introduce a change and see if it works out. At this point you should learn enough about your current position and consider multiple Options.

Experiments contain a sub-cycle:

Prepare: This is the planning stage of your Experiment. At this point, all you have are your assumptions about the change. In this step you validate your approach with people affected by the change.

Introduce: In this step you start working with people affected by the change. Once a change will reach this step, it is part of the process.

Review: Here you review the outcomes of the Experiment. Normally you do this after the amount of time you thought you would need for the change to stick.

In this model, Insights is when you observe the situation as it currently is. Then you move to Options, where you evaluate cost, value, and impact of each possibility. From this you create a hypothesis to test the expected benefits of that test. Using that hypothesis, you form an Experiment.

Why Experiments?

When undergoing a change in your organization, you want to automatically call everything a change. But calling changes “Experiments” helps you develop an approach that makes it OKAY to not know everything upfront.

There might be unexpected impacts of a change transformation in your organization that you didn´t previously consider, and for that reasons calling a change an experiment helps you be more creative and learn while the change is in progress.

Experiments consist of 3 main components: 

Hypothesis: When you think a change is a good idea, then you create a hypothesis. It is based on your own bias.

Measurements: This is how you´ll measure the outcome of the Experiment

Big picture: You can track a big picture on your change wall. Your Experiment should fit into the big picture of your change.

Creating Hypothesis

All experiments start with hypothesis. Below you find a hypothesis structure:

We hypothesise by <implementing this change>

We will <solve this problem>


Which will have <these benefits>


As measured by <this measurement>

This structure or template helps change agents to be explicit about Experiments. You´ll be able to measure your Experiments better when you explicitly state the benefit, measurement and goal for your Experiments.

Another advantage of using this template that it uses a simple language everyone can understand, so nothing complicated.

You can follow the following thought process:

  • think about what the experiment would be
  • think about who would be affected
  • think about what would be the benefit
  • think about how to validate the Experiment as successful
 I believe most of people are concerned if these selected processes are right thing to do, so how could we validate them?
Validation is confirming that the change you´re planning is the right one to focus on before you spend all your time and effort designing a change that is likely going to be wrong.
To validate your Experiment, you have to go through 2 steps:
1. The first step involves asking 2 questions before running the Experiment: 
How will we know this Experiment has been successful?
How will we know we are moving towards our outcome?
2. The second step is to review your Experiment with people that are affected by the change to see their reactions. 

Measuring Experiments

“What gets measured gets managed.”
Peter Drucker, Management Guru.
It´s important to be careful how we measure progress, because it can influence behaviour.
Jason suggests using both qualitative and quantitative measures to measure business outcomes that organizations see when adopting lean practices, but also intangible measures like whether or not people feel that this way of working is really effective.
This is crucial because it´s not only important that organizations are making progress, but people should be happy too.
Qualitative measures
Jason uses an example in his book when he asked a team during a daily to raise and resolve project risks and issues. He didn´t feel theses were effective and to find out how people feel he posted a poll asking for feedback. On the right side of the flip chart he wrote “This meeting is awesome!” and on the other side, “This meeting is terrible.” People had to vote by marking an “X” under the choice showing their opinion.
Quantitative measures
NPS (Net Promoter Score) s a good way to collect the data. By using NPS, you ask a question: “On a scale of 1 to 10, how likely would you recommend this product/service?” People who vote 9 or 10 are promoters and those who vote 1 to 6 are detractors.
In overal, allowing teams to align themselves with overall change strategy is sometimes more important than actually pushing measurements on them. It motivates the team to contribute because they feel a sense of ownership about their involvement in the change process.
It´s crucial there is a strong alignment between management and teams for this approach to work. More about alignment read here.
Sources: http://leanchange.org/resources/experiments/
Book Lean Change Management by Jason Little
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Veronika Goncalves

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