- Brian Levy

# Hypothesis Planning in Performance Management Part 2

Updated: Apr 27

At Bridgeport Digital, our network of teams uses ‘Hypothesis Planning’ as a strategy, on a weekly basis, to plan and execute the hypotheses we produce through ‘risk reduction;’ which is when we test high risk items to verify whether or not our assumptions are true. In hypothesis planning, we plan our hypotheses on info that is current. First, we settle on a goal, based on an assumption, (what we believe to be true, “the risk”) before we test our hypotheses. Then, as we test our hypotheses, we de-escalate any risk of untrue assumptions along the way; making our goal more attainable.

**Hypothesis Planning Explained**

This is all theoretical underpinnings of the process that we’d like to do. From a process perspective, we start by finding out what the goal is. For example, let’s say we own a training company and we’d like to find an effective way to distribute classes to people. One of the goals for our company is to create a distribution network and use it to reach out to the public; to get people in the class. That way we can focus on the material and delivery and they can focus on the hard part, which is getting people into the class.

To further explain, we have our goal, we should ask ourselves some questions in order to figure out how to reach the goal. The main question I want to ask is, "What must be true in order for us to develop a robust class distribution network?" The answers become our assumptions. Again, I want this robust class distribution network where I can decide to hold a course and I can pick a date; then share with the network and then they figure out how to get hundreds of people into the class. What must be true?

There must be a schedule for the class; a class schedule exists.

There must be a professional instructor that would teach the class; an instructor available.

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**Decision-making In Hypothesis Planning **

Decisions have a life cycle:

They start with zero to little information, known as a ‘hypothesis.’

Then we collect information and form a ‘conclusion.’

Next, conclusions tested by our actions become a ‘decision.’

After the decision has been validated, use it to make predictions, we call this a ‘theory.’

Lastly, after a theory’s been tested multiple times, it becomes ‘knowledge’ used to make future decisions/hypotheses.

**How We Use Risk Reduction**

In our next step, we may not have time to test every one of these assumptions out. However, some of these assumptions are wrong, so we need to make a decision on what to do, based on these assumptions. Then, the decisions we’ll make, if based on wrong data, could be wrong themselves. We want to avoid that, we only want to make the right decisions. What we want to do is take each of these items and rate it in two dimensions. The first dimension is the probability that the assumption is incorrect; the probability that it’s wrong. The reason I want to know the likelihood that it’s wrong is because the more it’s wrong, the more I should test it. If there are things I’m certain are 100% true, I should test those things less. If it is wrong, I also want to look at the impact of reaching our goal. If it’s a big impact, then I’ll tend to test these things to be sure that it’s correct. If it has a low impact, it isn’t as necessary to test it out.

We want to do this, as we put things in context, because we’re making assumptions in context about our customers. We’re making assumptions about what they want, like, and what they want to see. If I’m wrong about my assumptions, customers won’t like what I’m producing and we won’t get the revenue from it. All of product management should be thinking about customer validation. We want to build something, but before we finish building it, we need to validate whether or not they want it. Here’s what we’ll do, we rate each assumption.

**Ratio Impact Scale**: We’ll rate our assumptions on a scale from 1 - 10; 10 as in most likely to be incorrect and 1 as most likely to be correct; not doable/doable (impact).

What’s the probability that as we build a robust class distribution network, we won’t have a class schedule? (5)

If we’re trying to build a robust class distribution network and we don’t have a class schedule, how big of an impact does that have? (6)

Class schedule exists (5) (6) = 30

What’s the probability that as we build a robust class distribution network, there aren’t any instructors available? (2)

If we’re trying to build a robust class distribution network and we can’t find an instructor, how big of an impact does that have? (10)

Instructor available (2) (10) = 20

Calculate magnitude by multiplying the two numbers together.

Based on the ratios, 20 and 30, which one should we prioritize first?

Creating a class schedule (30)

*“The secret of getting ahead is getting started. The secret of getting started is breaking your complex overwhelming tasks into small manageable tasks, and then start on the first one.” - Mark Twain *

**Concluding Hypothesis Planning**

We should prioritize creating a class schedule because it has the highest risk between the two. What we’ll do next is think about forming a hypothesis based on this assumption. A hypothesis is a relationship between the assumption itself, what you plan to do, and the results you obtained from it. As the highest risk, we’ll work on this first. Throughout hypothesis planning, teams at Bridgeport continue to use risk reduction to verify whether our assumptions are true or not because if our assumptions are wrong, we won’t attain our goal. Hypothesis planning gives teams the opportunity to identify the risks, test them out, and use the knowledge we’ve collected to make future decisions.