Updated: May 29
You meet your CEO and she asks, what's the probability that the change initiative you are leading is having a positive impact?
Impact & Results: demonstrating that change is more than just a good idea
Reporting on the impact and results of your change initiative is one of the most significant challenges that you will face as a change or transformation lead.
I've written about the need to bake in FAIRR reporting measures using change informatics and Impact Reporting dashboards (see video below), but how do you get a credible sense - that feeling - of whether your change initiate is creating the positive impact you hope for?
So, what is the probability that your change initiative is having a positive impact?
Imagine that you are implementing a digital transformation project called The Knowledge Stream (KS) in an international financial services company - the sector doesn't matter, and I encourage you to think of a similar initiative in your organisation.
The KS project takes 'individual knowledge in the organisation and gives it wings'. Based on an ethos of sharing for the common good, KS will provide opportunities for everyone to access the knowledge they need when they need it most.
The Senior Management Team decided to invest in KS because they believed that it would positively impact the quality of customer experience, which they hope will impact client acquisition and retention.
KS went operational 12 months ago, and you need to report on the project's positive impact. You have designed an Impact Reporting Dashboard (IRD), and the data tells you the following:
73% of client consultants who increased Customer Satisfaction Scores (CSS) by 5% or more in the last 12 months accessed KS.
During the same period, 22% of client consultants accessed KS and did not increase CSS by 5% or more.
Historical data shows that before KS the probability of CSS increasing by 5% or more is 4%.
So, what is the probability that access to KS is improving Customer Satisfaction scores by 5% or more?
Introducing Bayes' Theorem
Bayes’s theorem, in probability theory, a means for revising predictions in light of relevant evidence, also known as conditional probability or inverse probability. (Britannica.com)
Thomas Bayes was an English statistician who in 1763 proposed the following formula, which can be used to forecasting risk and the probability of success (see the video for more information):
P(A|B) is the probability of Event A occurring, given that Event B has occurred.
P(B|A) is the probability of Event B occurring, given that Event A has occurred.
P(A) is the probability of Event A.
P(B) is the probability of Event B.
Bayes' Theorem has been adapted for special cases, where Event A is a binary variable (e.g. yes/no):
P(B|A-) is the probability of Event B occurring, given that Event A- has occurred.
P(B|A+) is the probability of Event B occurring, give that Event A+ has occurred.
Using Bayes' Theorem, you can now explore the probability that engagement with Knowledge Stream increases CSS by 5%+:
P(A) = The probability that CSS increases by 5% or more (4%)
P(B) = The probability that employees are accessing KS (73%)
P(A|B) = The probability that CSS increases by 5% or more given access to KS
P(B|A) = The probability that KS has increased CSS by 5% or more
0.73 x 0.04 / 0.73 x 0.04 + 0.22 x (1-0.04)
In this case, the probability of KS engagement increasing CSS by 5%+ is 12.2%.
Two questions prompt what you need to do next:
What do you feel about this number - this is your reflex response?
What do you think about this number - this is your critically reflexive response?
These two questions prompt three further questions for taking data to action. Given what you feel and think:
What could you now do?
What do you now want to do?
What will you do?
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