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What is Business Science?
To govern and lead a business, we must always carve out time to put our lab coat on and observe, reflect, think and experiment.
Why? Because if we want to be in a position to predict outcomes we need the scientific method - a framework designed and perfected to eliminate bias (as best we can), establish knowledge and make reliable predictions. In PwC's 22nd Global CEO Survey they found that organisations are “struggling to translate a deluge of data into better decision making".
Without the scientific method and time to think slowly, our understanding is limited and dependent on the cognitive biases and assumptions that our brain uses to react and navigate most decisions which we make automatically (thinking fast).
Whether we are leading a business of 1, 50, or 50,000, our responsibility is to ensure that we are making the best decisions to serve our purpose. Business Science applies the scientific method to gather and interpret information relevant to our business in order to turn it into business intelligence.
The number one priority for CEOs when it comes to data collection is in relation to customers' and clients' preferences and needs, however, only 15% find their current data comprehensive! To understand this better, we need the Scientific Method it will allow us to predict, what ideas will yield the best results and that will be based on empirical evidence.
So how do we systematically work through this?
Here's the steps:
Step 1: Ask a Question A simple step that is often overlooked!
When it comes to marketing to understand clients' and customers' needs you need to make sure you're not cherry picking from your data.
In my business, the Change Makers Collective, our focus is on consumer insight and brand growth. When we work with clients and stop to think about this step we often find that what was being measured isn't necessarily valid for answering the questions that stem from this.
Step 2: Research Existing Sources
The keywords here are BENCHMARK & CURRENT BEST PRACTICE.
Without knowing what is going on in your industry - how do you know what is "good", "normal" or "bad"? And how do you know whether something is a priority for innovation or chugging along just fine? Most importantly how do you know what outcomes to expect in order to determine the amount of time and money you invest?
What do people within the business think will work? What research can they gather to support why they think it will work? Look at reliable information for what's happening "out there" in your industry.
Step 3: Formulate a Hypothesis
Now it's time to play a guessing game! What's your BEST guess of what to expect based on the research and data you've gathered?
Based on benchmarks and best practice, what's your prediction for what you need to do - and what will happen as a result?
Document your hypothesis.
Step 4: Experiment Design & Recording of Results
Depending on what your question is and your budget - there's all sorts of ways this can go.
You could be testing advertising on Facebook to find what content most appeals to your market.
You could be running a beta test of an app you've developed. You could be trying out a new sales script.
The key with any experiment you run is you need to have 3 things:
1) An Independent Variable
What you are testing - like the new sales script or different email headlines
2) A Dependent Variable
What is changing because of the independent variable - like the number of sales made or the number of emails opened.
3) A Control
What you use to see if there is a difference between what you usually do and the test - in business, we usually use baseline data for this (eg. sales made using the old sales script or how many email opens we usually get) Sometimes our data sets will be too small to determine whether there is what we call a "statistically significant difference" between our test and our baseline or control - basically this means, is this difference because of chance, or is this difference actually tell us something about what we're testing.
So we do need to make sure we think about how much weight we put on the results of these experiments when dealing with small numbers.
Step 5: Draw Conclusions
Ok, so now we've got all this data - what do we make of it? Are there other explanations for what we observed during our experiment? Are there any confounding variables that we didn't consider? How does this inform what we do moving forward?
Step 6: Report & Debrief
The final piece of the puzzle is to document everything so that what we've learnt becomes part of your business intelligence and think about how we can continue to deepen this knowledge with ongoing practices for continued improvement and innovation.