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The data you already have can’t tell you how customers will react to innovations.<br>To discover if a concept will succeed, you must know how to proceed. <br><br>Find out more at: https://www.dtechsystems.co/resources/
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The Discipline of Business Experimentation
Innovations Innovations D Don’t Always on’t Always Pay Pay Off. Off. Sometimes They Fail Spectacularly.
Big Big Data Has Data Has It’s Limits It’s Limits It can’t always predict the future.
Innovations Innovations Need A more reliable way to evaluate new initiatives. Need Rigorous Rigorous Testing Testing
A RIGOROUS EXPERIMENT Relies on proven scientific and statistical methods. Has a sample size that will yield valid results. Tests one independent variable against a dependent variable while holding all other variables constant. Incorporates careful observation and analysis. • • • •
Five Keys to Five Keys to Good Experiments Good Experiments Purpose Buy-In Feasibility Reliability Value
#1 #1 Does the E Does the Experiment xperiment H Have a a Clear Purpose Clear Purpose? ? ave Start with a Strong Hypothesis Avoid if the Hypothesis results are weak • •
#2 #2 Have Have Stakeholders Agreed Stakeholders Agreed to Abide by to Abide by the the Results? Results? Weigh All the Findings Walk Away if They’re Negative • •
#3 #3 Is Is the the Experiment Experiment Doable Doable? ? Potential Roadblocks Too Much Complexity Costly Sample Size Operational Disruptions • • • •
# #4 4 How Can We Ensure How Can We Ensure Reliable Results? Reliable Results? Randomized Field Trials Blind Tests Big Data • • •
#5 #5 Have Have We Gotten We Gotten the Value out Value out of the of the Experiment the Most Most Experiment? ? • Invest in areas where the ROI will be highest. • Determine which components have a positive return.
It All Comes Down It All Comes Down to to Rigor Rigor Purpose Buy-In Feasibility Reliability Value
Experiment Experiment Checklist Checklist Purpose • What specific management action are we considering? • What do we hope to learn? Buy-In • What specific changes will we make on the basis of the results? • How will we ensure that the results aren’t ignored?
Experiment Experiment Checklist Checklist Feasibility • Do we have a testable prediction? • What sample size do we need? • Can we avoid disrupting operations at the test locations? Reliability • What measures will we take to counteract bias? • Would others conducting the same test obtain similar results?
Experiment Experiment Checklist Checklist Value • Can we do a targeted rollout focusing on areas where the payback is highest? • Have we implemented only the components with the highest returns? • Do we understand which variables are causing which effects?
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