Let’s imagine we are a milk bottling company. Our customer is a food retail chain as they pay us for our product. Our end-user is a paying customer for our bottle of milk at the food retail chain and is not a customer of ours. We’re getting bad press about the volume of plastic in our milk bottle production, we need to cut costs, and we feel guilty about our impact on the planet.
We don’t want to go back to the bad old days of breakages. We might need to run an experiment to investigate the viability of toughened low-cost recyclable glass bottles with no foreseen public health side effects. We might have an idea to test based on new glass production technology. But we don’t want to go too far down the road with a solution without proving the concept, potentially wasting lots of money and time. So we try to prove the production technology concept and see what customers and end-users think about it. We think it is better to find out sooner if we need to persevere, pivot, or stop.
If the proof of concept does not work, the learning is knowledge value. If the proof of concept does work, the learning is knowledge value. We might need to run more experiments, eventually experimenting with our potential new glass milk bottle product with the certifying authorities with larger batches, and so on. After proving concepts, the team can turn its focus on sorting out the problem with the implementation of that solution.
If it makes the desired impact on the customer’s desire to improve its environmental record, that would be customer value. If there is the desired impact for milk bottle purchasers, that would be end-user value. The customer value would entail patent application, approval by certifying authorities for public safety and the environment, plus sustainable costs without unintended consequences. If the environment also improved as a result of a trend towards these new glass bottles, that would also deliver societal value. If these new bottles really catch on and we have patent protection then we might pivot to glass bottle production, which would generate huge customer value from customers we don’t know about yet, and it would generate huge societal value as a consequence.
Sometimes, we have lots of ideas, and we need to experiment with some of them in parallel to let the data do the talking. Whatever the outcome as long as we release to learn, there is knowledge value from running experiments.
In this case, organizational value can increase from the improved inspiration of employees, customers & shareholders, improved financial health, and improvement in the organization’s social reputation.