>The more experiments you can run the more likely you are to understand and solve the needs of your users.
There is an interesting chapter for this in Donella Meadows thinking in Systems
Oscillations! A single step up in sales causes inventory to drop. The car
dealer watches long enough to be sure the higher sales rate is going to last.
Then she begins to order more cars to both cover the new rate of sales and
bring the inventory up. But it takes time for the orders to come in. During
that time inventory drops further, so orders have to go up a little more, to
bring inventory back up to ten days’ coverage.
Eventually, the larger volume of orders starts arriving, and inventory
recovers—and more than recovers, because during the time of uncertainty
about the actual trend, the owner has ordered too much. She now sees her
mistake, and cuts back, but there are still high past orders coming in, so
she orders even less. In fact, almost inevitably, since she still can’t be sure
of what is going to happen next, she orders too little. Inventory gets too
low again. And so forth, through a series of oscillations around the new
desired inventory level. As Figure 33 illustrates, what a difference a few
delays make!
Faster is not always better, especially in systems with delays. By reacting to initial cues prematurely you can easily end up in permanent disarray that simply turns into a chaotic feedback loop. It is often worthwile to upgrade patiently, so one can actually gauge long-term effects instead of reacting to noise. Changing the consumer experience is a conversation.