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Lars RöwekampMicroservices Migration Guide: From Monoliths to Microservices with Lars Röwekamp (Open Knowledge)From this we derived a new hypothesis or, as it turned out, actually two new hypotheses. Presumably, the users were looking for the common visible watermark for images and found on our site not directly what they were looking for.
The associated hypothesis was that we only had to alert users to the benefits of invisible watermarks in order to significantly reduce the bounce rate from our homepage and eventually attract more paying users. The experiment was quickly defined: it had to be a new homepage, which should inform about the benefits of invisible watermarks. After we put this page into operation, an unexpected effect occurred: The bounce rate increased to well over 80 percent. Apparently, only the first of our two hypotheses was correct, but the second was not.
The users were actually looking for visible watermarks, but were not convinced by our homepage to use invisible ones instead. Maybe we also misjudged the customer’s problem. Although they had searched for “watermarks” and “photo”, they probably did not want any copyright information at all. Or it was too expensive for them. It could also be that they thought the watermarks cost money, but they were looking for a free service. Thus, the next experiments resulted directly from this experiment.The team should never forget to measure the results of its experiments.
The example shows an important aspect that is often forgotten: Only by a meaningful measurement could it be determined whether the experiment was successful and the hypothesis was correct. This results in two consequences. On the one hand, the measuring points for checking the experiment should be part of the user story. Either there is a separate category on the story card or the measurement points are included in the acceptance criteria – after all, these criteria decide when a story is completed.
On the other hand, the definition of “completed” changes: A story is not finished after the end of the development, but when the hypothesis has been checked. For this purpose, there should be a separate column “in measurement” after the column “development completed” on the task board. This column contains all ready-made stories for which the team is currently collecting data. The hypothesis described in the user story can often not be validated immediately.
It makes sense to collect data at least until the next Sprint Review.The collection of data or measurement can be done within the application or z. In Google Analytics. For this purpose, it is useful to provide the stories in the January Calendar 2019 Free Printable column “in measurement” with information on when the change went productive (see Fig. 4). Otherwise, it will be very difficult to understand later if changes in the measurement data are related to a user story.