Here are 2 experimental pitfalls you category email list need to know about: 1. Design should be proposed before testing and based on data You might be asking yourself, "If I have data from market research or customer analysis, why do I need to do an experiment?" It is indeed tempting to misuse the data to arrive at causal conclusions: either find data that supports your conjecture; Or if you can't find corroborating data, simply revise your conjecture. But we still have to use the data from a more systematic perspective, establishing a baseline in the form of hypotheses before category email list looking for evidence.
Of course, these sources are still crucial for category email list growth experiments, but it is more important to generate conjectures than to draw causal conclusions, which is the first benefit. There is also the benefit of using them to pre-validate your assumptions, which will greatly improve your success rate. 2. Do not perform multivariate testing in one experiment The most common pitfall in growth hacking experiments is testing too many variables in one experiment. Ideally, only category email list one variable should be set per test.
For example, only change the pricing category email list and keep all other variables the same. In doing so, changes in price can be a good explanation for differences in the performance of the data. If you change both the definition of value and the price, it is likely that there is no explanation for the discrepancy in the performance of the data. Also, testing multiple variables in one experiment requires more traffic and longer time to get significant results. To avoid these two category email list problems, most companies with low traffic use a phased test plan.