There are 4 easy steps that anyone doing Digital Analytics must follow.
The first step I am talking about is ensuring our sample size is large enough before running any tests. If we are running a test for a new product that is about to launch into the market we need to make sure that we are testing a wide range on individuals. If we test 10 individuals and they say that they hate the new product, does that mean we shouldn’t launch the new product. Absolutely not, just because those individuals might not have liked the product doesn’t mean that they represent everyone. Our sample for any test has to large enough to identify a proper representation of the population.
The second step is ensure that we are testing long enough. This implies that when we test our product that we are giving individuals enough time to fully evaluate the product. If we run the test short then we are not get accurate information, and if we run it too long then we might get over saturated results. We have to find that perfect middle ground to get the best results in terms of length.
The third step is to use the appropriate Attribution Models. There are 5 main models: First interaction, last interaction, linear, time decay, and position based. Each of these have different point in which we assign credit for when ever one of our products if sold. For example, first interaction we give credit to the first location our product was seen. Another one like linear for example is where the credit is equal distributed across all place the buyer went to get our product.
The forth and final step is making sure conclusion are unbiased. The basically means were your test results based on nonbiased answers and responses. Was the group that you tested a random sample of individuals and not individuals that already love or hate your product.
