Digital Marketing Class

When studying digital analytics, there are five components essential to yielding accurate and effective results.

In all aspects of marketing, regardless of platform, decisions need to be supported by measurable data.

In order for experiment results to be accurately representative of a population, the sample size need to be a sufficient size. Statisticians can use a formula to find the ideal minimum sample size as it depends on the number of variables being measured. The larger a sample size, the more it’s patterns represent the total applicable population. Do you remember counting the number of each color m&m per packet in grade school? Just one bag was not enough to determine the true most-present color of m&m in every packet of the class.

Several aspects of time surrounding your test are important as well. Time of year, time of day, the situational factors surrounding your subjects that day compared to another, the time it takes to conduct your test, and more affect the results. Consideration of such factors are critical to keep in mind when formulating your tests. Answers and results provided can change depending on the subjects’ mood or how their day went.

Explaining attribution models could take up another entire blog on their own, but I will be making a brief introduction so as to not overlook the imperative subject. They work to identify points of connection between sales and interactions. Conversion values among different interaction paths is another way to describe the use of attribution models. When identified, future digital marketing strategies can be adapted to yield more favorable results.

Unbiased conclusions are the expectation and generally a rule for acceptance in the analytical community. Numbers don’t lie if a test is conducted properly. This includes taking into account the sample size and appropriate time-related factors. In other words, numbers cannot be idiotically refuted if the data is accurately represented.

Don’t be dumb. Digital analytics are necessary.