A Checklist for Making Data-Based Decisions
Ensure your Sample Size is Large Enough
In order to properly collect data from a population, researchers must survey an appropriate sample group from the entire population. This is to make sure that the information is relevant and correctly represents the demographic or experimental population. Marketers have discovered, the larger the sample size, the more accurate the information, but it also increases the cost. Finding statistically significant data is essential in providing information in the marketing decision making process. Decisions must be backed by accurate data and information.
Make Sure Your Testing is Long Enough
The testing period should be a fairly lengthy process to ensure that the proper sample size of a population is tested. Although, the testing shouldn’t be dragged on in hopes of more data that would lead to favorable or biased results. It is detrimental to results, and will reflect it in the long run, to cut testing short.
Use Appropriate Attribution Model
Attribution modeling offers an outline for marketers to analyze conversion. Last Interaction credits the whole lead to the last interaction the business had with the customer. Vice-versa, the First Interaction Model gives credit to the first touch point. Last Non-Direct eliminates any kind of direct actions before communication. Linear attribution splits the conversion across all interactions. Time Decay spreads the conversion out based on importance leading up.
Make Sure All Conclusions are Unbiased
Although information and data are accurately displayed, researchers can report biased opinions that are used to set false narratives.
Hi Mathew,
Great featured image and excerpt! it really made me want to read your blog. I truly believe that when you have a goal it is very important to have a to do list for making data-based decisions. You mention 4 points that are very important. 1. sample size needs to be large because it is a small sample size it might be biased. 2. Make sure your testing is long enough, which means that the testing period should be lengthy just to ensure that the proper size of thee population is tested. 3, use appropriate attribution model to analyze conversion. and last but not least, make sure conclusions are not biased! but based on research and data. Good job.
Great title and featured image! You organized the information nicely, but I do think bullet points or even white space between the sections would help the reader stay on track.
Your featured image is what caught my eye and your checklist is super insightful!