Posts Tagged ‘relevance’
People groan whenever I bring up statistics in relation to marketing theory. In reality, though, most marketing decisions are made based on numbers. Without some level of smart statistical analysis, you can’t make an informed decision based on your data and all of those research dollars are wasted.
Understanding the Bayesian average is one thing. Understanding how to calculate it is something different. Understanding how to apply it is something in a whole other league. So here’s a quick and simple case study regarding product feedback and comparing aggregate product ratings using Bayesian statistics.
A traditional average is easy to understand. If you take a group of people, add their heights together and divide by the number of people in the group, you know the average height. A simple average is a relatively easy way to create a prediction for future behavior – in many cases, you can reasonably assume a new person entering the room would be at or near the average height.
Most statistics are based on solid, static data. The average for a group of numbers is independent of what numbers are actually included in the group. Statistics give us a snapshot of our data so we can make high-level decisions based on it without knowing the details of each discrete measurement. This simplicity makes statistics powerful indicators in business, but it also betrays their weakness.
In our careers as marketers we are often presented with problems that require some kind of statistical analysis. One of the most frequently-faced issues is that of content or quality ratings.