For this blog post, I will be reviewing our glossary terms from Marketing Analytics chapters twelve and thirteen.
brand recognition: the ability to confirm a prior exposure to a brand
This is something that I am familiar with but always find so interesting. When I think of the term brand recognition, the first thing that comes to mind involves something silly we do as consumers. For example, we don’t always say something like, “could you pass me a tissue?” We might say “could you pass me a Kleenex?” There are several different brands of facial tissue, yet when we say Kleenex, we all know what we are referring to. The same goes for Chapstick. Chapstick is a brand, not necessarily a product. So when you are going to the store to buy some Chapstick, you may just be buying lip balm.
breadth: the range of usage scenarios for a brand
As someone who has an interest in advertising, this term seems valuable to me. Marketers seem to always be looking to stretch that good quote, that good photo, or sound bite where someone had something positive to say about your product.
campaign life cycle: the process of creating and running a campaign through several stages
I have been the creator and facilitator of various social media campaigns throughout my job history. One in particular, for a yoga studio, is most memorable. The owner of the studio wanted creative ways to increase her following on Instagram. I suggested we hold a contest where users would post photos of themselves doing a particular yoga pose each day and then be entered to win a free yoga class from her studio at the end of the month. It was a great success! We had so much fun creating the content. It didn’t even feel like we were working.
Here is the yoga studio I used to work with – click me!
Bayesian statistics: application of evidence expressed in terms of degrees of belief to problems in statistics
I love learning about history and how different historians have affected how we do marketing today. This was particularly interesting to me, because it is a method that is still as relevant as it was created in the 1700s and 1800s. Here is a photo of the dashing young man responsible for such a theory.
Contrasts: linear combination of variables whose coefficients add up to zero, allowing comparison of different means among experimental conditions
Something that I personally struggle with when it comes to my studies is the idea that math is used in everything we do. I always struggled with math and now try to avoid it when possible, however I can appreciate the significance of it when it comes to graphing and analytics. Here is an article that talks more about contrasts.
False negative: results of the exploration stage of the A/B test lead to the belief that options A or B do not differ in their success, when in reality one is likely to outperform the other in the exploitation stage
The idea of a false negative always proves interesting to me in the same way that a false positive does. This can apply to virtually every decision making process, so being thorough and understanding your variables is significant.
In conclusion, we learned many new terms in the last two chapters from Marketing Analytics. Understanding and utilizing these terms in our day to day lives as marketers will only enhance our skills.