What I learned week 3

This weeks classes got the deepest they have yet in material, and we even took a full class to go over absolutely any questions that the class had. I wasn't able to attend Tuesdays class, but I understand that the class had a discussion about the interesting things they learned from chapters 4 and 6 of the HBR guide,  as well as an article that we were asked to read called "The Impact of Publicity and Advertising on Marketing and Company Performance." Thursdays class we played a little game to decide who would be asking the questions. One person would roll a die and the number it landed on would be the person to ask a question. Different questions were asked about sentimental analytics, linear regressions, correlations and causation, and it gave everyone an overall better understanding of the concepts.

The main things I learned in Thursdays class was the above concepts, although its stuff we learned in earlier chapter readings, the class discussion ensured confidence in understanding the material. Sentimental analytics is an algorithm that takes all data such as impressions, Facebook likes, tweets, shares, etc. and decides if the data they receive could be used positively or negatively. This was really crazy to learn because marketers do this on a daily basis and I know my own personal information every day gets analyzed and put into an algorithm to help push products of my own interest my way. The biggest example I can think of personally is when I will google something to find info on it or a price, and immediately their will be promotions for it while I search through Instagram posts. Once understanding the concept it becomes much more apparent in every day life.

We also talked about correlations with data, and how two variables can either have a complete correlation and one causes another, or there could be none at all. This information is important because when it comes down to taking risks using data, it is extremely important to understand if that data actually shows what you think it is. An example of correlation is for instance, when the weather is hotter, more people buy ice cream. With this information you know more ice cream gets sold in summer months. Although this is an obvious correlation, marketers and business owners analyze any data that helps them figure out correlations in their business. In my own online business, I have seen correlations with products we sold. We went from selling clothes and accessories, to kitchen, outdoor and garden products and saw a 20% increase in sales. The correlation was simple, it was easier to drop ship other products then clothes because people are afraid to buy clothing through drop shippers and wholesalers when theres many trusted company's already.

Overall Thursdays class discussion was very helpful and made the material we have learned so far more clear. I expect this next week of classes to be longer and even more in depth to marketing and analytics and I look forward to learning more about how marketers use data in ways to improve efficiency and overall profit.