For class September 27th

For class Thursday we had to read a lengthy article focusing on social media presence, and how social media advertising can effect television, and specifically the super bowl. Because social media is beginning to hold a lot of power in marketing and brand awareness, it has a major role in effecting consumer emotions for super bowl commercials, as well as just the consumer in general.

One thing I found interesting throughout the article is how specific advertisements may have an immediate influence on internet search activity when the TV advertisement is coming from a huge event. For example, the commercials surrounding the Olympics had immediate  on and off line purchase behavior with its commercials. What I found to be most interesting about this is that the author says that the "moment of truth" feeling we talked about Tuesday almost becomes diminished. Instead of their being a moment of truth, consumers just go immediately to google as their first search. I think I found this interesting because regardless if I'm watching the olympics, the Super Bowl, or just a daily rerun of ESPN, I too just google exactly what I'm looking for after commercials have been aired and I like a product I saw. I can take this personally because yesterday I bought a pair of shoes after seeing a commercial for it and google was in fact my first search.

Another thing I found interesting was the researchers educated guesses on what would happen with the Super Bowl advertisements. Because of the high elevated status and focus on the Super Bowl and its commercials, the authors expected that social media will positively influence viewer engagement on game day, and after the game has been played. I found this to be interesting because before I continued reading to see the results I immediately agreed with what their expectations were from personal experiences with the Super Bowl, and it simply makes sense that viewer engagement would be positively influenced by the brands and commercials. The results found were that the commercials on that day overall focus on the execution of the commercial, and the brand itself. I found no surprise by this because throughout younger life in elementary school through high school, the first question teachers ask are what commercials were the best and why? This engagement is proven true as kids throughout the class remember the ones that had an influence on them, and were often big name brands that kids would remember the next day. After this part of the article it has confirmed with me that the Super Bowl still generates massive buzz with its commercials and the brands that do it right often get remembered and talked about during and post game.

The third interesting point of the article in my eyes, were the results that ended up being given from the Super Bowl. The results stated that, 60-75% more people liked watching super bowl advertisements over regular television airings, and that 23% of viewers actually said the commercials are more important then the game. The last result that stuck with me is that over 70% of people said that they pay more attention to Super Bowl commercials over regular commercials. I found these results to be so interesting because it was nothing that I was surprised about. Personally, when I watch the Super Bowl and my favorite team, the LA Chargers, aren't playing, I focus more on commercials and advertisements too. I feel that many people are like this, and if it isn't because of their favorite teams not playing, some people may watch the Super Bowl that have no real interest in who wins or football. The results showed that tweets, texts, and social media conversations hold high value when it comes to brands and their Super Bowl commercials especially when compared to regular television advertisements.

3 questions:

Is the rarity behind Super Bowl advertisements and engagements actually because of brands, or is it because of the past where people just expect to see better and more numerous commercials on that day?

 With NFL T.V ratings decreasing will the buzz behind social media engagements and Super Bowl commercials ever reverse negatively? or will it continue to soar in the future?

Will a new brand ever make a commercial and make a splash on social media on Super Bowl game day  or will it continue being big brands like Doritos, Pepsi etc?

For class September 25

The assigned readings for this class session are all focused on the consumer. The consumer purchase decision and journey is what decides everything about the success of a marketer, so its very important to me and to everyone learning marketing. Three things that really stuck out at me during the readings were the history behind the consumer decision journey, the new use and importance of E-
WOM (Internet word of mouth) and company " moments of truth" which basically give a company the chance to make an impression to customers.

The first thing I found really interesting in the readings were the history behind consumer decisions. I had always thought that marketers and researchers had the upper advantage when it came to influencing consumers, but apparently that is not true. Because consumers had the leg up on purchasing power, companies have made recent changes to try to change that, where their marketing can influence people to buy. The updated action that businesses are taking include 4 steps that ultimately target the consumer. Those four steps include, engage experience through technology, use information about a customer, use knowledge about where the customer is in their journey, and to extend interactions through value and services.  Because customers have have the power right now, its very interesting to see how businesses can overcome that to gain control.

Another thing I found interesting in the required readings was the E-WOM, or internet word of mouth, and how big it plays in the consumers journey to purchasing. The biggest thing with the power of the internet word of mouth is that consumers are going away from traditional marketing and promotions, and simply wanna hear their peers experiences before purchasing similar   products. Because of this, brands are pressured to communicate through social media, as well as create really good products that are going to have high reviews and praises so other consumers buy. With big businesses like google+ and Facebook jumping in on the internet word of mouth, it is extremely important for a company's products to be good their, where millions of active users could be potentially listening to others experiences.

The last thing I found very interesting was company " moments of truth" that happen and ultimately define certain experiences. The Moment of truth is the contact point between companies and the consumer that overall give them a chance to make an impression. This is very important because like stated, its the "moment of truth" in which a company will bring a new customer in, or potentially fail to a competitor. What I found most interesting about this idea is how all big name companys are using moment of truths, like Amazon, Google, Facebook, and Proctor and Gamble, which all have heavily influences online.


3 questions:
If big companys like Facebook, Amazon, Google, and Proctor and Gamble use the importance of moments of truth and contact experiences, why wouldnt every business follow this model for success?

If customers have been in the drivers seat when it comes to purchasing power and influince, does that mean marketers have overall failed in recent years?

Will electronic word of mouth continue trending upward as the new way that consumers will purchase? if so what does that mean for businesses and their competitors?

What I learned Week 4

A different week of classes for me, I unfortunately didn't make it to either class due to being sick. With this being the case, I feel I probably lost a lot of valuable class time with what we had learned, and judging by class recaps it seemed everyone learned cross tabulation analysis.

Cross tabulation analysis seems to be a very important form of analysis for managers. The class example that got used was for Oreos, where the class used Simmons online database to find numbers on Oreos and learn how to interpret the numbers that they see. After extra lab sessions to catch up I will understand how to read Simmons data better to be fully caught up to speed with the rest of the class.

Because I didn't get to fully learn what my classmates did this week, another thing I learned from our readings was the consumer decision theory. This really focused on the consumer, and what is going through their heads as they purchase new products, and how marketing needs to change in order to continue being successful. The ultimate goal of marketers is to get consumers to purchase at the moment they are most influenced, so by understanding the customers better, the better position the marketer is in. One thing I found most important in this reading was the importance of touch points. Touch points are different things like advertising, news reports, commercials, or even just conversation with family or friends, it ultimately decides when a product "touches" the consumer it is trying to reach. I believe this was most important because to understand a customer and their behavior I think its even more important to understand how to reach the customer with products.

This week was a weird one, and I look forward to getting back to class action to not only catch up, but dive more in depth into the analytics and numbers that are class is moving forward into.

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.

Readings for 9/13/18

For our class discussion Thursday we were assigned to read chapters 9, 10, and 11. Chapter 9 talks about predictive analytics and how businesses use them regularly . Chapter 10 talks all about regression analyst, how they are used, their benefits, and errors that commonly get made. Chapter 11 focuses on correlations and the risks behind correlations with business's.

One thing I found interesting in chapter 9 was the use behind predicative analytics and how they are used more often then I thought. The reading gives examples like Customer lifetime value, and forecasts of sales for the next quarter, and I never realized how important this is in every business. I made a personal connection with this aspect of chapter 9 because I'm a very habitual buyer, with products and the store I get it from. Because of this, I see how important I am as a customer from the various places I spend my money at. Whether it is gas for my car, deli sandwiches, or the clothing I wear, I am very important to the customer lifetime value of the stores I go to. One thing I didn't fully understand was the data aspect of the predictive analytics. The author says that its important to make sure you use good data, but how does one specifically understand what is good data or bad? Without the knowledge of knowing what is good data or bad, I feel like the risk would still always be there to analyze it.

Another thing I found interesting in chapter 10 was the example used to explain regression analyst. Not fully understanding how regression analyst are used or when they are supposed to be used, the example broke it down to help me learn. The example used a business manager  trying to predict next months sales. I now understand why regression models are so important for business's as the variables that relate to the cause almost usually show a correlation. In the example there was a clear correlation with sales and rain fall, which directly effects their sales and how they go about promotions. The question I have for chapter 10 is the reading says that you must be specific to the data analyzer, but in what ways can you be most specific to not run into problems?

Chapter 11 teaches the importance of a correlation and when to act on it or not. What I found most interesting was the two most important reasons to decide if a correlation is worth taking a chance on. Those reasons were that confidence that the correlation will reliably happen again in the future, and the trade off between how big the risk and reward is. I found these important because I didn't know that before, and it stuck with me when thinking about future business risks myself. One example I think of is real estate, where my best friend and moms boyfriend are both heavily involved in. Correlations within the market, what other houses were sold for, and what are other houses are available definitely have a huge role in deciding what properties people end up with next. The question I have for this chapter is one chart shows that its worth it to go through with correlations when the benefits outweigh the risks, but is that always the case? is their ever cases of correlation where everything can look perfect but acting may still not be the best idea?

Readings for 9/11/18

For class today, we had to read chapters 1 and 11 of Marketing Analytics by Mike Grigsby. Chapter 1 was an introduction to marketing analytics and market science, while chapter 11 focused on A/B testing and just market tests in general.

Chapter 1 is the author introducing market science, and his take on it with his beliefs of why it is so important. One thing I found very interesting is in one of the first paragraphs, Grigsby says, "Any ‘marketing’ that is not about consumer behaviour (understanding it, incentivizing it, changing it, etc) is probably heading down the wrong road"(Grigsby). This stuck with me because as a marketing student that makes a lot of sense to me and every business's marketing should be based on those behaviors. Another reason I found interest in that quote is because it led me to my first question which is, if it is super obvious why marketing science is more efficient and leads to more answers, why is not every business using it at this point? If chances of making a wrong decision can be diminished or decreased, then I feel that every business that promotes and markets themselves should be using it. 

Another thing I found interesting about the assigned readings were the authors opinion about testing in chapter 11. The author makes it clear that he himself isn't a big tester, and doesn't always like the results they give. He goes more in depth to say that sometimes the test ends up getting "dirtied" where the test consumers receive stimuli they weren't supposed to. Grigsby describes the sometimes often testing error and says that the number one rule is only one thing can be different in measuring test vs control. This leads to my next question, which is if testing could go bad at any point and happens somewhat often, why would it still continue to be a go-to idea for any business? I didnt seem to understand where if their could be common error, why companies would still use it. 

The final thing I found interesting on chapter 11 was the recap on A/B testing. We've already talked about this in past classes, and the book touches up on it and makes it even more clear on how it works. I was interested that the point of A/B testing is to only change is that the test cell has treatment while the other cell doesn't. I found interest in this because that information then gets tested and if their is a significant difference between information then it becomes worth it to test at a larger population. My final question to this weeks readings is chapter 11 touches on both lift measures, and A/B testing, which one is better and why? This was unclear to me after reading through because it seems like the author favors A/B testing more, but why? 

Week 2 Reflections

9/9/18

In this weeks classes we discussed the basic analytic process after reading about it in chapters 4 and 6 of Harvard Business. We learned that their is four steps to this process that include 1. Ask a question 2. Figure out what data will be needed to answer the question 3. Where will the data be found and 4. ultimately answer the question and communicate the results. We learned that we must be as precise as possible, and that its extremely important to be as specific as possible in both the question and the metrics used to answer it.

We further analyzed the basic analytic process by bringing it back to the ping pong games we played our first class meeting. We worked with partners to ask a question and analyze the question asked in relation to our ping pong game. This is where I learned just how important it is to be as specific as possible with data and metrics because after thinking about what metrics I should be using and got as specific as possible, my partner and I ended up changing our question entirely.

Another thing we did for was MBTN's tutorials, where our first tutorial was on percentages. This was a good first tutorial to get the class used to seeing numbers and percentages, as I assume they will become more apparent in our class in the future. I found this to be interesting because all the tutorials are based on concepts and I'm excited to see what other concepts we will learn throughout the semester.

The basic analytic process was what I found most interesting this week, just beginning to understand how important analytics are for business's to be efficient and successful, I have noticed I have had to use it myself with my business partners in our e-commerce store. In the drop shipping/ wholesaling business, profits are never guaranteed and often have swings month to month depending on the products being sold. After some monthly declines, we asked the question should we ultimately change all the products we sell? With metrics from website visits, advertising mouse clicks, sales, and products being sold, we ultimately changed what we sell online from clothes, hats, and accessories to kitchen, outdoor, and garden. The data analytic process showed me how business owners have to think daily, and I'm looking forward to what else I can learn in class to continue improving my own business.

Thursday 9/6/18

Thursdays readings assigned to us were chapters 4 and 6 of Harvard Business Review. Chapter 4 focused on how to ask for data from data scientist the correct way, while chapter 6 focused on knowing and understanding the differences between data and metrics. I found both chapters interesting in their own ways, and I was able to make some life connections with chapter 6.

One thing I found really interesting in chapter 4 was right in the first two paragraphs where the author explains that many individuals of business's and organizations don't know how to  request new data or analytics from data scientist. This stuck with me because wanting to own my own business's in the future, I have never even thought about these aspects of business before. It was quite eye opening to me, and then the author further explains how to fix these individuals problems with information based on what questions to ask, what data is needed, and how to obtain the data gathered. I thought obtaining the data was especially important because I learned that before investing in recourses into new analysis it should ensure that the information gathered will be productive and meaningful. 

Another interesting thing I came across during these readings came from chapter 6, where a Youtube video is the prime example throughout the chapter to reveal that you can't pick your data, but you can pick your metrics.  In the Youtube example, dosomething.org released a video in hopes to get people to donate their old sports equipment to kids that didn't have any. After record breaking views, 1.5 million, you would think it was a whopping success except for the few 9 people that signed up for it with a total of 0 donations at the end. I found this particular  Youtube example to be very helpful to understand the difference between data and metrics because it was clear that their metrics were wrong somewhere. 

Another really interesting thing I took away from chapter 6 was the difference between vanity metrics and and meaningful metrics. Vanity metrics are the ones to be considered maybe good for certain things, but not what we want, while meaningful metrics are the ones that serve a purpose and do not waste recourses. The example used for vanity metrics was obviously the Youtube video explained before, but also simple examples like an organizations Twitter followers, Facebook fans, media impressions etc. Although sales may go up while these numbers go up, if their cant be any proof behind it then they are considered vanity. This really stuck with me as a learning lesson because I experience it every day in my own life while owning an online e-commerce store. Being in the e-commerce business myself I notice daily that Instagram followers, Facebook likes, and different social media platform impressions do not guarantee any sales by any means. Some products may see extreme exposure with no sales, while others will see half of it with hundreds of sales. 

3 questions

My first question is if 95% of the worlds data is unstructured and requires lots of money and recourses how do organizations and business's know what data is clean and where to find it?

Another question is in the case of vanity and meaningful metrics, if Twitter followers, Facebook likes, and media impressions work part of the time but not always, how would you decide how resourceful it is? 

My last question is in terms of the K.I.S.S rule for the author states "it may not be possible to avoid all expenses... by asking the right questions of your analyst you can ensure proper collaboration and get the information you need". In this particular paragraph, what are the "right questions" that need to be asked to decide if the model is too complicated or not?

Week 1 chapters 4 and 5

One question I had from the reading is in Chapter 4, specifically when the author is talking about marketing analytics, and metrics. My question is that if their are so many hundreds of metrics for marketing, and all of them cant directly prove what marketing worked because its indirect from sales or revenue, then how do we know what is the best to use or most efficient to use? I understand that by using more metrics only more results will come of them, but their is so many metrics to use how do we know which is going to  be most efficient.

Another thing i found interesting was the process of A/B testing. An unfamiliar term with me until today, I'm confused about the process of the A/B testing and what the consumers see on their end. My question would be do different consumers see different variations of the same screen, or do they release one look and then the other when testing to see which would be better. I understand the idea behind A/B testing, and how the results are measured  but I am confused about how they get presented to the consumer during the test.

Another thing I found interesting was the barrier culture and catalyst cultures in organizations. I thought the idea behind that made a lot of sense, but my one question would be how do businesses just simply make the change from barrier to catalyst? Would their be barricades along the way while switching, and would it be a slow process that would have to be eased into to successfully change? Especially when coming from business's that already are used to doing work on their own intuition. 

week 1 Reflection

From class discussions and the readings, I have already learned some new concepts and ways that analytics get used for business's and marketing. Some big concepts that I didn't understand before hand were how metrics were used and how the best ones were chosen, A/B testing, and barrier cultures vs catalyst cultures when it comes to analytics.

Metrics can be calculated in so many ways that I learned its important to only choose the metrics that are going to help for what you are trying to accomplish. Because their is so many, it would be easy to use and analyze metrics that don't help an organization ultimately figure out how much the marketing and metrics are working.

We also discussed A/B testing extensively in class and I learned that, that is the idea of showing two different ideas to a consumer and seeing the results as to which one does better. One consumer may see one thing while other consumers see others and the data will show to which one is more successful. An example of this would be when a laundry detergent company uses a red label and a blue label to sell a product. At the end of a period of time one colored product label will do better than the other. Another example of this is on a website, the controller may only choose half the consumers to see the website one way, while other consumers see a different variation of it to get information on which site should be used.

The last big take away from class discussions and reading was the barrier vs catalyst cultures to using metrics. I found this to be really interesting because some organizations see metrics as not as important, and may only use few or no metrics at all to analyze data, while other organizations  use it rigorously to continue bettering their business. Those who use it constantly use metrics as a catalyst to help boost success, and those not doing that are considered to be the barrier.

First reading and class discussions were very interesting as I got to learn a little about metrics and the importance of them before we dive into the class for the semester.