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Class recap 12/9

This week we dove head first into out simulations. These simulations are about a company named Blue that sells products including laundry detergent. The company has many competitors within the market that vary in value. The point of the simulation is so be able to use the skills we learned about market segmentation and tactics to create an approach for the company that would better its business in the future. The first thing we had to do was create a target market based on income, ethnicity, age, household size, and location. In doing so we had to run the simulation many times in order to look at certain aspect of each segment such as market share, revenue, operating profit, brand demand, and trade channels. After sorting through them to find which segment best summarizes who the company wants to sell to, we then had to gather facts and generate insights in order to figure out how to approach the production. After generating theses insights and forecasting sales we were able to come up...
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Class Recap 11/16/18

This week we learned a lot about how to further segment a market. On Tuesday we looked at the 4 P's of segmentation, RFM, and other strategies to segment a market. When a business student thinks of the 4 P's they immediately divert their brain the marketing mix; product, price, place, and promotion. With segmentation these 4 P's hold different value; partition, probe, prioritize, and position. Partition is the actual segmentation itself. This part focuses on the fact that one size does not fit all because there are many different intricate aspects of each consumer that makes them different from or similar to one another. This entails the behavioral aspect and how each segment requires different treatment in order to maximize revenue/profits as well as satisfaction/loyalty. The probe aspect is grossly overlooked in its importance. This is where you do the digging into what the consumer's attitudes about the brand are and what their shopping behavior is. This may seem sim...
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Class Recap 11/11/18

This week we went into depth about finishing up regression and then diving head first into survival analysis. All of this encompasses dependence statistical analysis. We have been looking at OLS and logistic regression and how each of them can effect marketing as well as what information it can provide. OLS or ordinary lease squares regression is meant to explain relationships between variables as well as predicts what might happen in the future. Logistic regressions are the same but their dependent variables are binary which means they can only be one of two options. Within logistic regression they're are many different summary stats that you can look at that will show whether the data provided is able to be used in order to lead to an effective response. First when looking at the model you must look at the variables that are in the equation as well as variable not in the equation and how they effect the data as a whole. When looking at this stat you are given the odds ratio. This...
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Class 2 Chapter 9

Interesting: 1.) The three typical uses of segmentation are finding similar members, making modeling better, and using marketing strategy to attach each segment differently. In terms of the first, this is when you find homogeneous members or finding those that are alike then seeing how they differ in term of satisfaction. When improving modeling you have to run a separate regression model for each segment and find the difference in the affect of the independent variables. When trying to attack the segments with marinating strategies you have to look at sensitivity. If a customer is sensitive to a certain subject it will change exactly what approach you will take compared to other segments. 2.) The four P's of strategic marketing; partition, probe, prioritize, and position. Partition is the act of dividing the market in to sub-markets to avoid the "one size fits all" concept. Probing is the aspect of finding more data but creating more variables. Prioritizing is the act ...
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Class 1 Chapter 6

Interesting: 1.) I think censored observations are interesting . They are observations where we don't know its status so the event has not yet occurred yet or it is lost in some way.You also can delete censored observations but you could potentially be throwing away valuable data because they still contain half the information at hand. This is what leads to partial likelihood. 2.)  I think it is interesting that you use logistic regression when data is periodic which means an event that can only occur at regular and specific intervals. 3.) The difference between descriptive and predictive analysis is that descriptive show what happened while predictive is a glimpse of what what change the future. Questions: 1.) How do you create more high-valued customer? 2.) Does LTV connect to LCV/CLV? 3.) If survival analysis is the better way to predict the future why is logistic regions used?
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Weekly Recap Nov. 2

This week we dove head first in Chapter 5. This mostly encompasses going over OLS regression and Logistic regression. The week before, with the Widgets data we received, we were able to get a first look at OLS regression by looking at a series of linear regressions outing marketing strategies affecting unit sales. OLS regression formally known as ordinary least squares is a way of showing how the dependent variable depends on the movement of independent variable and is minimizes the sum of squared errors. The dependent variables of OLS regressions are continuous. There are two purposes to OLS regression; to explain a relationship between variables and to allow prediction of what might happen in the future. After going through what OLS is and all that is carries, we looked at logistic regression. Logistic regression is very similar in the fact that it has variables, constants, and error but the main different is that its dependent variable is binary which means that the dependent variab...
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Draft Data Report

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After looking over the data provided to Widgets Inc containing 4.5 years of sales and promotional data for 5 regions, there were many insights created. The data proved to be relevant and consistent overall after fixing a few minor functioning errors. The data was split into three classifications; frequencies (categorical data), descriptives (scale/continuous data), and correlations. Each of these sections gave rise to a series of insights due to the numbers they derived. In terms of frequency data, I was able to come up with one major insight; there will be more data for the earlier quarters of the years than the later due to the fact that there is an odd number of data for months acquired since the time range is January to June. The descriptive data gave way to insights that most of the promotional budget was allocated towards media spending and the least amount was allocated towards SMS. The correlations data proves that as an increase in spending occurred in SMS, the advertising sp...
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