Author: espin086

Optimizing Marketing Investment to Reach Communication Goals

Resources The code and data for the marketing optimization found below can be found on my GitHub account by clicking here: Background The problem of optimally spending marketing dollars can be formulated in many ways. The goal of this post is to explain how to minimize advertising investment given a minimum communication goal for a given set of target populations. This post will leverage a constrained optimization framework to answer a simplified marketing problem, namely: how do can we minimize the marketing investment required and still reach our communication goals?  The solution to the marketing problem will be obtained via Linear Programming and the Simplex Algorithm. Data The data above represent the media channels available for the marketing campaign: Television and Magazines. The reach of each one unit of advertising per  media channel (e.g. one unit of TV reaches 5 million Boys, 1 million Women, and 3 million Men). The unit cost of each media channel (e.g. TV 600 and Magazine 500) and finally the marketing targets for the product being advertised in million  (e.g. 24 million Boys). I’ve saved these data to a Google Sheet which are then imported into R in the next section. Optimization Model The following questions represent a standard linear programming model specification, which is similar to the specification we plan on using in the empirical calculations in this post:     All the code for this analysis...

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Screening Stocks Based on Value & Optimizing Portfolio to Minimize Variance

The goal of this post is to introduce Fundamental Stock Analysis, specifically this post will focus on introducing key financial, operational, and equity based measures to select a handful of stocks out of thousands. The selection process aims to find a small group of stocks that should be considered as invest-able based on their fundamental performance. We identify healthy companies whose stocks price is consistent and offers potential for security and growth by using the rules outlined in the book “Computational Finance” by Argimiro Arratia which is based on previous work on the topic conducted by Graham’s work from 1973. Graham’s rules have been adjusted adjusted for today’s financial climate (e.adjusted for inflation) 1) Adequate size of enterprise: The recommendation is to exclude companies with low revenues, consider only companies with more than $1.5 billion in revenue. 2) Strong financial condition: Use the current ratio (current assets/current liabilities) to eliminate companies who are in a weak short-term financial condition, consider only companies with a current ration of 2 or greater. 3) Earnings stability: Consider only companies with positive earnings in each of the past 10 years. 4) Dividend record: Consider only companies with uninterrupted payments of dividends for at least the past 20 years. 5) Earnings growth: Invest in companies that have growth rates of  3% or higher in earnings per share (EPS) over the past 10 years. 6) Price-to-Earning ratio: Purchase stock if the stock is adequately...

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Minimizing Risk in a Portfolio of Assets

INTRODUCTION There are many instances in business where a portfolio of assets must be evaluated in terms of risk and rewards.  The key questions may be: “How much should we invest?” “What should we not invest in?” “What is the risk of different budget allocations and what are the expected rewards?” “What is the optimum allocation if we want to minimize risk?” etc. Similarly the concept of an asset portfolio can take the form of: Assortment of clothing for a retailer Chargebacks for a credit card processor Scholarship recipients Movies for a Hollywood studio Collection of stocks etc. The objective of this post is to introduce the concept of the Minimum Variance Portfolio.  The Minimum Variance Portfolio is an optimum allocation of funds across risky assets where the risk (variance) is minimized in the optimization.  The simplest example would be a 2 asset portfolio, such as a portfolio consisting of an ice cream shop businesses and a coffee shop businesses.  In this scenario, during the summer people will buy more ice cream but coffee sales will be lower during warm temperatures but during winter the opposite will be true.If the mix of stores in this portfolio is chosen in a way to reduce variance in revenue due to weather, it is theoretically possible to hedge against the risk of weather.  Basically, if one chooses the right number of coffee and ice cream store to minimize revenue...

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Measuring Marketing Effectiveness: Cobb-Douglas Production Functions

  Introduction, Data, and Program Measuring the effectiveness of a marketing channel is difficult due to the large amount of variables and other confounding factors. The field of Marketing Mix Modelling was first developed by econometricians to accurately estimate the impact of marketing on consumer packaged goods, since manufacturers of those goods had access to good data on sales and marketing support. This post is going to use concepts from microeconomics and econometrics to understand the effectiveness of Television (TV), Newspaper, and Radio on the sales of a good. These data come from the the textbook “An Introduction to Statistical Learning with Applications in R”.  I have provided these data along with the R program used to derive the marketing estimates derived in this post, please see the links below: Advertising Market Mix Modelling R Program   Marketing Production Function Production functions are used in economics to model the relationship between inputs and outputs.  Production functions are very flexible and have been used in various branches of economics.  Agricultural economists use production function to model how different inputs effect crop yields, educational production functions have been used to model how different classroom inputs effect children’s learning, and macroeconomists have used production functions to understand how labor and capital inputs effect the total national output. I’m going to use a production function to model how different marketing inputs effect sales,...

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Outliers: Statistically detecting influential observations in R

One of the difficulties in accessing the quality of an econometric or regression models is determining if any of the key regression assumptions have been violated.  Regression analysis contains several key assumptions in order for the results to actually be in accordance with reality.  In regression analysis one is trying to measure the impact of certain variables on an outcome that we are interested in understanding or influencing. In order to determine with a fair degree of accuracy how strong these relationships are a few assumptions must be made.  If any of these assumptions are violated then the precision of the estimates can come into question.  The goal of this posts is to explain what these assumptions are and most importantly how to test and potentially correct violated regression assumptions to obtain the most accurate measure of the phenomenon we are trying to measure. In particular, this post will focus on outliers, subsequent post will address other issues that can arise in regression analysis. ######################################## Outliers are observations that have a particularly large influence on the mean or average of numbers.  Regression after all is just an algorithm for estimating the conditional mean or the average impact of one variable on another.  Typically, when people speak of outliers they are talking about a one dimensional outlier, for example a really high priced home. However, regression analysis is a multidimensional...

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