Design a Marketing Experiment Sample Report 

 Introduction 

 This report fundamentally aims to develop a marketing experiment for Instituto´s drinks by The UTEC Uruguay Company . The campaign is all about TV advertising campaign highlighting “ feeling the taste ” . With   the growing taste consciousness among the audience , the taste of drinks is increasingly becoming an   important consideration of the consumers . The experiment is going be a before – after design   experiment . I am not going to focus on full factorial , as it will be difficult & time - consuming to quantify   impact on sales if several parameters are changed at the same time . 

 Experiment Design 

 In this experiment , the independent variable is increased TV advertising campaign . On the other side ,   the dependent variable observed here is amount of sales . The experiment will be run in Philadelphia ,   PA . The control market observed will be San Antanio , TX . Both are the two resemble cities of USA . The   test and control markets are so chosen that they resemble closely in terms of population , demographics   and other city attributes . Both Philadelphia and Antanio are similarly sized cities with a population of   around 16 , 000 , and 00 . Since the entire cities will be exposed to the experiment , the sample size would   be big enough to be statistically significant . Since it is a before - after design experiment , sales will be   recorded for both control and test markets for a 3 month average from June - August . The experiment   will be run for the next three months from September – November . The change in sales during the   experiment will be then used to calculate the lift in sales . The experiment certainly adheres to the first   three rules of causality . However , the experiment has no control over the fourth rule , which deals with   the presence of external factor . Prima facie , there appears to be no external factor influencing the   experiment , however there might be competitive response that shall be accounted for . 

 Anticipated Issues 

 Both the before and during experiment results are collected during the summer months from June to   August . If the field implementation is executed anywhere between September to November , the winter   months might see the dampening in the impact due to the seasonality involved . Any other external   factor during implementation such as a new competitor entering the market or decrease in prices from   competitors could adversely affect the field results . 

 The above - mentioned issues might have an adverse impact on the sales ; however , the experiment will   still demonstrate the impact of TV advertising on sales . The seasonality and external factors   notwithstanding , the experiment will provide a fair guideline on whether to go ahead with the   nationwide marketing campaign . The lift in sales would also provide a concrete idea on the appropriate   amount of spending on the campaign . 

 Experiment 2.0 

 Another version of the experiment can be through paid web advertising , which is mostly used , now days   in the marketing . Web advertising is so popular because it provides much better control on the target   audience , test markets and analytics . Hence , it can be a full factorial design with the independent   variables as both price and advertising theme . This will also yield or generate more informative results   such as : 

  It demonstrates the impact of more than one independent variable     The web analytics gives far more insights on the buying preferences of the audience .     This version of experiment is also generally cheaper and quicker to implement than the offline 

 campaigns such as TV or billboard advertising . 

 The only concern with the web experiment is that it does not include the chunk of audience that is   not so active on web . In the context of this particular product , that chunk of inactive users could be   huge . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Design a Marketing Experiment Sample Report 

 Introduction 

 This report fundamentally aims to develop a marketing experiment for Instituto´s drinks by The UTEC Uruguay Company . The campaign is all about TV advertising campaign highlighting “ feeling the taste ” . With   the growing taste consciousness among the audience , the taste of drinks is increasingly becoming an   important consideration of the consumers . The experiment is going be a before – after design   experiment . I am not going to focus on full factorial , as it will be difficult & time - consuming to quantify   impact on sales if several parameters are changed at the same time . 

 Experiment Design 

 In this experiment , the independent variable is increased TV advertising campaign . On the other side ,   the dependent variable observed here is amount of sales . The experiment will be run in Philadelphia ,   PA . The control market observed will be San Antanio , TX . Both are the two resemble cities of USA . The   test and control markets are so chosen that they resemble closely in terms of population , demographics   and other city attributes . Both Philadelphia and Antanio are similarly sized cities with a population of   around 16 , 000 , and 00 . Since the entire cities will be exposed to the experiment , the sample size would   be big enough to be statistically significant . Since it is a before - after design experiment , sales will be   recorded for both control and test markets for a 3 month average from June - August . The experiment   will be run for the next three months from September – November . The change in sales during the   experiment will be then used to calculate the lift in sales . The experiment certainly adheres to the first   three rules of causality . However , the experiment has no control over the fourth rule , which deals with   the presence of external factor . Prima facie , there appears to be no external factor influencing the   experiment , however there might be competitive response that shall be accounted for . 

 Anticipated Issues 

 Both the before and during experiment results are collected during the summer months from June to   August . If the field implementation is executed anywhere between September to November , the winter   months might see the dampening in the impact due to the seasonality involved . Any other external   factor during implementation such as a new competitor entering the market or decrease in prices from   competitors could adversely affect the field results . 

 The above - mentioned issues might have an adverse impact on the sales ; however , the experiment will   still demonstrate the impact of TV advertising on sales . The seasonality and external factors   notwithstanding , the experiment will provide a fair guideline on whether to go ahead with the   nationwide marketing campaign . The lift in sales would also provide a concrete idea on the appropriate   amount of spending on the campaign . 

 Experiment 2.0 

 Another version of the experiment can be through paid web advertising , which is mostly used , now days   in the marketing . Web advertising is so popular because it provides much better control on the target   audience , test markets and analytics . Hence , it can be a full factorial design with the independent   variables as both price and advertising theme . This will also yield or generate more informative results   such as : 

  It demonstrates the impact of more than one independent variable     The web analytics gives far more insights on the buying preferences of the audience .     This version of experiment is also generally cheaper and quicker to implement than the offline 

 campaigns such as TV or billboard advertising . 

 The only concern with the web experiment is that it does not include the chunk of audience that is   not so active on web . In the context of this particular product , that chunk of inactive users could be   huge .
### Output: 