Case Study Review
Hallo Multiple measurements were collected Including household size and previous purchase behavior as well as pre-ad exposure, during exposure, and post-exposure data to determine if the individuals exposed to ads are more likely to purchase than those unexposed to the ads, and to determine the percentage of exposed households who actually purchased the advertised product. Each metric was tracked per advertiser by Consumer Direct. The pre-ad measurements consisted of data collection for both groups during a 52-week period to measure purchases within the category of the ads prior to exposure.
After a year, the eight week ad exposure period began, and purchases were tracked. Data tracking continued for another six weeks following ad exposure. The ads were considered to Increase sales under two conditions: (1) purchases Increased after ad exposure compared to data collecting during the 52 week pre-ad data collection, or (2) purchases Increased after exposure compared to the control group’s purchases. In addition to the collected measurements, an analysis of covariance (ANCHOR) was used to compare the ad- exposed group to the control group.
ANCHOR is simply a multiple regression analysis tit at least one quantitative and one categorical variable. In the Consumer Direct study, the control group is considered the quantitative variable and the test group is the categorical variable also known as the primary interest of the study.
A matched subjects design was also used as it matches each subject in the control group with an equivalent in the test group effectively matching households based on size and previous purchase history.
More than one variable can be matched which In this study Includes both household size and prior purchase behavior. Matched subjects sensing decreases the possibility of differences between Individuals that can skew results (Shuttlecock, 2009). Explain the appropriate analysis for data collected through Consumer Direct A Tolerate analyses would prove setup Tort comparison Detente Drawn Devotionally with the possibility of purchasing a product; however, it will not analyze the complex sales data and other valuable information and relationships that can be discovered.
The multivariate analysis (NOVA) would be appropriate because it provides multiple levels of analysis, is typical for consumer and market research, and one of its remarry purposes is to predict (Cooper and Schneider, 2011).
An important goal in most research situations is to be able to predict outcomes based on prior information, and a multivariate analysis would assist in achieving this goal.
Through the analysis, the researcher could predict the chance of a consumer making a purchase after seeing an Internet ad. A prediction could also be made for the increase in sales a company can expect after the consumer has been exposed to the company’s brand of products or services. This will aid companies in tracking inventory more efficiently, ultimately increasing the company’s bottom line. The other purpose of NOVA is to explain by solving questions that stem from the data.
It can explain the most important variables in predicting a sales lift which could include the key variables in the study, household size and prior purchase behavior, or additional variables can be examined such as age, gender, marital status, income, education, and occupation. By determining a hierarchy of most influential variables, companies can focus on advertising towards the most influential variable first as a guarantee that sales will increase, and then continue down the hierarchy until an optimum level f revenue is reached.