Fancher Golf Center Marketing Research
Wendy Clark
MT355-01: Marketing Research
February 8, 2016

The case study is concerning a former PGA golfer by the name Brian Fancher who is deciding whether or not to build a golf center in his hometown of Harrisonburg, Virginia or nearby Staunton, Virginia. The golf center will have indoor and outdoor training facilities where Brian would give golf lessons. The golf center will also sell high-end golf equipment and custom club-fitting services. Brian contacted the Shenandoah Valley Small Business Development Center at James Madison University (JMU) to conduct some marketing research on his behalf. The center used students enrolled in a marketing research class to do the research. The students used the following groups for their research: permanent Harrisonburg residents 18-70 years old, JMU students, and Staunton residents 18-70 years old. According to the case study the following is the list of research problems to be addressed: determine percentage of people who say they are serious golfers; determine the frequency that golf is played during the spring, summer and fall; measure level of satisfaction with current abilities; measure level of satisfaction with current golf instruction opportunities, high-end golf sources, and club-fitting services in area; measure assessments of proposed golf center concept; and measure whether or not people would use the golf instruction services or the club-fitting services. There were teams assigned to each of the three groups. Each team used a different data collection method. Team 1 was assigned to the Harrisonburg residents that were 18-70 years old, and they used the mall intercept method. According to Brown, Suter, and Churchill, Jr. (2014) the mall intercept method is “A method of data collection in which interviewers in a shopping mall stop or interrupt a sample of those passing by to ask them if they would be willing to participate in a research study” (p. 226). After reading how team 1 collected their data, I see that there are errors with their data collection method. Team 1 stood in the mall during the day and solicited people to answer a survey, but their data was not collected from a pure random sample. Most people are at work during the day, so this would not be the typical cross section of people. I also think that the age group could have been expanded to people over 70 years old. People are living longer today and are more active, so including people over 70 would have been a good idea. The end sample was biased, since it was not an accurate sample of the general population. Team 2 was assigned to the JMU student population, and they used in-bound surveys. According to Brown, Suter, and Churchill, Jr. (2014), in-bound surveys are “A method of data collection in which respondents access a survey by telephone or on the Web to respond to survey items” (p. 230). Team 2 also had errors with their data collection method. Team 2 sent emails to every fourth student from 12 different campus organizations. The main problem that I saw was that they went with the email approach. Emails do not always get delivered, or sometimes the email will go into the junk mail box and get deleted. Also, the students may not always have access to a computer to check their emails. Even though this method produced a pure random sample, the delivery method may not have been the right way to go. Team 3 was assigned to the Staunton residents that were 18-70 years old, and they used telephone interviews. According to Brown, Suter, and Churchill, Jr. (2014) telephone interviews are “Telephone conversation between a representative of the research organization, the interviewer, and a respondent or interviewee” (p. 228). Team 3 also had errors with their data collection method. Team 3 did telephone interviews between the hours of 8:00am to 5:00pm when a lot of people in this age group would be at work. This caused the sample to be biased. Just like with team 1, team 3 did not obtain a pure random sample. They should have looked at changing their hours to make the phone calls later in the day when more people would be home. Some changes could have been made to prevent/minimize each of the errors. For one thing they could have broadened the age group since people are living longer. They also could have used a different data collection method such as personal interviews. According to Brown, Suter, and Churchill, Jr. (2014) personal interviews are “Direct, face-to-face conversation between a representative of the research organization, the interviewer, and a respondent or interviewee” (p. 225). They could have also extended the hours for the telephone interviews, so that more people would be home when they called. Another thing I would have done differently would be to post flyers in classrooms and asked teachers to help spread the word by telling their students to check their email for the information on the survey. The teams needed to brainstorm a little longer to come up with some ideas. Since these errors have occurred, I would suggest that the teams sit down and see what was done wrong. Once this is determined decide what data collection methods should be used to obtain the best results. If there is enough time, I would suggest conducting another survey with the data collection method that you decide is the best method. Then compare the outcomes from the original research and the new research. Thinking outside the box to come up with better solutions will give you better results.

Reference
Brown, T.J., Suter, T.A., and Churchill, Jr., G.A. (2014). Basic Marketing Research (8th ed.). Stamford, CT: Cengage Learning.