The purpose of a questionnaire is to help you gather information that will allow you to make an infomed decision to help solve a marketing problem or gather useful marketing information. It can be challenging to identify precisely what your information needs are, what types of questions to ask and how to best measure different concepts you want to learn about.  Designing and writing a useful questionnaire is partially an art and partially a science. Below are some tips to help you navigate through this challenge.

1. Include a brief (two or three sentences) introduction to the questionnaire telling the respondent about the questionnaire, thanking the respondent, detailing the estimated time to completion and assuring respondents of the confidentiality of their answers. This will help increase the response rate.
2. Begin the survey with a screening question(s), to make sure the person you are going to interview is qualified to answer your questions. You want people that are familiar with your product/brand/service/topic to be participating in the survey. The key to the qualifying question(s) is that if the respondent’s answer is ‘no’ to being familiar with or using the product or service, then the survey is terminated and the person does not participate. (Example: I am interested in the perception of customer service at Publix. My target market is current Publix shoppers. Therefore, my screening question could be: “Have you shopped in Publix in the last month?”  If yes, continue, if no, terminate.) 
3. As you develop questions, ask yourself the following to determine if you should use the questions: “Does each question produce information that is necessary to address the research topic and meet the goals of the study?”  If the answer is no, do not include them.
4. Use a variety of survey question types including ratings, rankings, forced choices, and semantic differential scales, to answer your research questions.  Keep in mind the types of questions you ask may limit the method of analysis and quality of the information you can get from analyzing the data.
5. Related to point four, consider using Likert-type questions when measuring attitude and satisfaction.  They are easy to construct and easy for respondents to fill out.  (Example: Please indicate your level of agreement with the following statements using the 1 to 5 scale below).
6. When using semantic differential questions, make sure that the descriptors are true opposites of each other.  Semantic differential scales use polar opposites that respondents are asked to choose from to best describe something.  For example, weak and strong, indecisive and decisive, cheap and expensive.  (Example: If asking their perception of a new item on a menu, the polar opposites should not be excellent and ok, as these are not true opposites and will skew the results).
7. Demographic questions go at the end of the questionnaire. Ask demographic questions that are relevant to your research.  These might include age, income, family size, employment status, geographical location, and other information.  These answers will provide useful cross tab analysis by showing response differences between men and women, purchase interest in a product by income level, or influence of family size on product attributes.
8. Be sure that response categories have no problems with mutual exclusiveness. (Example: Your age choices should not be 18-25 and 25-30 because if someone is 25, which category do they belong to?)  Also be sure categories have equal breaks.  For example, the age break of 18-24 has seven ages so all of the age breaks should have seven age breaks.
9. The questionnaire should be easy to complete with clear instructions, clear and simple wording and be neat looking.  For example, if a respondent answers a particular question with a no, they are clearly directed to a different follow-up question than if they answered yes. (The pretest will help with this part!)
10. Always pretest!  But be sure to pretest among the target respondents.  If you are conducting research among mothers with children who are heavy users of laundry detergent testing the questionnaire among college students can give you misleading results. 

Sara Weisfeld-Spolter, Ph.D., is an Associate Professor of Marketing in H. Wayne Huizenga College of Business and Entrepreneurship, Nova Southeastern University. Dr. Weisfeld-Spolter can be reached at sw887@nova.edu; Herbert Brotspies, D.B.A., is a Part-Time Participating Faculty in Marketing in the H. Wayne Huizenga College of Business and Entrepreneurship, Nova Southeastern University. Dr. Brotspies can be reached at brotspie@nova.edu

This entry was posted on April 15, 2016 at 11:39 AM and has received 4042 views. There are currently 15 comments. Print this entry. Download attachment.

The hunt is on to get as much customer information to guide marketing decisions by using what is currently known as “big data.”  Simply, big data is a collection of data, structured and unstructured, used to find marketing relationships not very obvious to the marketer.  Big data helps show marketers what is going on.  Big data helps the Chief Marketing Officer (CMO) understand the “what” but very little of the “why.”

Now, more and more marketers are turning to ways to find the “why” in marketing—why consumers buy the products they do, how they use them, and importantly, how they relate to products in ways big data or conventional market research surveys cannot.  They are increasingly using techniques used by anthropologists called ethnographic research, studying consumers where they live, where they work, in the kitchen, in the bathroom, in the stores, restaurants, concerts, malls, or college campuses.  This observational method helps marketers by showing how products are used, the meaning of products in their lives, and the lifestyles that influence purchase decisions. 

Ethnography evaluates consumer behavior in detail, identifying important patterns through observation of people engaging in activities such as browsing, buying and trying products, or using services.  Based on ethnographic findings, recommendations are made to conduct quantified market research, develop new products, add features to existing products, or change advertising approaches.

Intel, the computer chip maker, uses ethnographic research to understand how teenagers, who grew up with smart phones, use their devices differently than baby boomers, how television and PC technology converge, and how smart phones are taking over most of the functions of personal computers.  J.C. Penney looks in women’s closets to see the brands and styles of clothing they purchase for work.  Clairol, the marketer of hair coloring, watches how women apply hair coloring at home to improve the ease of product use.

According to ethnographic case studies by Consumer Research Associates,

·         Abbvie Pharmaceuticals, a marketer of a drug for HIV, wanted to understand the patient journey to identify opportunities for innovation in packaging, messaging, and service.  Researchers observed physicians with patients and conducted in-home interviews with patients to learn how drugs are used.  As a result of the research, new techniques were developed to help patients comply with their therapies and to help physicians communicate and personalize treatment solutions for patients. 

·         Miller Lite wanted to understand how brand updates would be received and understood by their current customers.  Researchers conducted in-home demographic groups to gauge reaction concepts being considered.  Interviews were conducted in stores and bars with different brand concepts in a natural setting to gauge consumer reaction.   Using a variety of ethnographic methods, the project culminated in the successful update of all Miller Lite branding and marketing materials.

·         Best Buy, a leading consumer electronics retailer, wanted to explore expanding its selection to include a health and fitness department.  They were interested in how well customers would accept this brand expansion with a particular appeal to female shoppers. They wanted to understand the consumer product research and decision-making processes and to identify triggers for investing in home fitness equipment among female shoppers. Ethnographers collected stories among women who recently purchased fitness equipment learning about stores the participants liked including Best Buy.  Researchers accompanied consumers on shopping trips for fitness equipment to understand the purchase process.  The ethnographic research helped Best Buy design the fitness department and provided direction in product selection.

Ethnographic research is also used in business to business situations.  Bosch, a manufacturer of production equipment, wanted to determine how to gain a competitive advantage over rival companies.  They first conducted interviews with production managers and then went into the manufacturing plant to observe how production-line staff used competitive equipment.  The observations revealed there were customer needs that were missed by competitors such as awkward adjustments and difficult maintenance procedures.  The result was a line of Bosch manufacturing products that overcame the issues of competitive products.  Observing the use of competitive products, an ethnographic technique, gave Bosch the insight they needed.

Miele, a German household products company, wanted to investigate the cleaning needs of people with allergies.  They sent researchers into homes of people who had children with allergies to observe cleaning practices.  Through ethnographic research, they discovered, parents spent extra time vacuuming mattresses to remove allergens.  Parents could not be sure the mattresses were allergen free, so they kept vacuuming.  Miele developed a vacuum cleaner with a series of lights that indicated when the item is dust free.  This reduced the time and uncertainty of parents vacuuming their child’s bed.  Based on this research, Miele also introduced a washing machine with special features to thoroughly clean pillows and bedding of allergens.

Big data, finding unusual relationships in structured and unstructured data, will always play an important part in marketing to understand what is happening.  But to develop insight as to why, marketers use ethnographic research and visit people in their homes, watch how they use products, listen to stories about how and why they buy, what they buy, and gain deep insight into the purchase decisions and the why of marketing.

*Image source: Tata Consulting Services (2015).

Herbert Brotspies, D.B.A., is a Part-Time Participating Faculty in Marketing in the H. Wayne Huizenga College of Business and Entrepreneurship, Nova Southeastern University. He can be reached at brotspie@nova.edu

This entry was posted on December 1, 2015 at 11:04 AM and has received 3253 views. There are currently 4 comments. Print this entry. Download attachment.

A firestorm has been brewing ever since Fred Reichheld claimed in his 2003 Harvard Business Review (“The One Number You Need to Grow”) article that the simple Net Promoter Score (NPS) measure of consumer recommendations was a good proxy of customer loyalty and an accurate predictor of business growth.  The publication of the HBR article was followed up with his bestselling book The Ultimate Question.  Many of the largest companies including GE, American Express, T-Mobile, Microsoft and Philips adopted the measure and in many cases, changed the way service is delivered to even tying employee and/or executive compensation to NPS scores.   The beauty of this measure is in its parsimony, consists of one simple question: “How likely is it that you would recommend us to your friends or colleagues?” Calculating NPS scoring is based on a 0-10-point “likely to recommend” scale ranging from “highly unlikely” to “highly likely.”  Those who score between 0 and 6 are considered “detractors”, those who score between 7 and 8 are “passives” and those scoring 9 or above are “promoters”.  The eventual NPS is then calculated by subtracting the percentage of detractors from the percentage of promoters.  For example, if 20% of Company X’s customers are detractors, and 60% are promoters, then Company X has scored an NPS of 40.

Companies have gravitated to NPS due to its straightforward approach to assessing loyalty and providing a clear measure of an organization’s performance through its customers’ eyes.  Further, the growing importance of word-of-mouth communications in driving future growth has made NPS more attractive.   Do higher NPS scores make a difference?   At American Express, for a promoter who is positive, the company sees a 10-15 percent increase in spending, and 4-5 times increased retention—both which drive shareholder value.   Research shows that sustained value creators—companies that achieve long-term profitable growth—have Net Promoter Scores (NPS) two times higher than the average company.  Further, NPS leaders outgrow their competitors in most industries—by an average of 2.5 times.

Yet, NPS has sparked considerable debate during the past 10 years as to the efficacy of the “ultimate question”, where a number of researchers have examined the construct’s validity and now the verdict on NPS is not quite as strong as when Reichheld first introduced the word-of-mouth metric back in 2003.   For example, in an excellent study by Keiningham, et. al (2007), their study’s results undermine a key supposition of NPS, i.e. that it is the single most reliable indicator of company growth.   Their findings, which contradict this supposition, have important implications for managers that have adopted the Net Promoter metric for tracking growth and have consequences as to the potential misallocation of resources.  Further, Mark Molenaar of TNS Research Surveys thinks that the score is too simple, too narrow and no better than other measures of satisfaction or advocacy.

This brings up an ongoing debate when it comes to measurement: “Are multi-item (MI) scales preferred over single-item (SI) scales such as NPS?”  According to conventional measurement theory, the (reflective) items comprising multi-item (MI) measure of a focal construct represent a random selection from the hypothetical domain of all possible indicators of the construct.  Using multiple items helps to average out errors and specificities that are inherent in single items, thus leading to increased reliability and construct validity.  Single-item (SI) measures seem to be a viable option in exploratory research situations where typically weaker effect sizes are expected and smaller samples are used.  Rossiter wrote that “when an attribute is judged to be concrete, there is no need to use more than a single item.”   Many scale development experts recommend following conventional wisdom and use MI scales when conducting survey research.  Back to NPS, Morgan and Rego suggest that if the NPS score is used, it should be supplemented with additional questions which would allow companies to further understand their customers and their reasons for recommending to friends and family.   Bain and Company suggests that research conducted using the Ultimate Question should be followed up with an open-ended question: “Why?”

*NPS Chart: www.davidmitz.com; by David Mitzenmacher, 2011.

William (Bill) Johnson, Ph.D., is a retired Professor of Marketing and Adjunct Professor in Marketing in the H. Wayne Huizenga School of Business and Entrepreneurship, Nova Southeastern University. He can be reached at billyboy@nova.edu

This entry was posted on January 6, 2014 at 8:57 AM and has received 5174 views. There are currently 45 comments. Print this entry. Download attachment.