Home

Solve My Problem

Free Reports

Products & Services

About Us

Index

Rewards

Free Newsletter

Contact Us

Other Resources

 

Self-Help Articles on Psychological Marketing
 

Creating an Opinion Research Survey to Increase Sales
By Gary Witt, Ph.D.

 In another article we looked at qualitative research and focus groups. Now we'll turn to quantitative research and surveys, the most popular method of quantitative consumer research.

We covered the importance of having a clear, specific purpose for your research, and how to build a list of top questions to ask. A survey allows you to ask a large number of consumers these questions, and turn their answers into numbers which can be manipulated and graphed.

There are four major ways to contact those being surveyed: telephone, personal (like door-to-door or mall intercept), mailed, and Internet. Telephone, mail and e-mail give you the most control over the characteristics of those who are surveyed. The Internet (survey on your Web site) and e-mail are the cheapest to do, but those who answer may not be representative of your target population.

Selecting an unbiased sample is critical if you want to extrapolate the results to all your consumers. For example, if you wanted to know if buyers of your cookies would pay another quarter for them, you wouldn't just send your researchers to malls in affluent areas. The people surveyed would likely have a much higher income than your overall buyers. So when the survey results showed people would gladly pay an extra quarter, that may not be true of your overall buyers, just the most affluent of them. A pricing decision made from this biased sample could be disastrous.

The two keys to a good sample are to (1) accurately define the population of customers you are interested in, and (2) select enough random members of that population. If you wanted to find out your buyers' attitudes about the features of a new type of expensive laptop before planning its marketing campaign, your target population would likely be computer users who are affluent. You might find that population by buying a mailing list from a computer magazine, specifying just those zip codes near your store which have the highest household incomes.

If the list is large, you will only need to sample part of it. Statistical analysis formulas show that even for a population of 10,000,000 people, you only need to accurately survey 384 people to get results that have a 95% chance of being accurate for your entire population. If your population is 10,000, you'll need to survey 378 people. If its 5,000, survey 357.

How do you pick those to survey? The most accurate way is to assign each person on your list a number, then use a Random Number Table (at your library), a "fishbowl" drawing, or any other method in which (a) every person has an equal chance of being picked, and (b) the selection of any one person does not affect the chance for anyone else to be picked.

Now you've got your sample of about 360 people. (By the way, if you think most of them are very similar, statistical laws say you only need to survey 240 people out of 10,000.) What's the best method to find out what you want to know, to find information you can really use?

Your decision about the method of sampling will probably be influenced by your budget and your timetable. Mail is low cost, but slow, and often doesn't have a good response rate without incentives. Telephone surveys have a moderate cost, and they are fast. Personal (mall) interviews are expensive and relatively slow (it takes a while to stop 360 qualified shoppers), but the quality of answers may be superior. Of course, there are other ways to elicit quantitative information besides surveys, such as experiments and observation, but we'll stick with this most popular approach.

Your survey questions can be "open-ended" (like fill in the blank, or "tell me what you think"), or "closed-ended" (like a multiple choice test.) The open-ended questions will give you richer data, but they'll be hard to turn into numbers and are more time-consuming to score. That's why most surveys either provide the answers or furnish the scale on which to rate specific responses. They're easy to administer, quick to score, and easy to understand. But they do reduce the richness and variability of the responses. Some surveys use a combination of both types of questions.

Good questions are difficult to write, but seem easy. Why? Because most people will create questions that have a hidden bias which can skew the results. For example, "Do you prefer the old or new package for Hamm's Hotdogs?" will skew answers toward the latter answer, since many people are conditioned to like "new" over "old" for nearly every product. For detailed advice on how to do write good questions, consult a book or articles on consumer research. One good book is How to Conduct Your Own Survey by Priscilla Salant.

The major problems with questions center around unintended bias and clarity. For example, a question which presented more positive than negative choices would show an unintended bias. A question which used unfamiliar abbreviations or vague words may be unclear. Finally, be careful about creating "double-barreled" questions, ones that are really two questions linked at the hip, like "Do you think green is an attractive color for Spring fashions, but not for Summer?"

Here are the three most common types of survey questions and their uses:

(1) Likert Scale questions: These questions give the consumer a five or seven step set of choices, like "Do you Strongly Agree, Agree, Neither Agree or disagree, Disagree or Strongly Disagree with the following statements? (a) I like to shop for clothes. . ." Use the Likert Scale if you want to find out how much consumers agree or disagree with various statements about your product, service, competition, price, or anything else. The answers help reveal the judgments consumers make. The next scale helps reveal the emotions which influence those decisions.

(2) Semantic Differential Scale questions: These questions ask the consumer to evaluate a product on a series of scales ("Please check the blank closest to your feeling or opinion about Starbuck's Coffee on the following scales.") Each scale is comprised of a set of two adjectives with opposite meanings (hot/cold, like/dislike, expensive/inexpensive), separated by five or seven blanks, like this: HOT ___ ___ ___ ___ ___ COLD. Each blank is given a numeric value in scoring, so the overall average result may be plotted exactly on the scale.

Here's another example: "How do you rate the Pet Store on each of these scales?
Expensive __ __ __ __ __ Inexpensive
Warm Feel __ __ __ __ __ Cold Feel
Exciting __ __ __ __ __ Not Exciting
Good Selection __ __ __ __ __ Poor Selection
Helpful Clerks __ __ __ __ __ Unhelpful Clerks," and so on.

Notice that the choices are not biased (we use "expensive/inexpensive," not "expensive/cheap"), since that would bias the results (some people would hesitate to admit they shop in a "cheap" store.). Using common opposites (good/poor) and common semantic tools (helpful/UNhelpful, courteous/DIScourteous) are good ways to keep choices balanced.

The Semantic Differential Scale is useful when you want to know in more detail how consumers see your product. Taken together, these beliefs play an important role in the consumer's overall judgment about the product or store, and likelihood of shopping there. Of course, consumers may rate you high on scales which aren't important to them ("attractive decor"), and low on ones which are ("price," "quality"), or vice versa. To understand better how to evaluate these results, you could include the third kind of survey question.

(3) Rank-Order Scale questions: Subjects are asked to rank items in order of preference, enjoyment, importance, value, or some other criterion. Each item is listed, with a blank next to it. For example, "Rank in order of importance these four attributes of a pet store:

__ Selection
__ Feeling I get when I enter
__ Price level of merchandise
__ Knowledge of Clerks about the merchandise."

Rank ordering is often used to find out where your product, or certain features of your product stand in relation to your competition. It shows you where you are weak, and it can present some good positioning opportunities when you find an important feature on which your competition ranks poorly.

Finally, include some demographic questions which are important to you, such as the person's age, sex, income level, race, Internet usage, and so on. These questions are usually found at the end of a survey. Don't go overboard. People are reluctant to answer many personal questions. Respectfully ask only what you need to know about your buyers.

One advantage of demographic questions is the opportunity to run "cross-tabulations" to show relationships between answers on some of the survey questions and certain demographic characteristics. For example, you might find that men are most interested in the power of your new laptop, while women focus on its weight. "Cross-tabs" are very useful to have when you're planning targeted advertising.

Quantitative research results are turned into numbers, most often averages. The results of each question can also be graphed to give a visual illustration of the results. Graphs also give you a way to longitudinally compare previous results with current results, showing trends. Your first survey will give you a "baseline" to which future answers can be compared. Knowing these trends can give you the edge you need over your competition.

Even if you are mathematically-challenged, it is important for you to know how to determine a few types of basic results. The Average or Mean is found by adding up all the individual scores and dividing by the number of scores. If you have a few outlandish scores which seem to be skewing your Mean, look at the Median. Find the Median by putting all the scores in order from top to bottom. Count the number of scores and divide by 2. Count down the scores to that number, which is in the exact middle of the list. That's the Median.

Finally, you should know the Standard Deviation (SD) around the Mean. That is the average amount of variance around the Mean. For example, if you asked people to guess the price of your ($25/room) carpet cleaning, and most answers ranged from $23 to $27, your deviation is small. But if most answers stretched from $15 to $45, you would have a larger deviation.

To find the SD, subtract each individual score from the Mean (treat all answers as positive numbers), and add up the answers. Divide it by the number of scores. That's your standard deviation of scores around the Mean. It's useful to know in comparing the Means of different questions, such as the rating of your product's value compared to the rating of your competitor's product. A small SD shows that most people have about the same opinion. A larger SD shows a wider variety of opinions, and probably indicates unfamiliarity with your brand or product class.

If you have good data, there is an enormous amount of useful information you can get out of a survey by applying statistical analysis. If you don't understand statistics, it's tough to do, so call someone who does. Try a college or a small business center. Of course, professional research firms will also be happy to help you analyze your data. If you have the funds, you can probably learn the most that way.

If you leave this article with nothing else, remember that consumer research is important for businesses of all sizes, that it should form the bedrock of your marketing strategy, and that even inexperienced business people can do some basic, useful research themselves. Good research will let you see your problems before your competition does, tell you why consumers are buying (or not buying) your product, and suggest new avenues for stronger sales growth. It's worth a regular investment.

>> For more on doing research using Focus Groups, click HERE.

(c) Gary Witt, 1998

Home * Solve My Problem * About Us * Free Information * Products & Services *
* Free Newsletter * Contact Us * Index * Rewards *

NOTICE: All material on this site is Copyrighted, and may not be reproduced without the written consent of the Marketing Psychology Group, Inc., Scottsdale, Arizona. If quoting text, please provide attribution. Thank you.