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The Five Rules for Reliable Marketing Research

The purpose of customer research is to develop new knowledge about a market. In order to do this, surveys must be designed to gather accurate, reliable data about customer preferences. Then, the collected data must be tabulated so that it reveals information and analyzed to produce new insights.

The amount of information and insight that you can extract from marketing research will determine the success or failure of your product or service. Following these five rules for reliable marketing research will dramatically improve your probability of success.

By following these five rules for reliable marketing research, you will gain new knowledge about the markets for your product or service. Perhaps most importantly, these rules will help you avoid expensive failures.

  1. Speak to the right people.
  2. Use only customer language.
  3. Use semantic scales.
  4. Avoid artificial choices.
  5. Analyze your research data.

Rule #1: Speak to the Right People

Sometimes there are instances when you do not want to speak with the whole universe of respondents. For marketing reasons it may be important to speak with a specific subset of the whole universe of possible respondents. Examples of ways that you might want to define respondent groups include:

  • Those who have positive purchase interest in a concept
  • Heavy users of a particular category
  • Certain corporate decision makers for a business product or service

    Ways to Control the Respondent Group

    1. Draw the sample for the research so that it includes the proper target audience.
    2. Add a screener to the beginning of the research that will drop respondents who do not meet your respondent profile.
    3. During research keep monitoring to make sure that a representative sample has been interviewed.
    4. Verify at the end of the research that the respondent group is composed of the people with whom you wanted to speak.

    Rule #2: Use Only Customer Language

    Use customer language and address only those issues that are relevant to customers. Ask customers about benefits or about how it feels to be a user of a product or service. Customers have opinions about, and an understanding of, benefits and how they feel. Thus, they are can reliably answer questions about these topics.

    It is important to avoid overly detailed or very technical questions in customer research. This is a very common error. Engineers want to ask about every technical detail, but customers don’t care and cannot give meaningful responses.

    Rule #3: Use Semantic Scales

    One of the classic marketing research issues is the measurement of "How well-liked is a product, service, or an idea?" There are two approaches for collecting this quantitative data:

    Alternative 1: Would you rate this product, service, or idea: Excellent, Very Good, Good, Fair, or Poor?

    Alternative 2: On a scale of 1-10, with one being the lowest and ten being the highest, how would you rate this product, service, or idea?

    Comprehensive testing has shown that semantic scales are much more likely to generate consistent responses than numeric scales do. In fact, the semantic differential on the scale in Alternative 1 has been proven to be equal steps that correspond to 5, 4, 3, 2 and 1, where 5 is Excellent and 1 is Poor. Thus, different customers respond in a consistent manner to this scale.

    On the other hand, different customers often respond differently when using a numeric scale. For example, many respondents equate "good" with "average" or the number "5". However, based on their school experience, another large group of respondents equates "good" with a "C" grade (70%-80% correct) or a "7-8" rating on a 10 point scale. Mixing both types of responses interferes with learning about customer evaluations of a product, service, or idea.

    The bottom line: Semantic scales are easier for respondents to understand. Semantic scales generate more consistent results. Thus, research using semantic scales produces more reliable customer information.

    Particularly in business situations, respondents are prone to answer what they think they are supposed to say. The more that questions stray from reality, the greater the occurrence of research-induced responses.

    Rule #4: Avoid Artificial Choices

    Research respondents will answer whatever questions they are asked. Respondents have a desire to please the interviewer. More importantly, respondents have a need to give an answer that appears competent. Particularly in business situations, respondents are prone to answer what they think they are supposed to say. The more that questions stray from reality, the greater the occurrence of research-induced responses.

    Asking customers to make artificial choices is a very common source of mistakes from customer research. For example, surveys of transportation company customers often ask "Which is worse: a late shipment or a damaged one?" Shippers are both able and willing to answer survey questions like this one; however, their answers are meaningless. In real life, shippers don't have to make this trade-off. Their shipments are delivered on time and without damage or they switch suppliers. These artificial choices resemble questions from the teen social game "Would You Rather" as in "Would you rather wreck your dad's car or have a bad prom date?" The question is fine for amusement, but not much else.

    In order to avoid this problem, think about your research survey as a conversation with a customer. Would that customer agree that your questions reflect how he/she makes decisions? Would the questions and choices seem reasonable? Do the choices addressed by your questionnaire reflect the issues he/she thinks about?

    Rule #5: Analyze Your Research Data

    Customer research results need to be analyzed and interpreted. Also, it is important to combine company data with customer research data in order to learn more.

    Analysis is important because some marketing questions cannot be answered directly by customers. For example, customers are often not aware of all the factors that create brand loyalty. An analysis of buying behavior versus different customer experiences will often reveal important unconscious brand loyalty factors.

    It is particularly important to develop a tabulation plan for analyzing customer research data before interviewing starts. This thinking beforehand will insure that all the questions that you need to have answered will be included in the research. Also, thinking about available company data in advance will often determine what additional data would be most helpful.

    Matthew Holmresearch