Successful customer and market research can provide the data you need to support specific business objectives, such as understanding requirements for a new market or a new product, fine-tuning your customer journey mapping, improving competitive market positioning, refining a customer facing process, analyzing your win/loss results, and measuring customer satisfaction.

With the proliferation of survey software, it can be tempting to jump into creating survey questions, aided in part by tips published on the software company's website. But successful research is so much more than wordsmithing the questions and choosing appropriate rating scales: It's a science that takes time and money. Your methodology and corresponding survey instruments/questionnaires must support the purpose of your research, along with any business, timing, and budget constraints.

Bad decisions made as a result of a flawed research process (purpose, questions, implementation, and results interpretation) can be extremely costly, and potentially fatal. Usually, only the very largest companies have research experts on their marketing team. If you don't have one, consider engaging an external expert to mitigate risk on this very strategic project.

Six Best-Practices to Guide Your Research

Based on our several decades of successfully implementing research initiatives and crafting research instruments, from questionnaires to discussion guides, here are six proven best-practices to help you design, field, and interpret your study.

1. Be clear about your purpose

Determine specifically what you want to learn, and understand what data you need to support the decision.

Here's an example:

• To decide whether to pursue this market, we need to know_____________.
• As a result of this research, we will be able to make the following business decisions(s) __________.

Once you understand the problem and opportunity, start the process by answering these four questions:

1. What do we know now?
2. What are the best opportunities as we understand them now?
3. How can we frame our options based on what we know now?
4. What do we need to know that we don't know now to make this decision?

2. Determine your target population

One of the critical decisions in the design phase is determining your target population—that is, the people who will participate in your study during the fielding phase. It is important to have a sufficiently large sample size if you want the results to be statically meaningful. If the sample size is too small, it is very possible to draw erroneous conclusions.

A sample should be chosen at random from the population so that it is representative of the population. Decisions based on the characteristics of the sample can then be generalized to the entire population. Random sampling enables you to draw statistical inferences based on information collected from a small group representative of the population under investigation. With a random sampling technique, each subject in the population has an equal chance of being included in the sample.

Make sure your research participants are qualified to answer the questions; otherwise, they may feel compelled to make up an answer. It is acceptable to include a "Don't Know" category; however, when you receive a lot of "don't knows," it may mean that they really do not know and are not qualified to answer—or that "Don't Know" was the easiest answer to select.

3. Design your instrument

Here are some quick tips for how to design your survey instrument so it delivers meaningful results that drive action:

• Carefully plan enticements or requirements for the study participants.
• Provide a brief overview of the objectives of the study in the research purpose document.
• Keep the number of your questions to 15-30. Enthusiasm and interest wane if the instrument, whether it will be fielded online, by phone, or in person, is too long; if it is, it will affect the validity of the responses to the questions.
• Keep questions brief, direct, unambiguous, and focused on a single topic. Questions should be written in neutral language; avoid biased or judgmental wording (such as "should," "ought to," "bad," "wonderful").
• Group your questions into subsets, with headings to orient the respondent. This is important regardless of methodology. All of the questions should fit together in a logical, orderly, thematically holistic manner. Questions from "left field" are distracting.
• Make early questions less controversial and designed to pique interest.
• Leave demographic questions for the very end. They clearly require the least thought, thus allowing the respondents enough energy to tackle more complex questions. Plus, questions that are perceived as "personal" may make participants more defensive and lead them to believe that their anonymity is being violated, thus altering their subsequent responses.
• For self-administered instruments, such as online surveys, maintain consistency among scale questions so that all worse-to-best scales run in one direction (left to right or right to left). Research suggests that a scale with the "best" response at the left provides a higher mean response than the same question arranged worse-to-best (called the primacy effect, or the tendency of people to favor the left side of the scale). So, best-to-worse will yield better results, while worse-to-best may give a lower mean with a wider standard deviation.
• Use open-ended short-answer questions to provide richer, more personalized responses. Remember, though, that these are harder to analyze, since group summaries can be made only after time-consuming content analysis. Limit the number of short-answer questions unless you are prepared to spend considerable time in assessing group consensus.
• End with a brief description of how the data will be used. And a sincere "thank you," along with reassurance that someone will be reading and analyzing the data, is helpful to encourage survey completion in the future.

4. Test, then implement