Too Many Cooks Can Even Spoil the Marketing Research Broth

In yesterday’s blog post, we discussed the importance of sharing research findings with other stakeholders in your organization. Today, we’re going to discuss how that can go too far, by trying to accommodate too many constituencies within the organization in the design of our survey. It is very easy to fall into the “too many cooks” problem of marketing research. Sometimes we have budget constraints and need to gather as much information from a survey to satisfy all the stakeholders involved. Other times, the department commissioning the survey is not the same as the department funding it, so the head of the latter department will see to it that the survey also serves some of his or her objectives.

The problem with involving too many departments in the planning and design of a survey is that it will create considerable infighting and result in a “satisficing” survey, that asks several questions, some tailored to each of the company’s internal constituents. As a result, the surveys are overly long, cumbersome, and lack a coherent focus. Quite often, these surveys result in respondent fatigue, unreliable responses, and biased results.

When your company is faced with several departments needing to share a survey for information, the best thing to do would be to first understand the company’s overall objectives for a survey. Then talk with the different departments about those objectives and understand what their needs are. Get them to prioritize their needs. Then once you understand the importance of each topic or issue to each department, try to match the most important ones back with the general objectives of the survey. Then talk with all departments together and prioritize those information needs that are most in need to match the company’s general objectives. It helps to have senior management providing top-down support for this collaboration. Stress to each department that you may not be able to get all the information they desire right away, but the quality and usefulness of the data you collect is more important than the quantity.

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