BUSI 453 Week 1 Discussion Board Forum 1 Example

Discussion Board Forum 1: The Role of Marketing Research and Problem Definition

BUSI 453 Applied Marketing Analysis, Discussion Board Forum 1 Thread and Replies

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The forum prompt and the classmates are composites; the textbook, the research and the Scripture cited are real.

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A regional bank's digital banking manager asks whether the bank should add budgeting tools to its mobile app. The question is reasonable, yet nobody could design a study around it as worded. Malhotra (2015) distinguishes the management decision problem, which asks what the decision maker should do, from the marketing research problem, which asks what information is needed and how it can best be obtained. The first is action oriented; the second is information oriented.

In this case the decision the manager faces is simply yes or no on building budgeting tools. The marketing research problem might be stated broadly as determining customers' interest in in-app budgeting and its likely effect on their use of the app and their loyalty to the bank. Malhotra (2015) recommends following the broad statement with specific components. In this case they would include: what share of current app users would use budgeting tools at least monthly, which features they value most, whether interest differs by age and account type, and whether customers who would use the tools are those the bank most wants to keep. Each component points to a measurable answer. Malhotra (2015) also warns against two opposite errors in stating the research problem: defining it so broadly that it gives no direction, such as "improve the app," or so narrowly that it rules out useful answers, such as "determine whether customers want a pie chart of their spending." The broad statement with specific components avoids both by setting the scope and then naming exactly what must be learned.

Why does this step matter so much? Research is only valuable if managers use it. Deshpandé and Zaltman (1982) studied how managers use market research and found that they were more likely to use research they judged technically sound and actionable, less likely to use findings that surprised them, and more likely to use it when they had interacted closely with the researchers. Moorman et al. (1992) found that trust between research users and providers increased use, and that trust grew when researchers understood the user's actual problem. A study that answers a question the manager never asked will sit in a drawer no matter how well it is done.

The payoff of getting it right appears in firm results. Germann et al. (2013) surveyed senior marketing executives and found that firms deploying marketing analytics more widely in their decisions reported better performance, especially in industries with intense competition and changing customer preferences. Banking fits that description. But analytics only help when they are aimed at the decision the firm actually faces.

Problem definition also guards against a common error: treating a symptom as the problem. If the manager's real concern is that younger customers are opening accounts at online banks, budgeting tools may be one answer among several. Talking with the decision maker and examining the bank's own data first, as Malhotra (2015) advises, might reframe the research around why younger customers leave. In that case the management decision problem would become how to keep younger customers, and budgeting tools would be one option tested alongside others, such as faster account opening or lower fees. Getting this framing right at the start costs a few conversations; getting it wrong costs an entire study.

By the standard of Proverbs 18:13, a person who blurts out an answer before the question is finished has earned embarrassment (English Standard Version Bible, 2001/2016). Researchers are tempted to answer quickly, choosing a survey before understanding the decision. Defining the problem is the discipline of hearing first: listening to the manager, the data and the customer before deciding what to ask.

Reply to Jasmine

Jasmine, your coffee shop example, where the owner wants to know whether to extend evening hours, is a clear management decision problem. I think your research problem could be sharpened, though. "Determine customer satisfaction with the shop" is too broad, because satisfied customers may still have no reason to come in at 8 p.m. A tighter statement would be "determine the level of demand for coffee and food between 6 and 10 p.m. among students and residents within one mile, and the products they would buy."

Market orientation research supports focusing on customer needs that drive decisions. Kohli and Jaworski (1990) described a market-oriented firm as one that gathers intelligence about present and future customer needs, shares it across every department, and acts on it. Jaworski and Kohli (1993) found that market orientation was related to overall business performance and employee commitment. Your owner is acting in a market-oriented way by asking before acting; the research should collect intelligence that leads directly to a response. With the tighter statement, the specific components almost write themselves: how many people want coffee or food in the evening, on which nights, at what price, and whether they would otherwise go to a competitor or stay home. Each answer bears on staffing the extra hours.

Proverbs 15:22 credits successful plans to many advisers and failed ones to their absence (English Standard Version Bible, 2001/2016). Malhotra (2015) recommends discussions with the decision maker and interviews with industry experts at this stage. Have you thought about asking nearby businesses that already open late what evening traffic looks like? Their answers would be useful secondary information before any survey.

Reply to Daniel

Daniel, you argued that big data makes careful problem definition less important, because companies can analyze everything and see what emerges. I agree that data are more plentiful than ever, but I think the abundance makes problem definition more important, not less.

Wedel and Kannan (2016) reviewed marketing analytics in data-rich environments and stressed that the value of analytics depends on connecting models to specific marketing decisions, such as allocation, personalization and pricing. More data means more patterns, and many of them will be meaningless unless an analyst knows which decision they inform. Germann et al. (2013) found that analytics improved performance when they were deployed in decision making, which again ties value to the decision rather than to the volume of data.

Malhotra (2015) warns that a poorly defined problem wastes the entire research effort, however sophisticated the later steps. With big data, the waste can simply be larger. Proverbs 4:7 says that the beginning of wisdom is to get wisdom, and whatever you get, get insight (English Standard Version Bible, 2001/2016). Data are not insight. Insight comes from knowing what question the data are meant to answer. A retailer with millions of transactions can find that customers who buy diapers also buy certain snacks, but unless that pattern informs a decision about layout, promotion or assortment, it is a curiosity rather than research. The textbook's first step keeps the analyst pointed at the decision. Big data and careful problem definition are partners, not rivals: the data supply the evidence, and the problem statement decides which evidence counts. What decision in your company do you think its data currently serve best?

References

Deshpandé, R., & Zaltman, G. (1982). Factors affecting the use of market research information: A path analysis. Journal of Marketing Research, 19(1), 14-31. https://doi.org/10.1177/002224378201900102

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Germann, F., Lilien, G. L., & Rangaswamy, A. (2013). Performance implications of deploying marketing analytics. International Journal of Research in Marketing, 30(2), 114-128. https://doi.org/10.1016/j.ijresmar.2012.10.001

Jaworski, B. J., & Kohli, A. K. (1993). Market orientation: Antecedents and consequences. Journal of Marketing, 57(3), 53-70. https://doi.org/10.1177/002224299305700304

Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: The construct, research propositions, and managerial implications. Journal of Marketing, 54(2), 1-18. https://doi.org/10.1177/002224299005400201

Malhotra, N. K. (2015). Essentials of marketing research: A hands-on orientation. Pearson.

Moorman, C., Zaltman, G., & Deshpandé, R. (1992). Relationships between providers and users of market research: The dynamics of trust within and between organizations. Journal of Marketing Research, 29(3), 314-328. https://doi.org/10.1177/002224379202900303

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413