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Sources of Error in Market Research and the Ramifications of the Error

03 Apr 2024,9:51 AM

Market research is a vital process that serves as the bedrock for informed decision-making in the business world. It is specifically useful to businesses that want to understand the needs, preferences, and behaviors of their customers and competitors. However, market research is not always accurate or reliable, and the effectiveness of market research is contingent upon its accuracy. Various sources of error can affect the quality and validity of the data collected and analyzed (Hair et al., 2017). Some of the most common and notable sources of error in market research include population specification error, sampling and sample frame error, non-sampling error, measurement error and analysis errors. The ramifications of errors in market research are substantial, affecting product success, brand reputation, and overall business performance. Recognizing the sources of error is crucial for marketers to enhance the accuracy of their research. The real-life examples of New Coke and Microsoft's Zune illustrate the tangible consequences of overlooking market research pitfalls.

Sources of error

Population specification errors

Population specification error occurs when the researcher does not understand who they should survey thus defining the target population incorrectly or incompletely leading to inaccurate or misleading results in marketing research, as the sample may not reflect the true characteristics or preferences of the target population. For example, if a researcher wants to study the breakfast cereal consumption of families, they may choose to survey only the mothers, assuming that they are the ones who decide and purchase the cereal. However, this may ignore the influence of other family members, such as fathers or children, who may have different tastes or opinions on cereal. Therefore, the survey results may not capture the diversity of cereal consumption within families.

To avoid population specification errors, the researcher should clearly define the target population and use appropriate sampling methods to select a representative sample. They should also consider the possible sources of bias or error in their sampling frame, such as missing or outdated data, and try to correct them. Moreover, they should use relevant and valid questions to measure the variables of interest and avoid leading or ambiguous wording that may confuse or influence the respondents (Heckman, 1979).

Sampling and sample frame errors

Sampling errors occur when the sample is not representative of the population, either due to random or systematic factors. Sample frame errors occur when the list of units from which the sample is drawn is incomplete or inaccurate, leading to undercoverage or overcoverage of some segments of the population (Assael & Keon, 1982). The sources of sampling errors can be classified into two categories: random sampling errors and non-sampling errors. Random sampling errors are inevitable in any sampling process, and they result from the natural variation among the units in the population. They can be reduced by increasing the sample size, but they cannot be eliminated. Non-sampling errors are caused by human or technical factors that introduce bias or distortion in the data collection or analysis. They include measurement errors, non-response errors, response errors, interviewer errors, processing errors and analysis errors. They can be minimized by using appropriate methods, instruments, procedures and quality control measures.

The sources of sample frame errors can also be divided into two categories: undercoverage and overcoverage. Undercoverage occurs when some units in the population are not included in the sample frame, either because they are unknown or inaccessible. This can lead to a biased sample that does not reflect the true characteristics of the population. Overcoverage occurs when some units in the sample frame are not part of the population, either because they are duplicated, outdated or irrelevant. This can lead to a larger sample than needed, which increases the cost and complexity of the data collection (Lohr, 2012).

The consequences of sampling and sample frame errors can be serious for marketing research, as they can affect the accuracy and precision of the estimates and conclusions derived from the data. Sampling errors can lead to confidence intervals that are too wide or too narrow, which means that the true value of the parameter is either uncertain or misleading. Sample frame errors can lead to biased estimates that are either overestimated or underestimated, which means that the parameter is either inflated or deflated. These errors can have negative implications for marketing decisions, such as product development, pricing, segmentation, targeting and positioning.

Non-sampling errors

Non-sampling error occurs when the researcher makes mistakes in designing, conducting or administering the survey or when the respondents give inaccurate or dishonest answers. They arise in the process of conducting marketing research that is not related to the sample size or sampling technique. Non-sampling errors can affect the validity and reliability of the research findings and may lead to biased or inaccurate conclusions (Assael & Keon, 1982). Examples of non-sampling errors are

 - Non-response error which occurs when some respondents refuse to participate in the survey or fail to answer some questions. Non-response errors can introduce bias if the non-respondents have different characteristics or opinions from the respondents. For example, if a survey on customer satisfaction is sent by email, dissatisfied customers may be less likely to open the email or complete the survey, leading to an overestimation of satisfaction levels.

- Measurement error: This error occurs when the questions or scales used in the survey do not measure what they are intended to measure. Measurement error can result from ambiguous, leading, or complex questions, poorly designed response options, or misunderstanding or misinterpretation by the respondents or the researchers. For example, if a survey uses a Likert scale to measure customer satisfaction, but does not define what each point on the scale means or does not use a consistent scale across different questions, then the results will be affected by scaling bias, inconsistency bias or acquiescence bias. This can lead to unreliable data, distorted comparisons and faulty recommendations.

- Interviewer error: This error occurs when the interviewer influences the responses of the respondents through their behavior, attitude, tone, or appearance. Interviewer errors can also result from recording or coding errors, interviewer fatigue, or dishonesty. For example, if an interviewer shows enthusiasm or approval when a respondent gives a positive answer, the respondent may feel pressured to give more positive answers than they feel.

- Adjustment error: This error occurs when the data collected from the sample are adjusted or weighted to make them more representative of the population. Adjustment errors can result from using incorrect or outdated information, making inappropriate assumptions, or applying incorrect formulas or methods. For example, if a survey uses quota sampling to ensure that the sample reflects the population in terms of age, gender, and income, but uses outdated census data to determine the quotas, the sample may not be representative of the current population.

- Processing error: This error occurs when the data collected from the survey are analyzed, summarized, or presented. Processing errors can result from data entry errors, calculation errors, software errors, or human errors. For example, if a researcher accidentally enters a wrong value or formula in a spreadsheet, the results may be distorted or inaccurate.

Product or service that may have failed due to faulty market research practices

Whenever the failure of a product in the market due to faulty market research practices is mentioned, the case of new Coke and Microsoft’s Zune comes to mind. The Coca-Cola Company in 1985. New Coke was a sweeter version of the original Coke formula, which was developed after extensive taste tests showed that consumers preferred it over both Pepsi and old Coke. However, the company did not take into account other factors that influenced consumer preferences, such as brand loyalty, emotional attachment, and cultural identity. New Coke faced a massive backlash from loyal Coke drinkers, who felt betrayed by the change and demanded the return of the classic formula. The company had to pull New Coke from the shelves after less than three months and reintroduce old Coke as Coca-Cola Classic. New Coke is widely considered to be one of the worst marketing blunders of all time (Oliver, 2013).

Another notable example is Microsoft's Zune, which was launched in 2006 as a competitor to Apple's iPod. Zune was a digital media player that offered features such as wireless syncing, FM radio, and subscription-based music service. Microsoft conducted market research to identify the needs and wants of potential customers but failed to understand the existing market dynamics and consumer behavior. Zune could not match the popularity and appeal of the iPod, which had already established a dominant position in the market with its innovative design, user-friendly interface, and seamless integration with iTunes. Zune also suffered from technical issues, lack of content, and poor marketing. Microsoft discontinued Zune in 2012 after losing millions of dollars and market share to the iPod (Shaffer, 2015).

Market research serves as a cornerstone for informed decision-making in business, providing invaluable insights into customer needs and market dynamics. However, the accuracy of market research is susceptible to various sources of error, including population specification, sampling, and non-sampling errors. The ramifications of these errors are significant, impacting product success, brand reputation, and overall business performance. Recognizing and addressing these sources of error is imperative for marketers to enhance the precision of their research. The examples of New Coke and Microsoft's Zune vividly illustrate the tangible consequences of overlooking market research pitfalls, emphasizing the crucial role of accurate data in shaping successful marketing strategies. As businesses navigate the complex landscape of market research, a vigilant approach to error identification and mitigation becomes paramount for achieving sustainable growth and avoiding the pitfalls that have befallen others in the past.

 

 

 

 

 

 

References

Assael, H., & Keon, J. (1982). Nonsampling vs. sampling errors in survey research. Journal of Marketing46(2), 114-123.

Hair, J. F., Celsi, M. W., Ortinau, D. J., & Bush, R. P. (2017). Essentials of marketing research. McGraw-Hill.

Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the econometric society, 153-161.

Lohr, S. L. (2012). Coverage and sampling. In International handbook of survey methodology (pp. 97-112). Routledge.

Oliver, T. (2013). The real Coke, the real story. Random House.

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