Customer Satisfaction Research Brief for Coles Supermarket

This brief aims to determine the problem (gap) leading to low customer satisfaction at Coles Supermarket. The research brief’s scope and structure will incorporate a statement of research objectives, the information that is necessary for fulfilling the research objectives, primary considerations for the collection of data, and a conclusion that summarizes the research brief and a reference list.

Student’s Last Name 1
Marketing and Communication Information and Decision Making
By (Name)
Course
Professor
University
Date

Student’s Last Name 2
Introduction
Assessing customers’ level of satisfaction should always reflect on major factors that are
specific to a certain industry such as the brand image, service reliability, and customers’
perception. This brief aims to determine the problem (gap) leading to low customer satisfaction
at Coles Supermarket. The supermarket is an Australian supermarket that was founded in 1914
as part of the Cole’s Group. Cole supermarket’s chief product line is fresh produce. In Australia,
the supermarket is among the leading retail firms that operate more than 720 stores with more
than 90,000 employees. Conducting customer satisfaction research will help set objectives to
improve customer satisfaction. The research brief’s scope and structure will incorporate a
statement of research objectives, the information that is necessary for fulfilling the research
objectives, primary considerations for the collection of data, and a conclusion that summarizes
the research brief and a reference list.
Considerations for Data Collection Process
Relevant Contract Strategy and Response Rates
The most relevant contract strategy to use for the research is the lump-sum contract
strategy. In this type of strategy, there is a single tendered price that is provided for the
completion of a specified task to the satisfaction of a client such as Cole’s management within a
predetermined date (Palinkas, et al., 2015, pp.534). In this case, payments are done at intervals
upon completion of work. The most preferable response rate would be 80% and above.

Student’s Last Name 3
Training and Supervision of the Data Collection Team
Depending on the roles that they play in the data collection process, team leaders,
supervisors, as well as the measures in the data collection should receive different training.
Developing skills of these individuals can be done using surveys to make sure that all necessary
questions in the company are resolved (Cleary, Horsfall and Hayter, 2014, pp.479). Regarding
supervision, the supervising team should be structured normally, such that there is one supervisor
in every two data collection teams. The supervisor should provide support as well as control the
teams on ordinary issues. Nonetheless, they should support teams that have problems with the
data collection method. Finally, the supervisor and the data collection coordinator should always
browse through the data collection method daily to identify missed entries and other errors.
Data Processing Requirements
Data processing refers to the process of collecting and manipulating items of data aimed
at producing meaningful information. It entails changing the information in any possible manner
that is dictated by the observer. Processes that will be involved in the data processing
requirement include validation, sorting, aggregation, summation, analysis, reporting, and
classification (Andreae, et al., 2016, pp.42). The data processing requirement will entail
validating the collected data. Also, data coding is to help in data preparation. Data coding is
essential is grouping and assigning values to the responses obtained during the data collection
method. Coding is important in the gap analysis because it makes it easy for the research team to
deal with simplified data (Syed and Nelson, 2015, pp.382). Nonetheless, data editing, which
entails checking for outliers along with basic data checks is also important in identifying and
clearing data points that are likely to affect the accuracy of the results.

Student’s Last Name 4
Potential Barriers
Several barriers are likely to arise when collecting data to investigate the customer
satisfaction problem. Such barriers are referred to as information quality barriers. The main
barriers that are likely to be encountered in this research include data availability. The barrier
involves difficulties that arise over time when storing large amounts of information that has got
conflicting and ambiguous concepts in different computer systems such as formats and codes
(Palinkas, et al., 2015, pp.537). The second barrier is data accuracy. A good electronic record of
the collected data eliminates reworks by capturing the data once at the source and providing the
data for reuse at later stages of the research. However, in practice, this is rarely achieved.
Nonetheless, such data may be less accurate, thus affecting the quality of the results. The last
barrier is data accessibility. Data accessibility is concerned with ethical issues linked to data
ownership, privacy, confidentiality, and security (Akidau, et al., 2015, pp.1794). This is a barrier
to the research because, without this information, an analyst cannot conduct research and the
manager cannot make a decision.
Recommendations
To address the above barriers, the following should be considered:
1. Regular maintenance of data as well as systems to address issues with changing
data requirements.
2. In monitoring, catching, and correcting errors at points of data transfer, human
resources and technology should remain dedicated.

Student’s Last Name 5
3. To improve data completeness, it is essential to develop minimal data set
standards including structured data entry.
Conclusion
The significance of the research is to investigate customer satisfaction concern at Cole
Supermarket. The research agency is to conduct the gap analysis on behalf of the organization
basing the research on the research objectives and the main considerations in the data collection
process. Lump-sum contract strategy is the best contract strategy to use with a response rate of
80 and above. Training and supervision of the data collection team improve the effectiveness and
efficiency of collecting data. During the process of data processing, the team can use coding to
simplify the data by assigning values to the responses. Nonetheless, coding is very effective in
gap analysis because it makes it easy for the research team to deal with simplified data. Some of
the potential barriers for this research include data accessibility, accuracy, and availability.
However, by following the recommendations, the research agency can overcome these barriers,
thus enhancing the accuracy and quality of the results.

Student’s Last Name 6
Bibliography
Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R.J., Lax, R.,
McVeety, S., Mills, D., Perry, F., Schmidt, E. and Whittle, S., 2015. The dataflow model: a
practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-
of-order data processing. Proceedings of the VLDB Endowment, 8(12), pp.1792-1803.
Andreae, M.H., Rhodes, E., Bourgoise, T., Carter, G.M., White, R.S., Indyk, D., Sacks, H. and
Rhodes, R., 2016. An ethical exploration of barriers to research on controlled drugs. The
American Journal of Bioethics, 16(4), pp.36-47.
Cleary, M., Horsfall, J. and Hayter, M., 2014. Data collection and sampling in qualitative
research: does size matter?. Journal of advanced nursing, 70(3), pp.473-475.
Palinkas, L.A., Horwitz, S.M., Green, C.A., Wisdom, J.P., Duan, N. and Hoagwood, K., 2015.
Purposeful sampling for qualitative data collection and analysis in mixed method implementation
research. Administration and policy in mental health and mental health services research, 42(5),
pp.533-544.
Palinkas, L.A., Horwitz, S.M., Green, C.A., Wisdom, J.P., Duan, N. and Hoagwood, K., 2015.
Purposeful sampling for qualitative data collection and analysis in mixed method implementation
research. Administration and policy in mental health and mental health services research, 42(5),
pp.533-544.
Syed, M. and Nelson, S.C., 2015. Guidelines for establishing reliability when coding narrative
data. Emerging Adulthood, 3(6), pp.375-387.