Business Question

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Assignment Details

Business Research Report Analysis

This assignment builds upon your work in all previous units.

Chapter 14 of the textbook provides an overview of the business research reporting (oral and written).

When researching, the final step is to answer a business question or resolve a business problem and to report findings, conclusions drawn, and recommendations regarding the research question or resolution to the research problem. The report is provided in writing and sometimes presented orally to the person or group sponsoring the research.

The Marketing Vice President (VP) was tasked to answer this question: What new smartphone feature should be included in the following product release to increase interest in the company’s smartphone products?

A research design was created, and the data have been collected and analyzed. The Marketing VP and team have reviewed the analysis from Unit 4 and now need to create a written report.

Prepare a business research report for the company’s executive management team that includes the following:

The methods and processes used in the research study
Findings from the data analysis
Conclusions that can be drawn from the findings
Recommendations for the new feature that should be included in the next phone release (based on specific findings in the data analysis and the conclusions drawn from the analysis)

Ethical and Legal Considerations

Be sure to consider in the report the ethical and legal implications of adding the feature to the smartphone products.

Support for Recommendations

Make a case for your recommendations by integrating information from cited reputable sources in the fields of business, supply chain, or marketing research, in addition to specific data analysis results.

Deliverable Requirements

Your business research report analysis should be 25 pages in length (5 pages/unit = 25 total) as an accumulation of all the pages from previous units. Be sure to cite your sources using APA properly; include your references and in-text citations.

Submitting your assignment in APA format means, at a minimum, that you will need the following:

Title page: Remember the running head. The title should be in all capitals.
Length: 25 pages minimum
Body: This begins on the page following the title page and must be double-spaced (be careful not to triple- or quadruple-space between paragraphs). The typeface should be 12-pt. Times Roman or 12-pt. Courier in regular black type. Do not use color, bold type, or italics, except as required for APA-level headings and references. The deliverable length of the body of your paper for this assignment is 25 pages. In-body academic citations to support your decisions and analysis are required. A variety of academic sources is encouraged.
Reference page: References that align with your in-body academic sources are listed on the final page of your paper. The references must be in APA format using appropriate spacing, hanging indent, italics, and uppercase and lowercase usage as appropriate for the type of resource used. Remember, the Reference page is not a bibliography but a further listing of the abbreviated in-body citations used in the paper. Every referenced item must have a corresponding in-body citation.

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Grading Rubric: Maximum 140 Points

Grading Criteria Points
Deliverable requirements addressed; understanding of material and presenter’s message and intent are clear 60 points Followed assignment guidelines and criteria 30 pts Yes/Complete Mostly Not at all
Overall quality 30 pts Excellent Satisfactory Needs Improvement
Scholarly research that supports the writer’s position is properly acknowledged and cited, and direct quotations do not exceed 10% of the word count of the body of the assignment deliverable 30 points Title page, table of contents, tables, exhibits, appendices or reference page included 15 pts Yes Partially Not at all
Content is original with less than 35% match 15 pts Yes 36–50% 51% or over
Critical thinking: Position is well-justified; logical flow; examples 15 points Position is justified with examples 7.5 pts Excellent Satisfactory Needs Improvement
Presentation flows logically 7.5 pts Excellent Satisfactory Needs Improvement
Structure: Includes introduction and conclusion, proper paragraph formatting, and reads as a polished academic paper or professional presentation, as appropriate for the required assignment deliverable 15 points Includes introduction and conclusion 5 pts Excellent Satisfactory Needs Improvement
Paragraphs are formatted properly 5 pts Excellent Satisfactory Needs Improvement
Reads as polished academic paper or presentation 5 pts Excellent Satisfactory Needs Improvement
Mechanical: No spelling, grammatical, or punctuation errors 10 points Spelling and grammar are accurate 5 pts Yes Partially Not at all
Punctuation is accurate 5 pts Yes Partially Not at all
APA: Deliverable is cited properly according to the APA Publication Manual 10 points References are cited in accordance with APA formatting 5 pts Yes Partially Not at all
Reference page is included 5 pts Yes Partially Not at al

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Data Collection Strategies
Jami Britton
American InterContinental University
Professor: Dr. Boris
Course: MGT510-2305B-01
Date: 12/11/2023
Data Collection Strategies
In business, information is power since it leads to more effective decision-making that
contributes to the success and profitability of a company. Data collection is hence necessary
since it allows a business to get an understanding of customer preferences, behavior, and trends.
This leads to more informed decisions when it comes to product development and marketing
since insight gained can be leveraged to customize products, services, and marketing according
to customer preferences and behaviors which contributes to improving the customer experiences
and hence customer satisfaction (Smith & Bazis, 2021). Through this, a brand can build customer
loyalty and retain valuable customers. Investing in business research also allows a business to be
able to adapt to changing market conditions which is necessary to ensure continued success. A
business that does not keep up with changing market conditions cannot be successful in the long
run since it is likely to be pushed out of the market. The phone industry is for instance very
competitive and brands such as Apple and Samsung invest billions in research and development
to not only keep up with customers’ needs and preferences but to also ensure that these
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businesses remain at the forefront of new revolutionary technologies (Haro et al., 2020). In
marketing research, data collection strategies are critical as they inform how businesses
personalize their marketing and provide better customer experiences. This is since the data needs
to be in the right context and structure to be useful. Otherwise, this data is just a set of random
facts that have no real direction in terms of how it can help businesses make data-backed
decisions. Businesses can take either a quantitative, a qualitative, or a mixed-methods approach
to data collection.
Quantitative and Qualitative Data Collection
Quantitative data collection involves measurable data that can be expressed in figures or
numerical form. Specific questions are hence asked to extract information from prospects or
customers. Through this it is possible to test models, gauge trends as well as segment customers.
This approach is instrumental in assessing the usage of a product, identifying areas of
improvement, measuring customer sentiment, and recognizing opportunities that will enhance
the experience of the existing customer base. After collecting data mathematical calculations and
statistical analyses are performed to help shed light on important insights regarding the market.
Quantitative data collection is often preferred since it is cheaper and takes less time when
compared with qualitative data collection. This data is also standardized or normalized and hence
requires less data cleaning (Smith & Bazis, 2021).
Quantitative data collection methods include surveys or questionnaires, polling, and
experiments. Through surveys, it is possible to administer a structured set of close-ended
questions to a large audience. This is often used in business when there is the need to gather
numerical data on customer preferences, attitudes, and behaviors. Through polling on the other
hand, it is possible to get a quick snapshot of public opinion and can be very useful when making
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product development and marketing decisions (Poth et al., 2023). Through experiments, it is
possible to test hypotheses as well as understand causal relationships between variables of
interest. Data obtained from these methods can be used to test ideas, make predictions, learn
more about customers, determine trends, and hence make informed business decisions. Data
obtained from quantitative methods is considered objective and reliable (Smith & Bazis, 2021).
Qualitative data on the other hand happens to be more descriptive than numerical. This
data is meant to answer questions such as why and how and is gained through observations,
focus groups, or in-depth interviews. This method uses open-ended questions and the answers
obtained are descriptive and have little to no numerical value. This approach allows for
businesses to be able to delve deeper into the thoughts and behaviors of prospective or already
established customers. Data obtained from this approach allows for the generation of new ideas
and marketing opportunities, the formulation of predictions as well as explaining data obtained
through quantitative methods (Smith & Bazis, 2021). This data is hence more subjective in
nature but facilitates a greater depth of understanding. Interviews and focus groups are the most
common approaches to qualitative data collection. Interviews facilitate the understanding of
complex motivations, and perceptions, and the uncovering of nuanced insights. Focus groups on
the other hand capture diverse perspectives and generate insights that come from group
interactions. Market researchers can also observe how participants interact with a product
without making direct contact. Through this, it is possible to get an understanding of the natural
inclinations regarding the product based on how they interact with the product. Qualitative data
collection can be prone to human errors due to its subjective nature while open-ended questions
can make using this data difficult (Smith & Bazis, 2021).
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A mixed methods approach is often preferred in business research since it is possible to
benefit from the complementary strengths of both approaches. It is for instance possible to obtain
depth from qualitative methods and breadth from quantitative methods. Through qualitative data
collection, one can delve into individual experiences, motivations, and behaviors while
quantitative methods allow for findings to be generalized to a larger population. The subjectivity
of qualitative approaches can also be countered by the objectivity of quantitative approaches.
Combining these methods hence facilitates a more comprehensive understanding of market
conditions and customers’ behaviors, attitudes, and preferences (Smith & Bazis, 2021).
Assessment of Quantitative and Qualitative Data Collection Options
To determine which smartphone features, lead consumers to make a purchase, the
company will, first, conduct a focus group using a representative sample of the customer base.
This will allow for diverse perspectives regarding what features contribute to making the choice
to buy a smartphone. During the focus group, the discussion will hence be centered on
smartphone features to allow the participants to share their opinions and experiences. Openended discussions will hence be facilitated to prompt unexpected insight from the participants
and hence the discovery of new ideas. The focus group will be recorded and detailed notes will
also be taken on participant responses and interactions. After the focus group is completed, the
researchers will comb through all this information to identify common themes and patterns
related to smartphone features. The insight obtained from this process will then be used to design
close-ended survey questions that will be administered online. This survey will be made to reach
as many consumers as possible and will be available on the company website, social media
channels, and will be sent to emails of already established customers. Incentives may be offered
such as gift cards to encourage participation and increase response rates. This data will then be
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analyzed using statistical tools and through this, it will be possible to lend statistical validity to
insight obtained from qualitative data collection. Correlation or contradictions can be established
by comparing the focus group results with the survey results. The combined data however offers
a more complete picture of the market (Poth et al., 2023).
Justification
Purchase intentions have become crucial in the overly competitive smartphone market.
To sustain one’s market share there is the need to constantly upgrade various features including
product appearance, features, service quality, and software. Smartphones have also become
embedded into the daily lives of customers since they have become more of a necessity than a
luxury (Haro et al., 2020). There is hence the need to understand how various features impact
consumers’ lives. Through the focus group, it is hence possible to delve deep into consumer
preferences and determine how they influence purchase decisions. The survey on the other hand
allows for the researcher to confirm that sentiments shared by the small focus group are
applicable to most of the customer base. The insight shared during the focus group is hence
compared and validated against a larger sample. This improves reliability and strengthens the
validity of the results and ensures that the company makes data-backed decisions regarding
product development and marketing that will ultimately ensure success. This approach hence
leverages the strengths of qualitative and quantitative data collection methods hence ensuring the
company gets a holistic understanding of the market and at the same time minimizes the
limitations associated with each method.
Conclusion
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Using either a qualitative or a quantitative approach in data collection can present a
limited view of the market, however, by combining these approaches, a complete picture is
formed that ensures that a research problem is explored in terms of both depth and breadth.
Based on this discussion, it is possible to observe that a mixed methods approach allows
researchers to understand individual experiences and preferences surrounding smartphone
features while at the same time ensuring that these findings can be applied to the general
population. Qualitative data collection sets the stage for quantitative data collection since
insights gained from focus groups inform the design of the surveys. Quantitative data collection
makes it possible to quantify trends, measure preferences, and test hypotheses. Qualitative
approaches add context while quantitative approaches ensure the reliability and validity of
findings. In the end, a more complete picture is formed which leads to informed decisionmaking.
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References
Haro, A., Oktaviana, D., Dewi, A. T., Anisa, W., & Suangkupon, A. (2020). The influence of
brand image and service quality towards purchase intention and its impact on the
purchase decision of Samsung smartphone. KnE Social Sciences, 329-336.
Poth, C., Bullock, E. P., & Eppel, E. (2023). Adaptive mixed methods research design practices
to address complexity in business and management research. Handbook of Mixed
Methods Research in Business and Management, 329-347.
Smith, M. C. H., & Bazis, P. S. (2021). Conducting mixed methods research systematic
methodological reviews: A review of practice and recommendations. Journal of Mixed
Methods Research, 15(4), 546-566.
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Qualitative Data Analysis
Jami Britton
American InterContinental University
Professor: Dr. Boris
Course: MGT510-2305B-01
Date: 12/17/2023
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Qualitative Data Analysis
Mobile phones are no longer used solely for communication purposes; as this technology
has evolved, these devices have become a source of entertainment, comfort, and convenience for
consumers. In the race to develop novel products there is hence the need to consider how these
devices fit into the consumers’ way of life, their professions, and their hobbies. Due to increased
smartphone usage, various factors go into making the decision to purchase a specific brand and
model. Mobile phone companies, hence, need to consider features consumers value the most
before making major product development and marketing decisions since this ensures that the
products they put on the market are, in fact, in line with consumers’ tastes and preferences (Santy
& Atika, 2020). Innovative features and applications are hence constantly being added to
smartphones to meet customers’ needs and to allow these devices to perform a wide range of
functions. The market is incredibly competitive and companies are required to constantly work
hard to attract and retain customers. There is, hence, a race to constantly put out new
smartphones frequently with updated features and technologies as a means of gaining a
competitive advantage and avoiding being pushed out of the market (Santy & Atika, 2020). The
race to market is brutal, and failure to keep up with new technologies and capabilities could
mean the death of a company. This hyper-competitive nature of the market is intensified by the
ease of entry into the market and the many brands in the global market that are eager to gain
market share and greater brand recognition; as such, when one company falls, there is another
one willing to take its place. Features that influence purchasing decisions include product
features, brand image, perceived quality and value, and price, among others (Santy & Atika,
2020). The company took a qualitative approach to data collection and analysis in an attempt to
determine which product features the consumers considered carried the most weight in
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determining whether they made a purchasing decision or not. This paper hence discusses the
results of this research and how customer responses would influence decision-making in the
company.
Qualitative Data Analysis Strategy
The first step in the process is to arrange the data in a manner that makes sense. There is
the need to go through the transcripts of the interviews and systematically arrange the largely
unstructured information. The responses are then coded based on key features identified by the
participants that contribute to making their purchasing decisions. Computer-assisted qualitative
data analysis software can be used for this purpose. Group-related codes are then placed in
broader categories, such as battery life, camera quality, and the display of the phone. This
process will be simplified by using smaller samples of the customer feedback data that will be
used to establish the set of codes that capture the various themes that emerge, and as the sample
size increases, it will be possible to build on the emerging patterns (Lester et al., 2020). With
increasing sample size, adjustments will be made to enhance the codes and ensure greater
accuracy and consistency in capturing the nuances of the feedback. As overarching themes
related to smartphone purchasing decisions begin to emerge, it is then possible to explore the
connections between the different features and purchasing decisions. Through coding, it is also
possible to compress the tremendous amount of information collected into a digestible format
(Lester et al., 2020). It is also possible to build on patterns and gain incredible insight into the
data that facilitates informed decision-making. The use of software in this process not only
speeds up the process but can allow the uncovering of hidden insight that may not be visible to
the naked eye, hence facilitating a more comprehensive analysis of customer feedback.
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What follows is the actual data analysis that can relate the information to different
demographics as well as customer profiles, hence providing actionable insight that can guide the
company’s decision-making process. Using the responses, customer segmentation is, for
instance, possible based on demographic factors, age, customer behavior, and interests, among
other considerations. This data can then be visually represented through charts and graphs,
allowing for the frequency and prominence of each feature to be connected to the customers’
responses (Mezmir, 2020). The findings can then be organized into a comprehensive report
detailing the key features that influence customers’ purchasing decisions supported by quotes and
data from the analysis. There is the need to communicate and narrate the findings in a manner
that paints the full picture to the decision makers since the findings and insight are instrumental
in deciding the course of action in the development of the smartphone as well as the marketing.
A detailed report utilizing insight from the data analysis sets the company up for success
(Mezmir, 2020). The report should hence sort the information according to demographics and
specific customer information that can make decision-making faster and more straightforward.
The report can hence include specific recommendations regarding how the company should
approach product development and marketing of the new model of the smartphone.
The Strategy and Answering the Business Research Problem
Through the strategy described, the manager can develop an appropriate approach to
product development, marketing, and customer attraction and retention. By ensuring that the
product being released into the market meets the customers’ needs, the product will sell, hence
ensuring that the company continues to be profitable.
Product Development
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Battery life was identified as the most prominent feature that contributes to making a
purchasing decision. 32 of the 60 participants identified long battery life as a key feature they
consider before making a purchasing decision, with some even specifying a battery life of at least
7 hours. During the product development process, this will hence be a key consideration since
the company needs to ensure that the smartphone they develop has a long battery life. 29 of the
participants, on the other hand, discussed the quality of the camera as another feature that they
consider important when making a purchasing decision. The quality of the camera, including
features such as crisp and clear pictures, sharp resolution, and camera pixels, was consistently
highlighted. The clarity and crispness of the display were significant to 17 participants. Mobile
payment functionality was mentioned by 16 participants. Other features that mentioned by a few
users include remote control capability, multiple windows view, and processing power. During
the product development process, these features will hence be prioritized to ensure that the
smartphone developed is in alignment with the prevailing needs of the customers.
Marketing
Following product development and the incorporation of these specific features, the
marketing approach can be designed around how these specific smartphones tick all these boxes.
Through this approach, it is possible to draw in customer since they recognize that this product
embodies the features they value and can hence be incorporated into their daily lives to increase
the convenience of handling their responsibilities. The marketing campaign is hence likely to be
successful, which means increased sales for the company and increased profitability. By ensuring
that the company has the finger on the pulse of what customers value, they are likely to have an
edge in the market and, hence, be able to draw in more customers and effectively increase the
company’s market share (Arjuna & Ilmi, 2019). By ensuring the final product incorporates all
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these elements that customers value, it is possible to appeal to a wide range of customers. This
approach allows for a deep understanding of customer preferences in terms of product features,
and hence, informed decision-making occurs during the marketing of the product. The marketing
efforts are hence tailored to address customer preferences effectively (Arjuna & Ilmi, 2019).
Customer Attraction and Retention
In a dynamic market such as the mobile phone market, there is the need to ensure that
customer retention strategies are effective in meeting the prevailing customer sentiments
regarding specific products. There is, hence, the need to apply strategies that are responsive and
customer-centric. Customers’ opinions about a brand can be leveraged to guide how a brand is
able to enhance or revise its reputation to ensure that it remains relevant and visible in the
market. Through meeting customer expectations, a company is able to build a cult-like following
that ensures the business continues to thrive and reach new levels. This has been key to the
success of companies such as Apple and Samsung (Arjuna & Ilmi, 2019)
Conclusion
The smartphone market is incredibly competitive, and as such, companies need to
understand and meet the needs of the customers in order to remain in business and expand their
market share. Through qualitative data analysis, a company can make informed decisions
regarding its products. The insight gained can be leveraged to make strategic choices that will
contribute to the increased profitability of the company. This is based on the fact that it uncovers
nuanced insights into the features that truly resonate with the customers, allowing the company
to enhance existing products and introduce new features that are in alignment with customers’
expectations. The comprehensive report that is obtained from the qualitative data analysis can
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hence be used to inform product development, marketing strategies, and customer retention
strategies.
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References
Arjuna, H., & Ilmi, S. (2019). Effect of brand image, price, and quality of product on the
smartphone purchase decision. EkBis: Jurnal Ekonomi dan Bisnis, 3(2), 294-305.
Lester, J. N., Cho, Y., & Lochmiller, C. R. (2020). Learning to do qualitative data analysis: A
starting point. Human resource development review, 19(1), 94-106.
Mezmir, E. A. (2020). Qualitative data analysis: An overview of data reduction, data display, and
interpretation. Research on humanities and social sciences, 10(21), 15-27.
Santy, R. D., & Atika, S. D. (2020, January). Purchasing decisions in terms of perceived quality
and product knowledge. In International Conference on Business, Economic, Social
Science, and Humanities–Economics, Business and Management Track (ICOBEST-EBM
2019) (pp. 94-99). Atlantis Press.
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Descriptive Statistics in Business Research
Jami Britton
American InterContinental University
Professor: Dr. Boris
Course: MGT510-2305B-01
Date: 12/26/2023
Descriptive Statistics in Business Research
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Descriptive statistics can be very useful in business research since they provide brief
information coefficients that can summarize an entire data set. They include measures of central
tendency, variability, and frequency distribution. Measures of central tendency include mean,
mode, and median, measures of variance on the other hand include range, variance, and standard
deviation, and finally the shape of the distribution is determined by skewness and kurtosis.
Through descriptive statistics, it is hence possible to compress a lot of complex information into
a digestible format in order to facilitate decision-making. Through descriptive statistics, it is
possible to present data in a meaningful and understandable way to facilitate simple
interpretation since trends and patterns can be easily identified (Mooi et al., 2018). In this
particular case, the goal is to determine whether supplier A or B produces batteries that last 11
hours. Based on the data collected and analyzed, it is hence possible to determine whether the
data supports the claims made by the suppliers through the use of descriptive statistics. The mean
in this case represents the average value in the data set of suppliers A and B, the median on the
other hand is the middle value in the data set while the mode is the most reoccurring value. The
standard deviation represents the degree of variation in the data set and a lower value indicates
less variability. The sample variation represents the degree to which the numbers are spread out
while the range is the difference between the minimum and maximum values (Sarstedt et al.,
2019). The minimum and maximum represent the extreme points of battery life in the case of
both suppliers. The sum totals up the observation while the count represents the number of items
in the test. Using descriptive statistics, it is hence possible to determine which suppliers offer
longer battery life allowing the company to make an informed choice on which supplier to use in
product development.
Infographic
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Evaluation of 2 Descriptive Statistics
Informed by the different types of descriptive statistics, one measure of central tendency
and one measure of variability will be used in the analysis. The mean and the standard deviation
will hence be used since they offer the most comprehensive answer to the research question and
the objective of choosing the right supplier.
The mean in this particular case refers to the average battery life of the 10 batteries that
were selected from each supplier. The mean duration of Supplier A who is currently supplying
the company batteries is 598.7 minutes or approximately 9.98 hours while the mean duration for
Supplier B is 697 minutes or approximately 11.6 hours. This is significant considering the
qualitative research found that the battery life was the most significant consideration made by
customers before deciding to make a purchase. The higher duration offered by supplier B can
hence be used in the marketing to allow the company to generate more sales. The improvement
in battery life draws in the customers and they may hence end up buying the new phone model.
Supplier B made the claim that their batteries lasted 11 hours and this research found that these
batteries can even surpass expectations and have a far superior battery life when compared to
Supplier A batteries.
Consumers have come to depend on their phones in almost every aspect of their life.
People go everywhere with their phones and they are integral to their personal and professional,
as such they require a long battery life to ensure that they can go about their day without needing
to constantly stop what they are doing in order to charge their phones. This is a big
inconvenience that calls for new models of smartphones to have a long battery life to
accommodate consumers’ needs (Khanna & Singh, 2023). Based on the fact that batteries from
Supplier B last over 11 hours, they present a greater appeal and could be the right choice for the
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new smartphone model since this fact can drive sales making the new model incredibly
profitable.
Standard deviation on the other hand is a measure of dispersion in the dataset. Batteries
that report a lower standard deviation are hence likely to be more consistent in performance and
to report less variability. Batteries from supplier A reported a standard deviation of 24.72
minutes while those from supplier B reported a standard deviation of 10.85 minutes which means
that batteries from Supplier B are likely to offer more consistent performance across tests. This
therefore means when these batteries are being used by customers, the batteries from Supplier B
are likely to be more predictable and report more consistent durations between charges when
compared to batteries from Supplier A. In some cases, battery life performance tends to decline
when smartphones have been in use for some time. Consistent performance of batteries is
however required since customers are aware that if they leave the house with a fully charged
battery, they do not have to worry that their phones will die unexpectedly due to unpredictable
performance in the battery. A lower standard deviation in this case hence implies a more reliable
user experience and the companies can hence rest easy that customers will not complain about
the performance of these batteries (Cooksey & Cooksey, 2020). Based on these results supplier B
batteries are likely to offer a more uniform user experience with a reduced likelihood of batteries
that die unexpectedly after a short duration. Therefore, while marketing the new model of the
smartphone consistency in battery performance can become another selling point that can be
used in the advertisements and branding of the product.
Through the mean and the standard deviation, it is possible to get a more comprehensive
understanding of battery performance. In the case of Supplier B, the report of a higher mean
duration combined with a lower standard deviation means that this supplier is providing batteries
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that are superior in performance in terms of battery life and this performance is consistent and
not based on extreme values or outliers. These measures instill confidence in the reliability of
Supplier B batteries. It is however important to note that these findings are based on a small
sample of only 10 batteries and a larger sample may hence be needed to validate these findings.
Furthermore, the tests used do not capture all real-world scenarios that customers may encounter
and there are certain behavioral and environmental conditions that may impact battery
performance (Cooksey & Cooksey, 2020).
Persuasive Rationale
While deciding between the two descriptive statistic types, the mean is of greater interest
since it answers the research question of whether the batteries from Supplier A or B can last 11
hours. This measure hence provides a very straightforward answer in this regard which is also
easy to interpret. The standard deviation on the other hand adds depth to the answer and sheds
light on the consistency and reliability of these batteries. The mean however aligns seamlessly
with the research objective since it shows the average performance of the batteries that were used
in the test. This measure also facilitates clear and concise messaging to the relevant stakeholders,
especially the decision-makers in the company who need this information to decide on the right
supplier especially since it is easy to make comparisons between Supplier A and Supplier B
(Sarstedt et al., 2019). This numerical value can hence inform the supplier selection, the market
positioning as well as the product differentiation. Furthermore, the battery life is the measure that
the consumers are interested in and the mean is thereby more appropriate since it gives an
approximate of the performance of these batteries. The company can thereby confidently
communicate to customers that these batteries will last over 11 hours when marketing the new
model. The consumers are interested in the average performance instead of the extreme values
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making the mean the appropriate descriptive measure to communicate battery performance.
Consumer behavior often tends to be in line with central tendencies since consumers tend to
make decisions based on averages and as such the mean is a very valuable statistic while making
product development and marketing decisions (Khanna & Singh, 2023).
Conclusion
Based on this discussion, it is possible to understand the value descriptive statistics offers
to a company. The discussion also highlights the fact that Supplier B offers the superior battery
in terms of performance and after making the necessary cost-related considerations, the company
should go with this battery since it aligns with what consumers in the market are looking for. The
mean, in this case, is the descriptive statistic that holds the utmost value since it answers the
research questions and meets the research objective.
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References
Cooksey, R. W., & Cooksey, R. W. (2020). Descriptive statistics for summarizing data.
Illustrating statistical procedures: Finding meaning in quantitative data, 61-139.
Khanna, M., & Singh, N. P. (2023). A Study on Factors that Affecting Purchase Decision of
Smartphone. Journal of Informatics Education and Research, 3(2).
Mooi, E., Sarstedt, M., Mooi-Reci, I., Mooi, E., Sarstedt, M., & Mooi-Reci, I. (2018).
Descriptive Statistics. Market Research: The Process, Data, and Methods Using Stata,
95-152.
Sarstedt, M., Mooi, E., Sarstedt, M., & Mooi, E. (2019). Descriptive statistics. A Concise Guide
to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics, 91
150.

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