Jose Garcia
QUMT 6310
UCI Research Analysis
Summary/Overview
UCI through its operations has been able to win over customers and make sales. This has
resulted in a well-performing organization. One thing UCI has not focused on is on getting to
know their customers and store visitors. If they can identify the people attracted to their store,
they will be better prepared to adjust their marketing strategies as well as pricing and optimize
their performance. UCI is willing to undergo the task of learning about their store visitors and we
are here to help.
Problem/Research Question
Proper customer and market knowledge is necessary to develop successful products,
marketing campaigns, and pricing strategies. Without the knowledge of these important factors,
organizations will make decisions without a clear direction. Since UCI does not have knowledge
of the type of people that visit their store most of the marketing efforts and pricing have been
carried on from a blind perspective. UCI needs an in-depth understanding of who their clients are
to better market and price their products. The research identified the type of people that visit
UCI’s stores based on a 16-question survey. The survey responses produced data to help classify
the type of people that visit the UCI stores. This data will then allow UCI to create more accurate
marketing materials, better price their products, and provide information for expanding bicycle
riding globally. The objective of this study is to collect data about cycling store visitors and
develop a demographical, geographical, and psychological profile through analysis of the
collected data. From there, relationships between factors will be discovered and communicated
to UCI. These relationships will allow us to discover UCI’s target customers which then can be
continued to be targeted or chosen to pursue new customers. The main variable that we believe
has a strong relationship is income level and the bicycle price purchased.
Hypothesis
Alternative Hypothesis: There is a relationship between income and sales of bikes.
Null Hypothesis: There is not relationship between income and sales of bikes.
Alternative Hypothesis: b1 ≠ 0
Null Hypothesis: b1 = 0
Jose Garcia
QUMT 6310
Background/Literature Review
Because of the considerable time that has passed since the bicycle was invented and its
global success there is a good amount of research on the bicycle market from different country
perspectives. R. Petty has an older study that investigates the Schwinn market in America post
World War 2. It focuses on the state of the market in1930’s, 40’s, and 50’s. Imported bikes were
in demand and to counteract that the United States market promoted lowering of costs through
modernization of production. The study also sheds light on Schwinn’s distribution and branding
strategies which in this modern time we can learn from. The Italian bicycle brand Bianci has also
been dissected and studied for its marketing practices and approaches shedding some light on
potential ideas that can be implemented by UCI. More recently C. Zhao conducted a study on
bike-forward planning and attitudes in Beijing and Copenhagen and saw a great positive push
from city planners on implementing bike-friendly city designs in Copenhagen. Beijing citizens
saw the city as too big to utilize bicycles as transportation and inferior to public transportation.
Research Design/Methodology
The research consisted of data collection from store visitors inform of an in person digital
survey conducted by field workers. Data from these surveys was then added to spreadsheets to be
cleaned and prepared for analysis. From there, a linear regression between income and sales was
executed. Through observation and graphs and charts, patterns in data were discovered and
income level showed to influence whether the visitor purchased a bicycle and the price of the
bike. The linear regression is going to allow us to see the relationship between these variables
and be useful to predict sales according to income.
Sampling/Data Collection
We used the systematic sampling method. Systematic sampling will allow us to collect
data in a randomized manner to ensure we reduce the introduction of bias. We surveyed 50% of
the store’s visitors for a week. Different stores throughout the world were chosen. The field
worker asked every other visitor of the store to fill in the survey. The interview would entail
asking the respondents to answer the 6 questions stated above. The questions and the way they
are asked will result in short and concise answers that are easy to record and won’t be
cumbersome to the participants. The fieldworkers will be instructed to be courteous and
professional and keep a consistent method of executing the surveys with everyone.
The data that we gathered describes the visitors to the store. The respondents were asked
to answer in a few short words that capture their ideas. Responses for every sixteen questions
were collected per survey. We used iPads for the distribution of digital surveys to store visitors.
We provide the fieldworkers with badges to foster credibility and made sure they wore the
appropriate attire. After all the data was collected in each store the data was cleaned and
organized on a spreadsheet. The spreadsheet was organized by respondents and will include the
16 responses.
Jose Garcia
QUMT 6310
Data Description
The entire data set includes survey responses from 514 different people. Each individual
has 16 variables to it. These variables include marital status, gender, children, homeowner,
education, occupation, cars, commute region, age, bike ownership, income, bike price, model of
interest, and if they purchased. The variables we focused on were income and bike price while
filtering with purchase status and age. Bike price was our dependent and income was the
independent variable. The data was organized and cleaned and analyzed in Excel and Tableau
was used for further analysis and data visualization.
Exhibit 1
Exhibit 2
Here we have two exhibits. These graphs and charts help understand the data and get to
know the visitors and customers of UCI. Exhibit 1 shows the price range of bikes and the
preference of the customers. The 1,000 - 2,000 range was highly preferred by visitors of the
stores. We can also see that the majority of the store visitors come from North America,
followed by Europe, followed by the Pacific. Lastly, exhibit 1 shows us the number of visitors
categorized by their income. The most common visitor income is 40,000 next is 60,000, and
third is 70,000. Exhibit 2 illustrates purchaser data. This data can be of great use to UCI. The
most common purchaser income amounts were 40,000, 60,000, and 30,000 in that order. Next,
Exhibit 2 shows the percentages of male and female purchasers. Lastly, the age range
percentages of the purchasers are illustrated.
Jose Garcia
QUMT 6310
Exhibit 3
Exhibit 3 includes descriptive statistics for the data utilized. The mean is extremely useful
for seeing the average for the 4 variables. We are able to see that the average age of the visitors
is 25 years old’s. This can highlight the main target customer. UCI can then decide to focus on
this age range or develop other markets. Another important descriptive statistic is the mean bike
price. The mean bike price is 2,904.09.
Data Processing and Analysis
The statistical process used to analyze the data was linear regression. A UCI’s goal is to
obtain insight from the data and discover relationships as well as use the data to create
predictions linear regression is the most appropriate. The two software used were Excel and
Tableau and all of this was run on a Mac laptop.
There were plenty of missing data points discovered within the data set during cleaning
and preparation. To address this, a N/A category was added to gender to account for individuals
not wanting to share this information, for missing income figures average income for their
profession was imputed, and pairwise deletion for the children category would be utilized when
analyzing to ensure retainment of maximum data.
Exhibit 4
Exhibit 5
Jose Garcia
QUMT 6310
statistical model
After visual analysis of the scatter plot and trend line, and review of the linear regression
and ANOVA stats we can see no real relationship between these two variables. The slope is not
exactly 0 but it is close enough at -.0006265. To add to this, the ANOVA calculated a
significance of .6952 which is not lower than the set alpha at .05 resulting in a non-significant
model.
Conclusions
In conclusion, there is no significant relationship between income level and the price of
bikes purchased. Individuals with higher incomes do not buy more expensive bicycles and
individuals with lower incomes don’t buy lower-priced bicycles. Bicycle prices are distributed
all over the range within each income level, but you can see favoring around the average price of
2,444 throughout the income levels. This goes against our original beliefs and that has something
to do with the bias brought into the study. The belief that individuals with more income would
buy more expensive bicycles is not true. A limitation of this study is that UCI stores are only
located in North America, Europe, so these spending habits might not be true in other regions.
Even though this specific analysis did not result in significant variable relationship other insights
are observable from the data. Thanks to the data set developed during this research we can
continue looking at other variables for relationships.
Jose Garcia
QUMT 6310
References
1. Laird, P. W. (2012). <I>peddling bicycles to America: The rise of an
industry</i> (review). Technology and Culture, 53(2), 505–507.
https://doi.org/10.1353/tech.2012.0052
2. Mari, C. (2015), "Putting the Italians on bicycles: marketing at Bianchi, 1885-1955",
Journal of Historical Research in Marketing, Vol. 7 No. 1, pp. 133-158.
https://doi.org/10.1108/JHRM-07-2013-0049
3. Petty, R. (2007, May). Peddling Schwinn Bicycles: Marketing Lessons from the Leading
Post-WWII US Bicycle Brand. In Proceedings of the Conference on Historical Analysis
and Research in Marketing (Vol. 13, pp. 162-171).
4. Zhao, C., Carstensen, T. A., Nielsen, T. A., & Olafsson, A. S. (2018). Bicyclefriendly infrastructure planning in Beijing and Copenhagen - between adapting design
solutions and learning local planning cultures. Journal of Transport Geography, 68, 149–
159. https://doi.org/10.1016/j.jtrangeo.2018.03.003