Durham Improving St John Ambulance Website with Google Analytics Proposal 1. Proofreading 2. Identify logical flaws or deficiences in my proposal and writing Research Proposal
Using Google Analytics to improve the customer journey on St John Ambulance Website
1. Purpose Statement
Website and other online touch points have become more and more important for
organizations in contemporary world, where increasing number of people is getting used to
the virtual environment. In comparison with the traditional offline method, it is advantageous
that organizations now can track their interactions with the customer in a more detailed and
easier way, and store them as data for further usage. All the data, whether it is numerical or
textual, is valuable asset to the organization. With the development in analytics and data
mining, almost all kinds of data can be transformed into insights when handled with the right
method. However, the definition of a right method could be a complex and difficult question
to answer as it should be addressed according to the certain conditions in each case. In
addition, the fast changing user behavior and preferences are also making it necessary to
adjust the method based on the current circumstances. For example, cookies is replacing
HTML to track the users behavior on website because it adapts better to the new norm where
users now access website with multiple devices and platforms, and that will change the way
to handle the data compared to the HTTP-access-log analysis era (Clark, et al., 2014).
Furthermore, most of the website analytics and tracking tools are updated regularly with new
features and changes. Considering all the factors, it is important that research in this topic
should be tailored and also up to date.
St John Ambulance is a UK based charity which focuses on providing first aid services, first
aid training and first aid products to the public. Its website (sja.org.uk) is the major touch
point SJA uses to connect and communicate with its customer and the public. An article on
McKinsey Quarterly suggests that positive customer journey experience can help to improve
the overall performance including conversion rate and word of mouth (Court et al., 2009).
Therefore, it is reasonable and necessary to ensure website is properly designed and userfriendly. The charity uses Google Analytics to track the data generated from its website and
hoping to analyze those data, transforming into actionable insights to help improving the
customer booking and purchasing journey on its website, which are the first aid courses
booking and first aid products more specifically.
The aim of the research is to improve the customer booking and purchasing journey on SJA
website. In order to address the problem more efficiently and more effectively, one main
research question is raised along with two sub-questions. The main research question is: How
Google Analytics can be applied to improve the customer booking and purchasing journey of
St John Ambulance website? Two sub-questions are: 1. Which traffic source has the best
overall performance? 2. What improvement can be made to boost sales on St John
2. Literature review
The initial literature review of the research mainly comprises by studies on three topics: user
experience/customer journey, web analytics and website usability. which are the three major
fields related to the research question. The understanding of the business process and
customer journey experience is the foundation to determine the appropriate method of
analytics on website data. Richardson (2010) states that customer journey is the steps the
users will go through when using the product. Lemon and Verhoef (2016) suggest that
companies are putting more efforts on customer journey than ever before, because users now
interact with organizations through many different touch points. Some of them are hard to
manage, but also have great potentials for the firm. For example, the huge volume of
customer-to-customer interactions on social media are something never seen before. Word of
mouth is becoming a important factor to the success of the firm, and a positive experience
over the customer journey can help to improve it, as well as other metrics like conversion
rate, tremendously (Court et al., 2009).
To improve the customer journey on a website, the website usability is a vital factor to be
considered. According to Guideline on Usability published in ISO 9241-11 (1998), usability
stands for the extent that a product can be used to achieve certain goals with effectiveness,
efficiency and satisfaction. Krug (2014) gives usability a more down-to-earth definition as a
person of average (or even below average) ability and experience can figure out how to use
the thing to accomplish something without it being more trouble than its worth.
There are plenty of researches done in recent years, but there are no consensus on models to
evaluate the website usability (Lee and Kozar, 2012). In order to address this problem, Garret
et al. (2016) did a comprehensive literature review on the top 100 papers with keywords
design, usability and websites on Google Scholar, summarizing elements that is most often
discussed in past studies. However, the result is still debatable, because some of the elements
are overlapping in nature. For example, the most discussed element navigation is
overlapping with the third most discussed element organization, as both of them are
referring the website should be well-organized and structured so users can achieve their goals
more efficiently. In addition, reviewed papers should be constrained to the same industry,
because a E-commerce website and a News website clearly have different customer need and
requirements, and thus their standards will be also different.
Lee and Kozar (2012) criticize previous studies on e-business website used indirect metrics
such as revisit and duration to evaluate the performance of the website, rather than direct
measurements. So they designed a research based on a questionnaire and asked their
participants to browse Amazon.com and score different usability elements as well as their
purchase intention. The result suggests that eight elements have direct causal relationship to
purchase intention, and content relevance is the strongest factor. However, this study still
have some flaws. All the participants are students in university who has an average age of
27.74 years old, which is not representative to the whole Amazon.com users. Second, authors
used purchase intention as the evaluation factor, but it is still not equal to a successful order
to the e-business, as participants might not behave as what they have responded in the
questionnaire. A proof to that is another study conducted by Barba et al. (2013), which
described the whole process to develop and improve the ‘help’ function in a university library
website with the application of Google Analytics. In the beginning of the study, researchers
first conducted a survey asking whether participants will use the help function or not if it is
available. However, when the tool is officially released, users visit data showed a different
result compared to what they answered in the survey. The authors state that analytics are an
unbiased way to evaluate users actual behavior but it can only solve half of the problem as it
cannot speak to user needs and thus it is best used in cooperation with usability studies.
Atterer et al. (2006) also suggest that the typical dilemma for usability studies is researchers
have to invite and engage as much participants or data as possible to ensure the reliability of
statistics, and it could be better solved with web analytics tools.
Google Analytics is currently one of the most widely used web analytics tool. Clark et al.
(2014) state that it might record the data inaccurately, because if users blocked or deleted the
cookies or scripting, it will not be able to track those visits. Therefore, it tends to under-count
the actual visit data. However, Google Analytics successfully solved the biggest issue exist in
the old HTTP-log-analysis method, where Google Analytics will not record visit from webcrawlers that could account for 90% of the traffic but provide no insight. Clark et al. (2014)
also suggest that Google Analytics can be used under the academic context but should be
applied with some techniques and skills when interpreting some specific metrics like bounce
rate and unique visitors. Plaza (2009) used Google Analytics data to study the effectiveness
of return visit to the website, and also the relationship between traffic source and navigation
depth, and the results suggest that for that specific website, referring site has the worst
performance in all the traffic sources, which surprised the author as it is considered as a great
channel from the qualitative analysis perspective. The result again prove that data and
analytics is a valuable tool to use as it will provide an unbiased and objective answer. Plaza
(2011) further studied the relationship between bounce rate, traffic source and return visit
using regression analysis, and the result suggests that the less the bounce rate, the longer the
user will use the website, which thus also prove that website usability can increase the
performance of a website. Romanowski and Konak (2016) propose another perspective to
conduct research with Google Analytics data, as they score each web page a interactive
scores based on the content format, such as pure text and video, and use Google Analytics
data to study the relationship between user engagement and interactive score of the web page.
In conclusion, the best way to conduct research in this topic is to combine both qualitative
research method and quantitative research method. Analytics can reveal what users actually
do on the website. However, without the understanding of the customer journey and website
usability evaluation, the result generated from analytics cannot be transformed into actionable
3. Research Method
The target website being evaluated is St John Ambulance website (sja.org.uk). There are
mainly five functions the website serves, which are first aid advice, courses booking, first aid
supplies shop, donation and public communication. Among the five functions listed, courses
booking, first aid supplies shop and donation are generating income for the charity, and the
website now accounts for most of the transactions and sales for the organization. As proposed
in the research question, the primary functions being evaluated are the courses booking and
first aid supplies shop. The major customers can be categorized as businesses, schools and
individuals, and there are a wide range of courses that fit each segments need. The courses
are the major income source of the charity and will be booked online and conducted offline,
where the customer will receive notification after the payment. The first aid supplies shop is
operated as the normal E-commerce model, where customers order the product online and get
it delivered to them.
The research will be conducted with the mixed method, where both quantitative method and
qualitative method will be applied. As stated earlier, analytics can reveal the users actual
behavior on the website, but the result cannot be transformed into actionable insights without
understanding the customer journey and website usability evaluation. During the meantime,
pure qualitative analysis also have clear disadvantages. Participant tends to behave
differently from what they answered in surveys and questionnaires (Barba et al., 2013).
Traditional method of usability studies like focus group need to invite and engage a great
number of participant and data to ensure the reliability of the result, which could be timeconsuming and difficult to operate. Therefore, the optimal method to study this topic is the
mixed method where combined data analytics and literature review.
Literature review on past papers and industrial practices will be conducted first, as there are
plenty of related studies in recent years, and they can guide the research to be done in a more
efficient and effective way. Moreover, analytics is also vital to determine whether certain
relationships exist in this particular website. Except for evaluating available metrics on
Google Analytics, some of the data can also be exported and conduct further analysis. Excel
and Python will be adopted depending on the certain size of each dataset exported. Potential
data cleaning method will also be conducted on Excel or Python. Nevertheless, most data
displayed on Google Analytics can be used without cleaning as it has already been processed.
As stated earlier, the result for each website will be varied so it is important to make sure
what element is causing a positive result on this website. Regression analysis can be helpful
to confirm such relationship. First, conduct regression analysis to address whether navigation
depth (pages viewed) and return visit is positively associated with conversion rate or not.
Second, evaluate what is the relationship between return visit and navigation depth. More
relationships might emerged and then assessed during the research.
To address the question of what traffic source has the best overall performance, clustering is
a powerful method to investigate the user behavior. It can be applied to see whether certain
patterns exist among same traffic source.
Customer journey mapping will be applied as a tool to illustrate detailed steps in the customer
journey, which can help to understand and finding gaps in the full process. Website usability
evaluation model will also be used to identify the flaws in the customer journey. After
determining potential gaps and flaws with qualitative analysis, Google Analytics will be used
to validate whether those deficiencies actually exist. For example, if the customer journey
mapping shows a dead-end within the process, bounce rate of that page can be checked on
Google Analytics to see whether users actually recognize it as a dead-end. In addition,
metrics on mobile devices and desktop devices will be compared as increasing number of
users are now accessing the website with mobile devices. The overall performance of each
traffic source will be assessed based on multiple metrics and dimensions, such as lead
generation, conversion rate and user product interest.
The time range of the data will be January 2020 to June 2020. It is arguable that users
behavior might change due to the influences of pandemic, but the new version of the website
is launched and being tracked by Google Analytics from the end of 2019, so there are only
limited data range selection available.
There will be no human participants involved in the study, and all the data used will be
collected from Google Analytics. Users identity information will not be disclosed on Google
Analytics as users will be assigned and identified as a 20 digits randomized numbers when
they access the website for the first time. In addition, no personal information such as name
and age will be available on Google Analytics. Therefore, there are no ethical conditions
should be specifically considered when doing the study.
Atterer, R., Wnuk, M. and Schmidt, A. (2006) Knowing the Users Every Move User
Activity Tracking for Website Usability Evaluation and Implicit Interaction, in Proceedings
of the 15th international conference on World Wide Web, pp. 203212.
Barba, I. et al. (2013) Web Analytics Reveal User Behavior: TTU Libraries Experience
with Google Analytics, Journal of Web Librarianship, 7(4), pp. 389400.
Clark, D. J., Nicholas, D. and R.Jamali, H. (2014) Evaluating information seeking and use in
the changing virtual world: the emerging role of Google Analytics, Learned Publishing,
27(3), pp. 185194. doi: https://doi.org/10.1087/20140304.
Court, D. et al. (2009) The Consumer Decision Journey, McKinsey Quarterly, 2009(3), pp.
Garett, R. et al. (2016) A literature review: Website Design and User Engagement, Online J
Commun Media Technol, 3(6), pp. 114.
ISO 9241-11 (1998) Ergonomic Requirements for Office Work with Visual Display
Terminals (VDTs) – Part 11: Guidance on Usability.
Krug, S. (2014) Dont Make Me Think.
Lee, Y. and Kozar, K. A. (2012) Understanding of website usability: Specifying and
measuring constructs and their relationships, Decision Support System, 52, pp. 450463.
Lemon, K. N. and Verhoef, P. C. (2016) Understanding customer experience throughout the
customer journey, Journal of Marketing, 80(November 2016), pp. 6996.
Plaza, B. (2009) Monitering web traffic source effectiveness with Google Analytics, Aslib
Proceedings: New Information Perspectives, 61(5), pp. 474482. doi:
Plaza, B. (2011) Google Analytics for measuring website performance, Tourism
Management, 32, pp. 477481.
Richardson, A. (2010) Using customer journey maps to improve customer experience,
Harvard Business Review, 15(1), pp. 25.
Romanowski, B. and Konak, A. (2016) Using Google Analytics to Improve the Course
Website of a Database Course, in. 2016 ASEE Mid-Atlantic Section Conference, American
Society for Engineering Education.
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