Introduction
Google is a major player in digital marketing and customer engagement. The company provides tools that help merchants seamlessly engage their customers. Digital marketing utilizes online or computer-based communication platforms and mechanisms to communicate with customers. The sellers seek to reach out to possible markets through text messages, emails, social media posts, graphics, and other marketing channels (Bullock, 2020).
Google Analytics dominated digital market scrutiny due to its complex and advanced tools, massive number of users, and efficiency. A detailed analysis of data retrieved from a merchandise store is presented in this report. The analysis aims to increase the user base, transactions, e-commerce conversion, and profits.
Types of Data Collected
Data analysis on a Google Merchandise store empowers sellers, developers, and store owners to make informed decisions. First, it is possible to collect a wide range of data using cookies. Site owners view data in two main categories: users and sessions (Bullock, 2020). The two categories are different in that a user can have multiple sessions, and a session can have multiple users. The main data categories are summarized below.
Users
Users refer to the unique visitors to one’s site as identified by Google. All site users are assigned a unique identification key stored on their browser cookies. The identities are essential in tracking user behavior, success and bounce rate, page views, time taken on different pages, and goal completion rate. Since users may visit a site several times or spend more time on a page, their identities are essential in data collection and analysis. Figure 1 below shows a summary of user data on Google Analytics.
Bounce Rate
When users leave a site without exploring its pages, it implies that they are not impressed for various reasons. The bounce rate measures the number of users who initiate only one request. This could be a request for the home page or any other one as found on Google search results. Such users do not trigger any other request from the server. As a result, the bounce rate is associated with poor performance. Figure 2 below is a report on the site bounce rate.
Sessions
Sessions last for thirty minutes and start as soon as a user makes the first request to the server. All activities carried out during this time, whether clicks, page views, page scrolls, or transactions, are associated with this session. The number of activities completed during this conclave determines the engagement rate (Albright, 2020). The data is essential in determining the areas or pages that need to be improved. Figure 3 shows a graphical report of user sessions.
Average Sessions Duration
The average number of sessions is computed in seconds as the standard time measurement. The total time taken in all sessions is divided by the number of sessions completed. As mentioned earlier, a session lasts for 30 seconds. However, a user may not be active during an entire session. In this regard, the computation only considers the times a user executed an activity. Figure 4 shows the average session duration for new and returning users.
Percentage of New Sessions
Analytics logs data every time a user starts a new session. The percentage of new sessions is obtained by dividing all new sessions by the total number of sessions (Bennet, 2017). For instance, a user may have five sessions on a site. In this regard, the total number of new sessions is four, and the percentage of new sessions is 80%. The calculation is done for all sessions and users visiting the site. The percentage of new sessions is summarized in Figure 5 below.
Sessions by Channel
Google Analytics groups users into channels for detailed computation and data presentation. They include social, email, paid, or organic, depending on one’s users. Each channel produces its sessions, and their analysis is essential in determining the best set of target users. Without such data, it would be impossible for merchants to make informed marketing decisions. A summary of sessions by channel is presented in Figure 6 below.
Pages per Session
As mentioned earlier, a session begins once a user loads the first page of a site. It does not need to be on the home page. During a session, a user may opt to stay on the opened page or browse others on the same site. Google Analytics associates the visitor’s unique identity key with the pages visited and logs the data. For example, if an e-commerce site user buys an item and leaves, the number of visited pages could be as high as five, depending on the website structure. A detailed view of pages per session on Google Analytics is summarized in Figure 7 below.
Goal Completions
The structure of a website depends on the underlying business logic. A news website is structured differently from an online store as they serve different purposes. Goal completions refer to the number of times a user accomplishes a specific aim on a site or application. On an e-commerce site or app, a user may need to browse products, add items to a cart, check out, add payment details, and track order status (Burgess, 2020). Such activities must be executed to qualify for “goal completion” in the given session. A sample of the data is summarized in Figure 8 below.
Pageviews
Conventionally, Google Analytics tracks all pages included by the developer or site owner. Whenever a visitor browses a page, it is added to the page views. The data is important for marketing personnel to propose necessary strategy changes. Most alterations aim to improve the website or page ratings and increase one’s competitiveness. The Pageviews attribute is considered liberal as it does not show the performance or failure of specific pages. A summary of pageviews on Google Analytics is presented in Figure 9 below.
Pageviews by Page
The metrics of different pages can be broken down to reveal their performance. The number of visits or views per page is shown for a given timeframe, depending on user settings. A page may be viewed more than others due to the nature of its contents and metadata. Developers, marketers, and SEO experts rely on such measurements to initiate appropriate changes and optimizations. Figure 10 presents a graphical preservation of pageviews per page on Google Analytics.
Data Analysis
Top Segments in The Fourth Quarter of 2022
Correlation Analysis of Key Parameters
The correlation analysis for key parameters is summarized in the figure below.
There was found a strong perfect positive correlation between “users” and “new users,” “sessions,” “transactions,” and “profits” (0.999273196, 0.998138289, 0.987610328, and 0.97451483, respectively). There is also a very high positive correlation between new users and sessions, transactions, and profits at 0.995466042, 0.982394423, and 0.968277286 apiece. Sessions and profits have a strong positive correlation at 0.984184642.
T-Test of Users and New Users
The small p-values suggest the null hypothesis should be rejected. In this case, the total number of users is not necessarily influenced by the number of “new users.”The p-value for one-tail analysis is 1×10-5, an indication of statistical significance. On the other hand, the corresponding figure for two-tail computation is 2.19×10-5. The mean value of users is 942.78, while that of new users is 834.64
T-Test of New Users and Bounce Rate
The paid t-test on “New users” and “bounce rate” results are presented in Figure 13 below. The average value of new users is 834.64, while the mean value of the bounce rate is 41.1009%. The Pearson correlation is -0.22297 with a one-tail p-value equivalent to 1.56×10-6.
T-Test of Transactions and Profits
The t-test of transactions and profits is summarized in Figure 14 above. The average value of transactions is 53.86, while that of profits is 5369.251. The analysis found a strong positive correlation between the two variables (0.992209). There is a negligible p-vale for north one-tail and two-tail computations (5.13×10-5 and 1.03×10-4).
T-Test for Users and Profits
Figure 15 shows the t-test analysis for paired variables: users and profits. The average number of users is 942.78, while that of profits is 5369.251 with 100 observations. There is a strong positive correlation of 0.974515 with a 99 degree of freedom. There are significant p-values for both one-tail and two-tail computations (9.27×10-5 and 1.85×10-4).
User Base in the Fourth Quarter of 2022
The trend in the number of users varied significantly over the specified period. Although there is a pattern: a rise followed by a decline in visitors per week, the changes are not entirely predictable. The largest number of users was recorded on December 7, 2022, with a total of 4174 visitors. It was a significant increase from 575 registered on December 3, 742 on December 4, 1465 on December 5, and 2003 on December 6, 2022. However, there is a significant decline in the number of users from December 12 to December 31.
Top 100 Markets in the Fourth Quarter of 2022
T-Test for Profit and E-commerce Conversion Rate
Figure 17 summarizes the t-test results for profit and e-commerce conversion rate. The average values for the variables are 1301.096 and 0.009484. The analysis shows a weak positive correlation between profits and the e-commerce conversion rate. The calculations are computed with a 99 degree of freedom. As a result, the p-values for one-tail and two-tail are 0.073137 and 0.146274, respectively. The corresponding critical values are 1.660391 (one-tail) and 1.984217 (two-tail) apiece.
T-Test for Transactions and E-commerce Conversion Rate
Figure 18 above presents the paired t-test results for transaction and e-commerce conversion rates. 100 observations were made, resulting in average values of 13.5 and 0.009484 for the respective variables. A weak positive correlation of 0.342199 was observed with a 99 degree of freedom. The resultant p-values were 0.065004 and 0.130008. Since all p-values are above 0.05, they are considered statistically insignificant.
Discussions
The results indicate several instances of strong positive correlation between variables, as shown in Figure 11. Although there are numerous cases of negative correlation, they are all weak and, hence, ignored in the analysis and discussions. The main emphasis is on how to rely on the results to improve the number of new and returning users, transactions, profits, and e-commerce conversion rate.
The number of users visiting a site depends on the number of returning users as well as those browsing for the first time. This explains the strong positive correlation between “users” and “new users.” The frequency and length of sessions are strongly dependent on the number of “new users”, registering a correlation value of 982394423. Transactions and profits are coupled with the number of users (irrespective of whether new or returning). The strong correlation between profits and sessions implies that the two variables are intertwined in their influence on the decision-making of marketing personnel.
Online shopping sites are very common nowadays as more people prefer to shop from the comfort of their homes. However, the success rates of such sites are staggering due to prevalent customer behavior (Bolt, 2020; Bonini, 2019). It is the responsibility of the developers, site owners, and marketing personnel to ensure the platforms are competitive and appealing to visitors (Ciampi et al., 2021). Modern e-commerce platforms have various tracking tools that record almost every user activity. As a result, the marketing personnel can review several performance metrics and act accordingly.
Most visitors expect to find information as quickly as possible whenever they visit a page. This saves them time and money, potentially making their experience better. On e-commerce sites, customers tend to abandon carts due to the inclusion of extra or hidden fees (Burgess, 2020). Usually, most people prefer to shop online due to price competitiveness but are repelled by additional charges. The costs could include shipping fees or taxes, which may not have been shown on the product description page. In this regard, all information should be provided upfront to limit cart abandonment and bounce rates.
Today, internet users are more conversant and concerned with their privacy than ever before. Most people avoid sites where they have to create accounts. Although many people are free to share their data, such as name, age, gender, contacts, and addresses, on social media, a growing number are concerned with sharing this information with unknown sites (Naeem & Ozuem, 2021). Such data provision requests tend to keep visitors away, increasing the bounce rate and lowering site ranking (Naeem, 2021). Hence, it is recommended that data be provided to visitors in guest mode for maximum rating.
Cyber Security is a major concern nowadays as technology companies like Google have made massive investments. Whenever users are cautioned about a potentially harmful or insecure site, most of them abandon browsing altogether (Jamra et al., 2020). Such situations translate to low sessions and high bounce rates. The problem can be resolved by acquiring the required security certificates and approvals. At the end of the day, users will be comfortable visiting one’s sites without having to comprehend security messages or warnings.
Website performance metrics such as low response speed are key factors in poor metrics. Major issues such as missing page errors, broken links or crashes repel customers who seek a better experience. Studies have shown that minor performance issues may not disgust visitors although it has a significant impact on their satisfaction. Sites should be subjected to thorough performance testing to assess responsiveness, downtime, and load times. Necessary improvements should be executed to suit customer needs.
Some sites provide content in an inappropriate foreign language. For instance, presenting web content in Russian or Chinese to nonlocals repels visitors. Although Google provides automatic translation, developers and site owners should ship content in multiple languages for better comprehension and savvy. It is also a common problem for most users to view prices in foreign currencies, especially online shopping platforms. In this regard, content, currencies, and time zones should be localized for better user experience and performance metrics.
Site visitors seek to gather information as quickly as possible. For example, news websites should provide useful information within the first few paragraphs of a story. Research websites should filter search results to show the most relevant content first. If users have to take a long time to access their desired content, they will likely seek a better experience on alternative sites or pages (Chen & Yang, 2021). Product details should accompany their prices, and categories of descriptions should be included to reduce the time needed for goal completion. Since placing essential information, such as price and expiry date, affects user experience, the data should be visible with minimal exposure.
Conclusion and Recommendations
Transactions, profits, and e-commerce conversion rates are influenced by the number of customers and bounce rate. In order to improve gains, marketers must target more customers, retain them, and facilitate an increase in transactions. Bounce rates are high with new users and are attributed to a bad experience. In this regard, the first step towards improving any metric on the site would be improving the ease and seamlessness of user interaction. The web pages should be engineered responsively, fast, and detailed at a glance. The length of sessions increases with the simplicity of a web page, visibility of essential information, navigation channels, and buttons.
Unnecessary or lengthy texts should be replaced with engaging graphics. Most people avoid long text, which results in higher bounce rates and shorter sessions. The textual content can be replaced with charts, appealing images, and other associative visuals, significantly improving sessions. Graphics help people understand the contents and themes of a webpage or an entire website, improving page rankings and overall engagement. Complex help texts and manuals can be replaced with short, clear, and lightweight videos for illustrations. They provide apprentice-based training for users while copying the steps in action. The videos may not require cameras for recording or production but can also include descriptive texts and sounds.
The developers, marketers, and site owners need to understand the customer journey as it helps decipher what the customer is looking for. Such comprehension helps place content in the right place, improving the durations taken in one session. The information on a site dictates visitor browsing patterns and depends on their preferences (citation). Marketers should also track visitor behavior through different pages to understand their preferences. This implies that users can be shown information that best suits them. The tracking should combine multiple metrics for effective results. The web content should also be formatted properly to improve visibility and accessibility.
Most users discard carts for various reasons, especially if there are additional or hidden charges. All prices should be displayed on the product description page. The checkout process should also be simplified to eliminate any confusing steps or requirements. The sites should also provide secure payment channels and data storage mechanisms. This is due to rampant cyber security breaches and data theft.
Some users may be concerned with sharing credit card details with online merchants. In response, the sellers should provide numerous payment options including cash on delivery, mobile money transfer, and checks. The users will be able to choose their preferred payment option. The sellers should also enact sound return policies for faulty or damaged products. The language and price preferences should be localized based on regions and customer settings for a better experience. Lastly, competitive prices and coupons should be implemented to attract and retain users.
References
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Bennet, T. (2017). The complete guide to direct traffic in Google Analytics. Moz. Web.
Bolt (2020). 14 Reasons for cart abandonment and strategies to improve conversions. Bolt. Web.
Bonini, J. (2019). The 10 Most-Tracked Google Analytics Metrics [Original Data]. Databox. Web.
Bullock, L. (2020). How to leverage content to keep people on your website longer. Smart Insights. Web.
Burgess, J. (2020). 4-step guide to tracking cart abandonment in Google Analytics. Yieldify. Web.
Chen, N., & Yang, Y. (2021). The impact of customer experience on consumer purchase intention in cross-border E-commerce-Taking network structural embeddedness as mediator variable. Journal of Retailing and Consumer Services, 59. Web.
Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation: The mediating role of entrepreneurial orientation. Journal of Business Research, 123, 1-13. Web.
Jamra, R. K., Anggorojati, B., Sensuse, D. I., & Suryono, R. R. (2020). Systematic review of issues and solutions for security in E-commerce. In 2020 International Conference on Electrical Engineering and Informatics (ICELTICs) (pp. 1-5). IEEE. Web.
Naeem, M. (2021). Do social media platforms develop consumer panic buying during the fear of the Covid-19 pandemic?Journal of Retailing and Consumer Services, 58. Web.
Naeem, M., & Ozuem, W. (2021). The role of social media in internet banking transition during COVID-19 pandemic: using multiple methods and sources in qualitative research. Journal of Retailing and Consumer Services, 60. Web.
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