Personas creation using Google Analytics

This case study investigates how a marketing persona may be created with the use of data gathered by cookies and with the use of Google Analytics. The data gathered originates from the largest IT e-learning platform VITA. The basis of our research was measurement in the Google Analytics tool and the WooCommerce plug-in within the WordPress content management system. Our case study shows how a marketing persona may be created and recommends that once such a persona is created it needs to be validated repeatedly with data on real customers before it may be used for marketing purposes.

Introduction

Marketing personas are a useful way to create a relatable and understandable representation of a target group. This persona always needs to be created by the use of significant amounts of qualitative and quantitative data. The most common approach in the online sphere is to gather this data with the use of cookies and analyze it with online analytics services. Although it is a very common practice, companies and students alike can be found to lack understanding of this topic, especially combined with the use of marketing personas. This topic is further complicated by regulation relating to personal data, especially the General Data Protection Regulation (GDPR). In this paper we are therefore going to investigate this legislation in relation to the use of cookies and analytics tools of Google Analytics and create a persona based on data gathered by the VITA e-learning platform about its users.
Our research questions are:
1. What user data do companies have available?
2. How traffic data from Google Analytics can be used to create personas?
3. What are the differences between male and female visitors to the VITA e-learning?
4. Who is the ideal visitor (persona) of the VITA platform?
5. Is it true, based on data from VITA, that buyer and audience personas are not the same?

In the first chapter we are going to review available literature on GDPR and personal data, cookies, Google Analytics and personas. The second chapter is where the reader can find information on the methodology that was used in the creation of a persona. The paper then ends with discussion and conclusions.

1 Literature review

1.1 GDPR and personal data

The General Data Protection Regulation (GDPR) is currently the most important legislative document within the European Union, which defines what can be considered personal data, how it may be gathered and used and also the rights of the concerned persons. In §2 the law states that personal information is any data concerning the identification of a natural person that make up their physical, physiological, genetical, psychic, mental, economic, cultural or social identity (GDPR 2018). From that follows that under some circumstances personal data may be considered personal online identifiers assigned to individuals by technical devices such as IP addresses, cookies, RFID location data as well as email addresses when they are unique and make a person identifiable. The reason for this is that this data may be used to identify the person by lawful means by simple searches online or offline (Pawera and Veselý 2018, p. 127).
Gathering personal data may only be done with explicit consent given by the concerned person. This is regulated in §14 which states that an operator may ask for consent only separately, clearly, accessibly and understandably. The operator must be able to prove at any time that consent has been given. The concerned person has the right to revoke their consent at any time and should be able to do so the same way it was given. Some aspects of this paragraph are further specified in §19. There the law obliges the operator to make information available to the concerned person before consent is given. This includes the contact information of the operator, the purpose for which data is processed, the legitimate interests of the operator or of a third party, if the operator is inclined to take the information to 3rd countries or international organisations. Additionally, information must be provided as to how long data will be stored, about the rights of the concerned person, if they are required to provide this information and what happens if they do not and if there is the existence of automated individual decision-making including profiling. When an operator is in possession of personal data, they are required to abide by the seven principles that make up the base of GDPR, Lawfulness, Purpose limitation, Data minimisation, Accuracy, Storage limitation, Integrity and confidentiality, Accountability. They are required to uphold the conditions that were stated before consent was granted and must report any changes that are going to happen to all concerned persons. As mentioned before, the concerned person may take away their consent at any time and §23 also makes it possible that they may ask for deletion of any and all of their personal data in possession of the operator, which the operator must do without delay (GDPR 2018).

1.2 Cookies and user data

Http cookies are small text files often including identifiers that are created by web servers upon connection and sent to browsers. These cookies are then sent back each time the browser requests a new webpage. This way preferences, login information, habits, browsing information and activity (HP 2018). Http cookies are widely used to manage online experiences by users. Their wide use, unique identifiers and the amount of data that is gathered makes them not only ideal for marketing purposes and website management, but also for malicious actors. While cookies are simple text files that cannot change, therefore cannot be used as viruses, they may be used for spying on users. In relation to GDPR, cookies may only be used with the consent of the concerned person apart from the most basic and necessary cookies that are required to use the service.
There are three types of cookies:
• First-party cookies – created directly by websites that a user is browsing. As long as the webpage is reputable, they may be considered safe.
• Third party cookies – cookies generated by websites that the user is not currently browsing. Most often these are advertising cookies and cookies of analytics companies. Here each different advertisement that is present on an open website may generate a cookie even if the user has never clicked on an advertisement. Afterwards these cookies may continue to track the user to any website that contains these advertisements.
• Zombie cookies – a special type of cookie, that tracks a user without their consent. This cookie is directly installed on user’s device and may be automatically restored even after it was deleted. These may be also used by analytics companies, but also by the aforementioned malicious actors (Kaspersky 2021).

The use of cookies in online marketing is very significant as they are a source of large amounts of data on the behavior of equally large numbers of users. They are a quick way of getting behavior-based feedback through for example A/B testing. They also may be used to enhance customer experiences such as offering products, services or advertisements based on the user’s interest and are a way of targeting marketing activities.

Table 1: Overview of cookies and their purpose
Source: Authors

1.3 Google Analytics

Based on Google data and the HotJar report, Google Analytics is the most widely used analytics tool in the world. There are approximately 30 million websites and applications worldwide that use Google Analytics to track and analyze traffic. Of them about 32,000 web pages are in Slovakia and their use continues to grow in the future (Trends Builtwith 2021). Google Analytics provides website owners with JavaScript tags (libraries) to record information about the page a user has seen, for example the URL of the page. The Google Analytics JavaScript libraries use HTTP cookies to „remember” what a user has done on previous pages / interactions with the website. Google Analytics supports three JavaScript libraries (tags) for measuring website usage: gtag.js, analytics.js, and ga.js. Both versions of Google Analytics, Universal Analytics and Google Analytics 4, use first-party cookies (Google Developers 2021). Audience and behavior reports are used to analyze users’ website traffic and their behavior. In our work, we are primarily interested in the Audience with subreports: Overview, Active Users, User Explorer, which we use when creating personas. In the audience, we analyze demographics and visitor behavior, how they interact with content, and what technologies they use to access the web. When processing data from Google Analytics, we use the segment as a subset of data to compile and identify a male persona (Zheng and Peltsverger 2015, p. 3).

1.4 Personas

A simple definition of a Persona is „a summary of the characteristics, needs, motivations and environment of a key type of web site user”. A more specific definition from the Foviance guide to segmented personas is: „A persona is a fictional character that communicates the primary characteristics of a group of users, identified and selected as a key target through the use of segmentation data, across the company in a usable and effective manner”. We create personas based on qualitative and quantitative data about the target group and current or past customers (Smart Insights 2021). The main purpose is to predict behavior of users, based on their past behavior, to engagingly tell our marketing stories in the right tone, to the right people, and with the right words. The benefits of using personas are:
1. gain a better understanding of your ideal customer and consistent perception of who we are talking to,
2. segments/targets marketing and prioritization of product updates, improvement of services, optimization of sales techniques,
3. improves internal and external marketing processes and easy transfer of information, simple explanation to anyone else,
4. supports empathy and overcome objections of customers,
5. increases conversions a identifies negative personas.

In addition to the benefits, certain problems are also associated with personas:
1. cannot be used internationally,
2. one person will never capture the entire target group,
3. idealization of the customer.

When determining goals and finding out which persona will suit them best there are two types of personas. These are the following:
1. Buyer personas – Goal is to convert them to customers.
2. Audience personas – There are involved in the conversation with you. Goal is to share and like your content to spread your reputation and brand.

Based on Hubspot data (note [1]), on average, in up to 68% cases the buyer and audience persona are not the same persona.

2 Methodology

In line with the research goals, the authors used a qualitative method of the case study with cluster analysis. We analyzed data from Google Analytics about users of the largest Slovak IT online educational platform called VITA. Based on data from Google Analytics, we have compiled the following research questions:
1. What user data do companies have available?
2. How traffic data from Google Analytics can be used to create personas?
3. What are the differences between male female visitors to the VITA e-learning?
4. Who is the ideal visitor (persona) of the VITA platform?
5. Is it true, based on data from VITA, that buyer and an audience persona is not an equal persona?

The basis of our research was measurement in the Google Analytics tool and the WooCommerce plug-in within the WordPress content management system. Part of the quantitative research was the classification, i. e. sorting and segmentation of user information. Using deduction, we identified a male persona. The research sample consists of visitors to the VITA e-learning platform. These are potential clients of IT Academy, s. r. o. and VITA Company, s. r. o., based in Bratislava. The main business of these companies is education and provision of certification exams, especially in the field of information technology, marketing and management. VITA Company has an innovative educational system called VITA, which is implemented by its own e-learning portal using the WordPress content management system. VITA stands for Virtual IT Academy. VITA Company ensures the operation, administration of the educational portal together with the sale of accredited and certified online courses. IT Academy provides the creation of online content and courses, as well as the sale of online courses or employee training. analyzed data on traffic and visitors to the e-learning platform. We analyzed the data for the period 1. 1. 2020 – 1. 1. 2021 in Google Analytics. Based on the audience data, we created 2 segments, namely the men and women segment.

Table 2: Research sample of visitors the VITA e-learning platform
Source: Authors

3 Case study – VITA

In Google Analytics, we have identified basic audience metrics for both the women’s and men’s segments in Figure 1. Women make up 27.76%, men 20.42% and gender could not be determined 51.82% of the total data.
We found that the average session duration is nearly 13 minutes for men and nearly 9 minutes for women. For women, we also noticed a higher bounce rate and number of new users. Although the proportion of women in traffic to Google Analytics is 7.34% higher than that of men, we have decided, based on internal order data from customers, to create a male person. We compiled a persona for the men segment called Mario thoughtful.
The data showed us that the average age of an ideal male customer is 31 years old and he is employed as a programmer. Based on geographical data, we found out that the ideal customer lives in Bratislava, he likes to be educated in the category of programming languages. He is more of an introvert and an analytical type of personality. His goal is continuous education in the field of programming and obtaining certification. He often buys in e-shops and likes to travel. The most common frustrations are caused by work, data loss, or technical problems. Despite these frustrations, he would not change the job.

Figure 1: Basic metrics on traffic to the VITA e-learning platform
Source: Authors

Figure 2: Male person according to Google Analytics data
Source: Authors

Discussion and conclusion

Companies have access to user data from orders and analytics tools like Google Analytics (RQ1). Using cluster analysis in Audience tool, we grouped customers into clusters based on sex, demographics and behavior (RQ2). Based on data of Google Analytics, we found that the majority of visitors, up to 27%, are women, as can be seen in Figure 1. Women visit the VITA platform more often than men. Male visitors have 1.4 times higher pages/session, avg. session duration is higher by almost 4 minutes and the bounce rate is lower by about 9% for men (RQ3) The ideal male customer (buyer persona) is a man with an average age of 31, employed as a programmer who lives in Bratislava, likes to be educated in the category of programming languages. He is more of an introvert and an analytical type of personality. Its goal is continuous education in the field of programming and obtaining certification. He often buys in e-shops and likes to travel. The most common frustrations are caused by work, data loss, or technical problems. Despite these frustrations, he would not change the job (RQ4). Based on qualitative research and data analysis, we recommend implementing the creation of people based on data from Google Analytics and compare them with data from real customer orders, so we can better target users and satisfy their requirements and needs. Personas are used to better target ads, adapt the content of online courses to the needs of customers. We confirm the information from Hubspot based on data from VITA that the buyer and an audience person is not an equal person (RQ5).

Poznámky/Notes

[1] https://blog.hubspot.com/marketing/buyer-persona-research
The research was conducted in accordance with the objectives of the VEGA 1/0737/20 grant project called Consumer Literacy and Changes in Consumer Preferences when Buying Slovak Products.

Literatúra/List of References

1. Act no.18/2018 Coll.on the protection of personal data
2. hubspot.com, 2021. How to create detailed buyer personas for your business. [online]. [cit. 2021-06-03]. Available at: <https://blog.hubspot.com/marketing/buyer-persona-research>
3. Google Developers, 2021. Google Analytics cookie usage on websites. 2021. [online]. [cit. 2021-06-03]. Available at: <https://developers.google.com/analytics/devguides/collection/analyticsjs/cookie-usage>
4. Hotjar, 2021. Top 20 web analytics tools used by experts in 2021. 2021. [online]. [cit. 2021-06-05]. Available at: <https://www.hotjar.com/web-analytics/tools/>
5. HP, 2018. Computer cookies: What they are and how they work. 2018. [online]. [cit. 2021-06-06]. Available at: <https://www.hp.com/us-en/shop/tech-takes/what-are-computer-cookies>
6. Chaffey, D. and Patron, M., 2012. From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics. In: Journal of Direct, Data and Digital Marketing Practice. 2012, 14(1), 30-45. ISSN 1746-0174. [online]. [cit. 2021-06-06]. Available at: <https://link.springer.com/article/10.1057%2Fdddmp.2012.20>
7. Kaspersky, 2021. What are cookies? 2021. [online]. [cit. 2021-06-06]. Available at: <https://www.kaspersky.com/resource-center/definitions/cookies>
8. Pawera, R. and Veselý, P., 2018. Zneužívanie osobných údajov v praxi. In: Aktuálne výzvy prevencie počítačovej kriminality. Bratislava: Akadémia Policajného zboru v Bratislave. 2018. p. 127-135. ISBN 978-80-8054-774-5. [online]. [cit. 2021-06-06]. Available at: <https://www.akademiapz.sk/sites/default/files/KIM/ZBORN%C3%8DK%2021.3.2018%20WEB_0.PDF>
9. Smart Insights, 2021. Marketing personas. 2021. [online]. [cit. 2021-06-05]. Available at: <https://www.smartinsights.com/persuasion-marketing/marketing-personas/>
10. Trends builtwith, 2021. Google Analytics usage statistics. 2021. [online]. [cit. 2021-06-05]. Available at: <https://trends.builtwith.com/analytics/Google-Analytics>
11. Zheng, J. and Peltsverger, S., 2015. Web analytics overview. In: Encyclopedia of Information Science and Technology. Southern Polytechnic State University, USA. 2015. p. 3-9. [online]. [cit. 2021-06-03]. Available at: <https://www.researchgate.net/publication/272815693_Web_Analytics_Overview>

Kľúčové slová/Key words

GDPR, personal data, user data, cookies, Google Analytics, personas, marketing
GDPR, osobné údaje, používateľské údaje, cookies, Google Analytics, osoby, marketing

JEL klasifikácia/JEL Classification

C8, M31, M37

Résumé

Tvorba persón s využitím Google Analytics

Táto prípadová štúdia skúma, ako možno vytvoriť marketingovú osobu pomocou údajov zhromaždených použitím súborov cookie a pomocou služby Google Analytics. Zozbierané údaje pochádzajú z najväčšej IT e-learningovej platformy VITA. Základom nášho výskumu bolo meranie v nástroji Google Analytics a v module WooCommerce v rámci redakčného systému WordPress. Naša prípadová štúdia ukazuje, ako môže byť vytvorená marketingová osobnosť, a odporúča, aby sa po vytvorení takejto osoby opakovane overili údaje na skutočných zákazníkoch predtým, ako ju možno použiť na marketingové účely.

Recenzované/Reviewed

10. September 2021 / 25. September 2021