Netflix: Digital Marketing Strategy

Abstract

This dissertation analyzes the effects of digital marketing techniques on consumer behavior using Netflix as a case study. Netflix’s recommendation model of advertisement, which uses an algorithm-based marketing system to influence consumer-purchasing decisions, is the focus of the current investigation. From this background, this probe is designed to understand the experiences of Netflix users as they relate to the company’s algorithm-based recommendations system. The goal is to find out its influence on consumer behavior and cultural preferences when making online purchases. A key part of this investigation discusses the extent that Netflix’s product algorithm or recommendation model has influenced the cultural preferences of its consumers on the online streaming platform. Similarly, this investigation seeks to find out the extent that Netflix’s recommendation system has aroused consumers’ curiosity about the streaming service’s recommendation system. Two focus group discussions involving five participants, each, helped to generate data on the above-mentioned research areas. The findings of the investigation revealed that the algorithm-based marketing model influenced the cultural preferences of Netflix consumers by playing the role of a choice architect. Furthermore, the system helped consumers to identify products that suit their needs from a seemingly endless list of options. However, the ability to manipulate consumer behavior using algorithms remains a risk in the implementation of the recommendation-based marketing system.

Introduction

Background of the Study

Technology has changed the way we live and work in the contemporary world. Consequently, its effects have been felt in different industries with far-reaching implications on profitability and consumer behavior (Xiaoping and Tao, 2021). The entertainment industry is perhaps one of the most affected sectors of the global economy because digital advancements in product development have revolutionized purchasing behaviors (Anderl and Witt, 2020). Particularly, the growth of entertainment streaming services has created a shift in the development of entertainment content across consumer entertainment platforms (Operational Delivery Profession, 2020). The most notable effect has been the collapse of the theatre film industry, which depended on foot traffic to make sales. Indeed, people no longer go to the theatres to watch their latest movies because they can do so at the comfort of their homes.

In the internet world, the live streaming of digital content has been traditionally associated with the gaming industry. Different sectors have since benefitted from this development through technological transfers (Duncan, 2020). For example, the events business sector thrived on the backdrop of live digital content streaming during the corona virus period (Gilpin, 2021). Marketing communications services were also sustained in the same manner during the crisis. Companies that are associated with the provision of these services are in a unique position to leverage the present market demand of online services to boost their businesses.

In this investigation, Netflix is selected as a case study to understand the impact of the algorithm-based marketing model on consumer behavior. The company’s dominant presence in the digital entertainment streaming business justifies its selection in the study (Zhang and Zhang, 2018). The decision to use this company as a case study was likwise informed by the fact that the Californian-based firm controls about 44% of Europe’s on-demand market share of streaming services (Wayne and Uribe Sandoval, 2021). Its closest competitor – Amazon – only controls about 32% of the same market (Wayne and Sienkiewicz, 2022). Overall, today Netflix has a subscriber base of about 130 million people and a market presence that spreads across more than 190 countries (Roth, 2022). Its global operations provide a case study analysis of the impact of its marketing plan on consumer behaviors.

Problem Statement

Netflix has enjoyed a steady stream of customers from its conception. However, the firm has been unable to maintain the increase in subscription numbers due to cancellations, especially from long-term users (Cunningham and Scarlata, 2020). This reason explains why, in 2022, the company reported its first loss in subscription numbers (Roth, 2022). The problem was recently exacerbated when Netflix announced a plan to increase its subscription fees. In the first quarter of the year 2022, the firm lost more than 3 million subscribers due to this reason (Roth, 2022). Netflix’s revenue model is based on the subscription format where users watch a variety of films, or television shows on a digital platform after paying a monthly or annual subscription fee (Zhang and Zhang, 2018). Therefore, a reduction in subscription numbers implies a similar decline in profitability.

One of the main reasons for the cancellation of Netflix’s subscription accounts is its contentious recommendations system, which promises to supply users with movies to to watch. At the same time, some subscribers have claimed to have access to similar movies on alternative streaming applications, such as Hulu (Potter, 2021). Part of the reason for Netflix’s decrease in subscription numbers stems from a change in management attitude that affects consumer behavioral practices, such as password sharing (Roth, 2022). The increase in the subscription rate for existing users has worsened matters as well.

To mitigate these challenges, Netflix announced plans to develop cheaper –ad-supported subscription plans for its users. However, critics have questioned the fairness of the firm’s algorithm system, which recommends movies and films to different groups of customers (Mattison and Brouthers, 2021; Xiao, 2020). These criticisms stem from the perception that the company intentionally misrepresents the contents of films to attract additional viewership (Al-Kwifi, Farha, and Zaraket, 2020). Linked to these concerns, Netflix has witnessed a significant drop in its share price due to increased numbers of cancellations. Its rivals are also riling under the same pressure.

Research Aim

As highlighted in this study, the focus of the present investigation is on understanding the effects of Netflix’s marketing strategies on its users. However, given that the company has a broad-based marketing plan, the current study will focus on its recommendation system. It is designed to enable paying customers to refer their friends to the company for a reward.

Research Questions

In the course of formulating the research questions for this study, it is important to understand that the present study investigated the impact of Netflix on consumer behaviors and culture. The following research questions underpin the investigation:

What views do customers hold about Netflix’s algorithm-based recommendations system?
How does Netflix’s algorithm-based model influence consumer behavior?
To what extent has Netflix’s marketing strategies influenced consumers’ cultural preferences?
To what extent has Netflix’s recommendation system aroused consumers’ curiosity about the streaming service’s recommendation system?

Importance of Study

The findings of this study will be important in bridging the knowledge gap that exists between the implementation of marketing plans and the actual user experience of companies when using selected services. This analogy will be equally useful in helping to understand general trends in the marketing field, particularly as they relate to the consumption of entertainment content. Overall, the findings of this study are useful in generating new content for users worldwide.

Literature Review

This chapter examines the state of the extant body of literature on the research topic. It is fixated on understanding key areas of scholarly research that have been explored with links to the current purpose and objectives of the study. Key sections of this review explore the merits and demerits of the recommendations system and its use in developing marketing plans. This chapter also contains discussions about the impact of different recommendation systems on consumer behavior as well as the role of this system on choice facilitation. The influence of the recommendation advertisement model promotes cultural tastes and consumer autonomy to make decisions about purchasing behaviors that dominate discussions in this section of the study. However, before delving into the details of this analysis, it is important to understand the theoretical foundation of the present investigation.

Theoretical Framework

A dual theoretical framework is adopted for this review. It is predicated on understanding consumer-purchasing decisions based on a review of broad-based factors influencing the actions of users globally (Roth, 2022). At the same time, this theoretical review focuses on the cognitive processes influencing consumer purchasing decisions and the effects of digital marketing techniques on their outcomes (Deakin and Nicolescu, 2022). To this end, the actor network and critical consciousness theories form the basis for the dual theoretical framework mentioned above.

Actor-Network Theory (ANT)

The actor-network theory explains the nature of interactions between human and non-human actors. It suggests that both sets of players operate in a networked system where the actions of one group of people affect those of another. Developed by Michael Callon and Bruno Latour, this theory relies on findings from science, technology and society to explain how human actors engage with other players in the above-mentioned networked system (Syed, 2018). Based on this network of relationships, the ANT has been adopted in different social settings to explain human influences, behaviors, and actions (Ali Abbasi et al., 2022). Stated differently, every actor-network that is in place comprises of players who are known as “actors.” These players are not necessarily people because they may include things or intangible assets. However, the existence of these networked elements in a common system eventually creates action. Despite the presence of both human and non-human actors in the overall model of engagement, both sets of actors are treated equally.

The ANT model is relevant to the present investigation because the consumption of modern-day entertainment content is a part of a network system. Multiple players participate in the generation, dissemination, and marketing of associated content. Each player in this system has the ability to influence the overall health and outcomes of this model (Caredda, 2022). Therefore, the effect of one actor is analyzed within the boundaries of the impact it would have on other players. This relationship is useful in understanding the impact of Netflix’s algorithm-based marketing model on consumer behavior (Mamatha and Geetanjali, 2020). Similarly, it is relevant in comprehending the effects of consumer behavior on a company’s digital marketing strategy.

The insights highlighted above reveal that the actor-network theory provides a framework for judging the actions and activities of companies or consumers within a networked ecosystem. In this model, this ecosystem is designed to fulfill an intended objective of balancing the interests of multiple players in the decision ecosystem (Bu et al., 2021). The stability of the overall network depends on the ability of this system to balance the interests of different stakeholder groups (Sun, Habib and Huang, 2021). Stated differently, their interests should be aligned with the vision or purpose of forming the networked system in the first place (Demirtas, 2020). This structured framework is relevant to the current investigation because Netflix’s digital marketing strategy is examined within a networked system designed to maximize user preferences, as the core objective.

This networked web of interests forms the basis for the inclusion of the actor network theory in this analysis. Its implication on the current investigation is the examination of Netflix’s marketing strategy from two perspective’s – within and outside of the company. The latter analysis represents the networked model discussed above because it examines the company’s interests as it relates with those of others that co-exist with it in the networked system discussed above.

Critical Consciousness Theory

As its name suggests, the critical consciousness theory focuses on explaining people’s cognitive understanding as a predictor of their behaviors. Developed by Brazilian educator, Paulo Freire, the critical consciousness theory uses common logic to evaluate consumer behavior. In the process, it can review marketing materials from companies that sell products or services influencing it (Latif et al., 2019). The justification for using the critical consciousness theory in the present study is fixated on its ability to explain the effects of Netflix’s marketing strategies on consumers (MacKay, Chia and Nair, 2021). Particularly, its role is in helping the researcher to understand whether consumers understand the impact of Netflix’s algorithm-based marketing strategy, or not is pertinent to its adoption.

The concept of “critical awareness” features prominently in the operationalization of the critical consciousness theory in marketing literature. This concept was founded on the assumption that people could overcome inherent biases in their decision-making systems by simply understanding the nature of processes that influence their purchasing decisions (Luckner, 2021). Proponents of the critical consciousness model notably used this argument to protect low-income people from predatory marketing campaigns that influence their buying or purchasing plans (Hamilton et al., 2021). Therefore, by understanding the workings of this system, they could become empowered to make incremental changes to their lives that would improve their wellbeing.

To achieve the above-mentioned objectives, the concepts of reflection and action are key tenets of the critical consciousness theory. Indeed, it is assumed that the two impressions could alter structures and conditions that affect consumer behavior (Kim et al., 2018). Reflecting on past practices helps consumers to think critically about the information they consume as marketing messages. This process allows them to detect nuances and hidden marketing messages that influence their decisions (Latif et al., 2019). At the same time, they are likely to identify the main objective of the company and relate it with marketing messages conveyed on marketing media platforms. This process could initiate action, which could align, or clash with the original intention of the marketing message. The outcome is useful in understanding Netflix’s complex algorithm-based marketing model.

The concept of “critical awareness” is achieved in the critical consciousness theory when unique conditions are met within the consumer-marketing matrix of engagement. These circumstances are fulfilled in a multi-stage process characterized by three phases of cognition, which include crucial contemplation, political efficacy, and necessary action (Saleem, Shenbei and Hanif, 2020). In this tripartite framework of engagement, consumers can draw a link between their everyday experiences with the broader social challenges affecting their communities (Saleem, Shenbei and Hanif, 2020). This realization provides a basis for changing consumer behaviors as they experience different stages of “critical awareness” formation.

The concept of critical reflection, which is one of three states of the critical consciousness theory, will be used as the foundation for the present investigation. It is fixated on the need to promote social justice as the foundational aspect of marketing (Santander Trade, 2021). Therefore, it promotes that idea that people’s everyday experiences cannot be separated from broader social challenges affecting their communities (Leggett, 2020). This critical awareness model allows researchers to focus on larger systems that create injustices to consumers, as opposed to its actions or effects on victims (Santander Trade, 2021). The critical awareness approach to investigating marketing messages described in this analysis is similar to the ANT model mentioned above because they both focus on explaining the nature of interaction between consumers and companies within a larger system or framework that has other players as well. Therefore, there is a synchrony of approach, which makes both theories relevant to the current probe (Meng, 2021). In the context of the present study, the ability to understand Netflix’s algorithm-based marketing model is the basis for acquiring critical consciousness.

The Recommendation Advertisement System as a Choice Architect Tool

The growth and spread of technology in the entertainment field has brought a selection of products that consumers can choose to buy. This development has created a surge in information flow to consumers, which has inhibited their ability to make informed decisions on the best type of product to buy or consume (Santander Trade, 2021). Consequently, a “media bubble” and “group think” ideology has emerged in the online digital streaming service (Eze, Chinedu-Eze and Awa, 2021). The recommendation advertisement system has been proposed as a solution to this problem because it attempts to match consumer needs and preferences with product types and categories (Meng, 2021). This contribution has created the perception that the recommendation model is an instrument of choice architecture. Stated differently, it can be used to manipulate consumer decisions without the buyer’s knowledge or understanding (Kornberger and Mantere, 2020). In this regard, it attracts specific risks that could be injurious to the role of the consumer as the choice architect. At the same time, as mentioned above, the recommendation model adopted by digital media companies helps customers to analyze multiple volumes of data to help them choose, that which suits their needs. To this end, there is a need to understand the merits and demerits of the recommendation advertisement model.

Advantages of the Recommendation System

The recommendation advertisement system is designed to favor certain products and not others because of its alignment with a customer’s core beliefs and preferences. Companies use this recommendation system to improve their marketing effectiveness because it helps them to identify and develop messages that are targeted for a key audience (Eze, Chinedu-Eze and Awa, 2021). From a consumer standpoint, the recommendation system is beneficial because it promotes autonomy. For example, it encourages the personalization of products to improve the overall user experience (Łasak, and van der Linden, 2019). Consumers also prefer to use this system because it minimizes their search costs and time (Eze, Chinedu-Eze and Awa, 2021). At the same time, it eliminates the uncertainty associated with online searches.

Disadvantages of the Recommendation System

Despite the advantages of the recommendation system described above, it can backfire if it fails to heed to the needs of consumers. For example, dissatisfaction could be registered if it undercuts consumer autonomy (Łasak, and van der Linden, 2019). This outcome could undermine the overall goal of introducing the system in the first place because the loss of consumer autonomy is detrimental to the credibility of the purchasing process. In some cases, clogging such systems may build resentment from users who may feel that they are being denied their right to make decisions (Eze, Chinedu-Eze and Awa, 2021). This challenge has not been solved because algorithms do not account for every aspect of a human’s cognitive processes when predicting buying decisions (Gonzalez-Vicente, 2021). For example, it may not know one’s aspirations, thereby making it difficult to predict whether the system would appeal to them, or not.

Effects of Digital Marketing on Consumer Culture

Culture refers to the set of beliefs, norms, values and attitudes that shape people’s behaviors and actions. In business circles, culture affects multiple aspects of corporate performance, including product development, strategy orientation, policy development, and recruitment strategy analysis, just to mention a few (Antonucci and Varriale, 2020). The introduction of digital marketing techniques in business has created cultural shifts in various areas of product development and marketing as well (D’Erman, 2021). Particularly, the advent of social media and the conversion of traditional marketing strategies into virtual ones have transformed the relationship between businesses and their customers (Bolat and Korkmaz, 2021). Scholarly research materials, which have investigated this subject, have focused on understanding the effects of social media marketing strategies on consumer behavior with little emphasis on technology-based companies that can use their internal marketing infrastructure to generate adequate data about their customers (Heindl, 2021; Marsden and Henig, 2019). The relatively few numbers of internet-based companies that can generate consumer data via their domestic infrastructure could explain the skewed findings.

The ongoing coronavirus pandemic has formed the basis for which scholars have recently explored the impact of culture on consumer behavior. Particularly, there has been a growing body of literature indicating the potential cultural shift that the pandemic has created on consumer behavior (Rendeci, 2022). The reasons given for this transformation have been linked with digitization and the role that technology plays in sustaining business operations during the pandemic. The need for mediated communication methods emerged during the pandemic as well (Jiao, Xu and Zhao, 2020). They influence processes associated with information dissemination between companies and their customers (Spijkman and de Jong, 2020). Consequently, brand narratives have changed with more consumers preferring to see “do it yourself advertisements” that empower them with knowledge to create products or services at home (Barrowman, 2019). This change in consumer behavior is a direct product of the lifestyle changes brought by the COVID-19 pandemic on consumer behavior.

The reliance on digital communications during the pandemic period has heralded a new phase of scholarly research, which affirms itself in the entrenchment of technology tools to sustain modern life. Business operations are integrated in this analysis because core activities have been influenced by digitization and automation (Bhandari and Bansal, 2019). The success of online streaming services, such as Amazon and Netflix, have equally thrived on the cultural shift brought by digitization on the entertainment industry (Antonucci and Varriale, 2020). The pandemic further elevated the importance of digital communications in advertisement (Xie et al., 2021b). This development has cemented the effects of digital marketing on consumer culture.

Impact of Recommendation Styles on Consumer Behavior

The recommendations model of marketing engagement works by proposing a predetermined list of products or services to customers for purchase or consumption. It thrives on the assumption that consumers are busy or lazy and prefer to buy products that other people have tried or tested, as opposed to novel ones (Rowley and Oh, 2019). Nonetheless, the use of a recommendations model to expand product outreach has been marred by criticism from scholars who believe it limits consumer options during purchasing (Lopez et al., 2020). Furthermore, a growing number of pundits argue that the algorithms used to recommend products and services on digital marketing platforms are unclear and cannot be used as an accurate predictor of consumer tastes and preferences (García-Canal et al., 2018). Research studies, which have focused on understanding the impact of digital communications on consumer behavior, have shown that changes in attention span have emerged among younger consumers due to extensive social media use (Gehlen, Marx and Reckendrees, 2020). Furthermore, it has been proven that consumers today desire shorter and more relevant advertisements on their feeds (Shapiro, 2020). Therefore, the nature and type of content that can be communicated to them has changed.

Data also shows that consumers desire organic content as opposed to paid advertisements to motivate them to buy goods and services. Therefore, companies that develop marketing campaigns with a social appeal or that can generate organic interest get the best outcomes out of digital marketing platforms (Liam, Kim and Choi, 2021). Research studies also suggest that consumers have become fussier when selecting goods both online and offline (Jena, 2020). However, the best results in digital marketing have been reported among companies that maintain consistency between their online and offline purchasing experiences (Sparre, 2020). Overall, these findings show that marketing campaigns can be recalibrated to suit different recommendation styles appealing to different groups of buyers. Based on the changing dynamics of the online consumer market highlighted above, the marketing development process ought to align with current consumer preferences.

Relationship between Digital Marketing and Consumer Autonomy

The use of technology in business and marketing has changed the nature of interaction between businesses and their consumers. Traditionally, people had the liberty to walk into a shop, choose an item of their liking, and pay for it on the spot. The variety of products or services to choose and pay for was open for everyone to see. However, the growth of the digital space has changed how businesses interact with their customers because the latter cannot physically inspect the goods available for sale within a specific product category (Tran and Smith, 2021). Instead, the customer relies on what a business owner posts online as the basis for making their purchasing decisions. This plan has attracted criticism for its potential to limit consumer autonomy in decision-making (Wang et al., 2020). Stated differently, it has the potential to take away a persons’ ability to make purchasing decisions based on what they can see or hear online. Instead, it transfers the same power to a business owner who chooses what to display to the customer.

Broadly, the concept of consumer autonomy has been mentioned fewer times in current academic literature compared to other concepts, such as satisfaction and engagement. This trajectory in research discourse could be caused by the complex blend of factors accounting for one’s purchasing decisions (Xie et al., 2021a). Therefore, examining the concept of consumer autonomy in decision-making appears to stretch the limits of understanding regarding a person’s cognitive processes.

Summary

This literature review has investigated the state of scholarly literature on factors that influence consumer-purchasing decisions. The research has mostly focused on the recommendations-based model of advertisements that relies on algorithms to suggest products and services for purchase. The concepts of consumer autonomy, consumer culture, and stylistic differences in recommendation emerged as core tenets of the review. The ability of the recommendations model to act as a choice-making facilitator in marketing research has equally been investigated in this review. The evidence suggests that the recommendations model has its advantages and disadvantages. Thus, it can have a mixed impact on consumer purchasing behaviors. Consequently, it is important to undertake a context-specific understanding of the research topic. The focus of the present investigation is on the entertainment business using Netflix as a case study. It will help to bridge the information gap highlighted above.

Methodology

This chapter of the study highlights techniques adopted by the researcher in answering the study questions. Key sections of this chapter discuss the research design, data collection techniques used in the study, processes adopted by the researcher in defining the target population, and a review of ethical implications of including human subjects in the probe. These areas of the investigation highlight core tenets of the overall research methodology of the study.

Research Design

The researcher employed the qualitative descriptive research design in the present investigation. This technique focuses on gathering subjective data relating to consumer purchasing decisions and marketing data. The justification for employing this research design stems from its ability to provide researchers with a range of data to use in their investigation (Melnikovas, 2018). Given the unstandardized nature of qualitative information, this research design was consistent with the exploratory nature of the present investigation. Similarly, the research technique was appropriate in obtaining unbiased data because it enabled the researcher to interact with the respondents in their natural environment.

Data Collection

The researcher collected data from one primary source –focus group interviews. This type of data was collected in Copenhagen and among a respondent group that was majorly European. Respondents who took part in the focus group investigation were pre-screened to test their understanding of Netflix’s recommendation system. Emphasis was made to assess their understanding of the company’s algorithm-based model and marketing systems. Those who demonstrated sufficient level of understanding of these marketing instruments were included in the discussion. Those who failed to demonstrate the same level of competence were excluded from the review.

Target Population

The researcher collected data from Copenhagen Business School (CBS) students. This population comprises of people who are between the ages of 19-29. The distribution of respondents across this age range informed the decision to select college students as the target population because the same age group comprises the highest users of Netflix (van Wingerde and van Ginkel, 2021). Additionally, the researcher selected students of CBS because they shared an intra-college communication network that enabled the investigator to interact with the informants safely and freely. The above-mentioned target age group was equally selected for the present study because Netflix subscribers are mostly young people whose views could arguably be used as a premise for developing the company’s future products and services (Pande and Kumar, 2020). Therefore, the characteristics of the target population were consistent with the objectives of the study.

To get context-specific data from the student population, the researcher recruited students who have a business background. To this end, members of CBS Management of Innovative Business Operation program were given the first priority in the study. The recruitment process was initiated from a class Facebook program. Students who developed initial interest in the study sent links to potential participants who then responded by signing up for the study. Furthermore, students who were studying in an art-related major were similarly given preferential treatment in the study. Their participation was based on the assumption that they would engage in more creative conversations about art and entertainment, which are the core of Netflix’s services, better than others student groups (Leroy, 2022). Collectively, the informants were categorized into two focus groups, which answered unique questions relating to the research process.

Data Collection Instrument

The focus group discussions highlighted above were guided by a collection of questions stipulated in the interview protocol (see appendix 1). This set of questions was developed after undertaking a pre-study analysis to improve the quality of queries posed to the informants. The desired goal was to get reliable and valid data from the respondents. To this end, a pilot study was undertaken in two face-to-face surveys where the researcher posed a series of questions to a select group of student volunteer informants to find out their reaction (Raju and Prabhu, 2019). Emphasis was given to changing the wording and sequences of the questions posed to the respondents to find the right iteration of questions that would cover the spectrum of issues in the study. The overall responses of the volunteer group to these areas of probe provided the foundation for developing the final list of questions posed to the respondents. Overall, the statements posed in the questionnaire sought to find out the respondents’ views regarding their use of Netflix, based on four impact areas of recommendation systems identified in the literature review section of this study. The items posed in the questionnaire contained a blend of both open and closed-ended questions.

Focus Group Interviews

As highlighted in this chapter, focus group discussions played a significant role in generating data for this study. Two focus groups were formed for purposes of data collection. Each one of them had five members to make a sum total of 10 participants. This number is deemed an ideal one for leading quality discussions on marketing research (Melnikovas, 2018). It equally allows participants to share ideas freely as each one of them gets an opportunity to be heard. Broadly, focus group interviews were initiated in the study because of the need to get in-depth data on user’s views of Netflix’s recommendations system. Given that the study had a cultural inclination, the focus group discussion helped to unmask the challenges and opportunities for change within the social context (Krys et al., 2022). Overall, the focus group discussions were justifiably used in the investigation because they helped to improve the quality of debate on the research topic. This achievement could not be realized using a single interview for data generation.

Ethical Considerations

Studies that involve human subjects are subject to ethical review. To this end, researchers have a duty to protect the rights of their respondents as they engage in their investigations (Ahmad, 2020). This provision in ethics was applicable to the present investigation because human subjects were used in focus group discussions to come up with the findings. The ethical considerations applicable in this study focused on four key areas that included informed consent, confidentiality, penalties for withdrawal, and the researcher’s role in the study.

Informed Consent

The consent of a research participant to take part in a study is paramount in undertaking credible academic investigations. Particularly, it is important that people who are willing to take part in research investigations do so out of their free will (Khan et al., 2020). To safeguard these interests, all participants who took part in the investigation were deemed to be of legal age – above 18 years. Consistent with the goal of ensuring informants gave information freely; participants who engaged in the focus group discussions did so voluntarily. Stated differently, the researcher did not coerce or pay them to take part in the study. As an additional request, the researcher obtained permission to have the discussions taped for future reference.

Privacy and Confidentiality

It is important to protect the privacy of respondents who take part in academic research studies to protect them from the ramifications of giving their opinions on matters affecting other people or companies. Therefore, the confidentiality of data is often a common tool used to protect informants from such risks (Allibang, 2020). Consistent with this goal, information presented in this study is anonymous to protect the identity of the respondents. The researcher gave this surety to the respondents before they decided to take part in the study. Therefore, their names, genders, student identification numbers and other pieces of personal information were omitted from the study. Stated differently, personal information was selectively removed from the final publication and the remaining data stored in a computer that was secured by a password privy to the researcher.

Penalties for Withdrawal

It was important to include penalties for withdrawal in the ethical assessment of the study because of the risk that potential participants could withdraw midway from the investigation. To mitigate this risk, the researcher assured respondents that they were free to withdraw from the investigation at any point of the study. This freedom was granted because the risk of withdrawal could not significantly affect the quality of data that was generated in the focus group discussions (Law, 2021). However, the effects of withdrawal could have been acute if the study relied on one-on-one interviews with potential informants. Despite the freedom to withdraw from the study, all respondents took part in the discussions.

Researcher’s Role in the Investigation

As part of the duties expected of researchers, they need to perform a series of tasks that would improve the quality of discussions in focus groups engagements as well as guarantee the comfort of informants who have volunteered to give their views on the research topic. To this end, the researcher played three key roles in the research process. The first one was informing the respondents about the nature of the investigation and the expectations of participation. In this part of the analysis, information relating to the length, location, and time of the discussions were discussed. The second duty performed by the researcher related to explaining the potential risks of taking part in the study. The main one was the possible risk of leaking of personal information but the researcher guaranteed them that their views would be presented anonymously. The researcher also made the informants aware of the intentions of the researcher in undertaking the present study, which was to fulfill academic obligations. To this end, the informants were notified that the findings of the study would be used for academic and not commercial purposes. These guarantees helped the respondents to give candid views on the research topic.

Summary

The findings of this chapter show that the techniques adopted by the researcher to undertake the present investigation were informed by the purpose and objectives of the investigation. Data were gathered from focus group interviews which presented a joint discussion about the effects of Netflix’s algorithm-based marketing plan on its users. The researcher later coded this information and summarized it into unique themes that helped to meet the aim of the study.

Findings

As highlighted in chapter 3 above, the present study sought to find out the views of Netflix users about the company’s recommendation system. As part of efforts to understand the efficacy of the company’s marketing strategy, data was gathered using the focus group technique where discussions occurred in two groups. Both of them were tasked to explore four issues relating to the research topic. They included investigating the role of the recommendations model as a choice-making facilitator, understanding the role of the same tool as an influencer of cultural tastes, promoter of customer autonomy, and impact of different methods of recommendation. These four core areas of analysis were highlighted as gaps in in the literature review section of the stud and they were filled by the present investigation.

Focus Group Findings

Two focus group discussions comprising of 10 members discussed the four issues highlighted above. To recap, the debate was on examining the role of Netflix’s algorithm-based recommendations model on consumer behavior and cultural decisions. Top operationalize this discussion, the debate was centered around four core areas of problem-solving, which investigated the company’s marketing system as a choice-making facilitator, influencer of cultural tastes, moderator of cultural autonomy, and a facilitator of variable recommended actions.

Choice-Making Facilitator

The role of Netflix’s algorithm as a choice making facilitator was included in the focus group discussion because algorithm-based systems are designed to influence consumer choices (Kalé, Harland and Moores, 2020). One of the respondents said that the algorithm-based model is impactful to users because it operates in a language that all of them can understand. He added that most computer-based algorithms are encrypted in complicated computer languages, but Netflix’s algorithm is not dependent on any programming technique and can thus be understood by everyone. In this regard, the role of recommended marketing was hailed as a tool for facilitating decision-making systems in organizations.

Influence of Algorithms on Cultural Tastes

The influence of Netflix’s algorithm-based marketing model on consumer culture was an inherent part of the underlying discussions of this study. This area of the probe stemmed from research studies, which affirmed the influence of digital marketing techniques on consumer culture (Corporate Finance Institute, 2022). All the informants who took part in the study agreed that the recommendations model helps to nurture a culture of conformance and approval to specific cultural ideals. To support this statement, one respondent argued as follows:

I believe the company’s recommendations-based marketing model works in the same way as movies, or by extension, Hollywood, has spread western ideals around the world. By watching specific types of movies, or listening to songs that sound a specific way, has influenced how people talk, dress, and even live. I do not think Netflix is any different. The recommendations it gives to its customers are likely to influence their tastes and preferences in films or songs…whichever the case may be.

In response to this statement, one of the respondents said there is a risk associated with relying on Netflix’s recommendation system as the only basis for making purchasing decisions. He believed that the model enables users to focus on one area of art or media, thereby limiting their breadth of options for making purchasing decisions. The case was likened to researcher bias in data collection especially when scholars seek information that suits their preferred outcomes (Kohnert, 2018). Therefore, the informants agreed that there is a subconscious influence that recommendations have on people, which affects consumer-purchasing decisions. Ultimately, it limits their ability to consume alternative content.

Influence of Algorithms on Customer Autonomy

The role of customer autonomy in influencing consumer satisfaction levels is rarely contested in academic literature. The general assumption adopted by most researchers about this topic promotes the view that autonomous customers are more satisfied than those who are dependent on others to make their decisions (Pai, 2021). Given that Netflix’s algorithm-based model attempts to make decisions on behalf of users, it was pertinent to explore this area of focus in detail. From the onset of discussions that delved into this field of analysis, it was evident that the respondents held mixed views regarding the impact of the marketing system on consumer behavior. For example, three respondents held the view that the recommendation system adopted by the company made it difficult for users to examine products by themselves. They argued that, by the time one completes reviewing multiple choices recommended on the platform, they would possibly be tired to look at any other choices that exist out of the pool of products proposed. Therefore, they viewed the recommendations model as a limiting tool of consumer choice analysis because it narrowed the scope of potential products to view and buy.

Two respondents held a contrary opinion to the above statement because they argued that the current Netflix streaming platform has too many options that cannot be effectively reviewed without offering assistance to customers. They believed that the recommendations based model was useful in reviewing possible products of interest. Relative to this statement, one of the informants said,

Think of it this way, if you entered a shampoo store and were given two choices to make a purchase. One involves reviewing 300 possible products and the other is about reviewing five of them that have been recommended based on your unique personality…which one would you go for?

The question was posed to one of the respondents who answered that the second option would be preferable. He defended his position by arguing that he only made this choice because it would take too much time to review all the 300 shampoo products. Another respondent remarked that the argument should then be domiciled on the methodology that was used to identify the five recommended products. Four out of five of the respondents agreed with her statement by nodding affirmatively.

Impact of Different Methods of Recommendation

Discussions about the impact that different recommendation methods would have on consumer behavior were included in the focus group discussions because of varying levels of cognitive understanding among consumers (Wang et al., 2021). Four of the respondents in the first focus group discussion stated that the intensity level of proposed recommendations would have an impact on their purchasing decisions. She said that subtle marketing messages would be more appropriate for her taste compared to overt campaigns. One of the respondents agreed with this statement and said,

I agree with you…Uhhmm..for example, I am likely to pay more attention to a recommendation from a friend than a company or an advertisement that pops up in my computer or mobile phone screen. So, I would say that the medium of communication matters.

The respondents also explained that the company’s algorithm-based marketing system keeps changing and is often confusing to users. One of the informants expressed his frustration with the kind of recommendations proposed to him because every time she logged into her account, she would find the same films recommended. Therefore, she proposed that a different recommendation method be adopted at Netflix. Alternatively, if that was not possible, she argued that different recommendation-based systems be adopted to increase the diversity of products that are recommended to users.

Broadly, the respondents believed that the method of recommendation adopted by Netflix has an impact on their level of satisfaction with the company’s products.

Summary

Based on the findings highlighted in this chapter, there were few differences between the views expressed by the first and second focus groups. Stated differently, there was a similarity in opinion regarding the effects of Netflix’s recommendation-based marketing model on consumer purchasing decisions across the two groups of analysis. This similarity in opinion can be linked to the fact that the respondents attended the same business program in college. Overall, the informants demonstrated that Netflix’s marketing model influenced consumer’s cultural tastes and moderated their autonomy with mixed effects on user satisfaction. The recommendation style investigated also emerged as having a moderating effect on user tastes. Nonetheless, the most compelling argument made by the respondents was premised on the view that the algorithm-based model was a choice architect for Netflix users on the online streaming platform. The sheer volume of products and information available online enhances its impact. Therefore, the algorithm-based model functions as a tool for finding the right products to buy or consume.

Discussion and Analysis

As highlighted in this study, the focus of the investigation is on understanding the effects of Netflix’s algorithm-based marketing strategy on its customers. To this end, it is important to understand details of the company’s marketing model within the theoretical frameworks described in chapter two of this document. To meet this research goal, Netflix’s marketing strategy is examined to establish unique features that have improved the effectiveness of its marketing strategy. The choice making facilitator is one of the company’s key features of its marketing plan.

Influence of Algorithm on Consumer Choices

The impact of Netflix’s algorithm system on consumer choices was a core theme that emerged from the present investigation. Consumer choices were analyzed from a cultural and behavioral standpoint. Coupled with the economics of maintaining supportive consumer behavior on the online streaming services, it was important to understand how Netflix’s marketing mix helped balance different interests in the purchasing ecosystem to influence decisions (Hela, 2021). This theme also emerged as a core subject in the focus group discussions because the respondents discussed the effects of the algorithm model on user choices. They agreed that skewed messaging affected purchasing behaviors and had the potential to shift cultures towards specific interests or products.

As its name suggests, the choice making facilitator is designed to influence purchasing decisions by manipulating consumer decision-making processes through persuasive marketing. Typically, this influence is achieved when a recommendation-based system is adopted into a company’s marketing structure to help consumers make sense of multiple choices they have to make before purchasing a product (Jiang and Kim, 2020). Netflix’s marketing strategy is modeled on the same system. Its algorithm-based model is designed to influence consumer decisions by helping buyers choose what to purchase from a seemingly endless list of options.

The ANT framework described in the literature review section of this study could be used to explain the intersection between algorithms and consumer choices. It suggests that such effects occur within a broader interactive system of multiple actors influencing consumer choices (Liang and Wang, 2020). In the context of the Netflix case study, these multiple factors are mostly contingent on external forces, such as the economics of price control. Internal elements, such as product development strategies, equally affect the influence of marketing plans on consumers. These influences operate within the broader entertainment ecosystem that not only accounts for current factors affecting consumer actions but unseen considerations that may affect future behaviors as well.

To support the ANT framework analogy highlighted above, which represents a broad-based system of factors influencing consumer decisions, parallels can be drawn between the findings mentioned by the respondents and corporate best practices. Indeed, the recommendations made by algorithms work in the same manner as an institution would do by endorsing one of its workers to work in another organization or in a different capacity from that which they serve (Godelier, 2020). The goal is to promote habits and inclinations towards specific products or services that would make a company profitable or successful (Yuan, Leng and Wang, 2022). The choices influenced by this system influence the priorities consumers make in their purchasing decisions as well as the content of marketing messages developed at Netflix.

Impact of Algorithms on Consumer Culture

The impact of Netflix’s recommendation system on the cultural tastes of its consumers formed a significant part of the present investigations. The aim was to help in ascertaining the extent that this system influences consumer cultural preferences. Preliminary research on this topic suggests that intermediaries of cultural information influenced perceptions of cultural commodities (Beugelsdijk and Welzel, 2018). The effect of these information intermediaries on consumer purchasing behavior was ascertained when the system directed users to pay attention to specific cultural items. This model influences cultural systems, which later affect the tastes and preferences of users.

The impact of the above-mentioned systems on consumer actions demonstrate that most people live in “black boxes” or cocoons, which impede their ability to understand factors that influence their decisions. This phenomenon was captured by the critical consciousness theory, which seeks to explore factors that subtly affect consumer-purchasing decisions (Pajkovic, 2022). The same theory is keen on raising awareness among consumers about factors that influence their overall decision-making processes. Therefore, the idea that consumers live in information “black boxes” and cocoons are closely linked with this phenomenon. Netflix’s algorithm-based model is one of the techniques that demystify this phenomenon because it explains how consumers are made aware of their biases when making purchasing decisions, albeit with complex knowledge (Liu, 2021). The recommendations model adopted by Netflix differs from the traditional human-based interactions between company sales representatives and consumers. Instead, it influences consumer’s decisions covertly by defining the scope of their online marketing world (Patey, 2021). Therefore, most people find it convenient to use these systems as the basis for making their purchasing decisions.

Netflix’s algorithm-based marketing model acts as an information reservoir for cultural issues that affect consumer behavior and purchasing patterns. The system collects data every time a user logs in to their system and tracks their actions and behaviors in the manner a human being would do. By trailing online behavior, the system is able to recommend products and services that align with the observed actions (Lamb, Hsu and Lemanski, 2020). Contention often arises when people question how such systems pair online behavior and product purchase choices. This is the complexity of algorithm-based systems because they are configured to pair specific behavioral actions with specific product characteristics (Lamb, Hsu and Lemanski, 2020). Broadly, these algorithms function as cultural information intermediaries that facilitate data pairing to predict consumer behavior.

In a world where information is increasingly abundant, it is easy to understand the importance of the algorithm-based advertisement model in marketing. It helps consumers to comprehend multiple pieces of information and find those that are relevant to their unique needs (Lamb, Hsu and Lemanski, 2020). These systems can be loosely termed as “information intermediary hubs” where information coming from different quarters of the organization are synchronized and deciphered. Again, this process highlights the interconnectivity of relationships that support Netflix’s marketing ecosystem. This framework has been explored in this study using the ANT model. It plays a significant role in shaping consumer experiences after buying products or services (Wayne, 2022). Today, most consumers rely on these systems to identify products for purchasing. Broadly, Netflix enjoys the unique position of being a cultural intermediary and a conduit for gathering information for digital content creators. Indeed, it is able to analyze data that come from existing systems to know which type of content to make in future. The data allows them to observe patterns of preferences or tastes that would garner more interest from the public and achieve market success (Wayne and Castro, 2021). The impact that this recommendation system has on consumer purchasing behavior shows its power of influence in society.

The downside to using this algorithm-based system is the similarity in product or service development across different companies. Concisely, the overreliance on this system means that companies are relying on the same type of system and knowledge to develop content that appeal to customers (Shakeel, Yaokuang and Gohar, 2020). Given that they use the same information for product analysis and development, they are likely to come up with products that look alike, hence losing the uniqueness that customers crave for when making their purchasing decisions. Therefore, despite the importance of relying on algorithm-based systems to promote marketing effectiveness, algorithms have the potential of damaging the cultural uniqueness of product development that is crucial characteristic in the entertainment business.

Summary

This section of the study explores the findings highlighted in chapter 4 above in detail using the theoretical frameworks highlighted in chapter 2 of the dissertation as well as extant literature on the subject. As mentioned in the literature review section of this study, the ANT framework was applicable to the current probe. It helped to contextualize consumer behavior within a broader system characterized by different actors. The critical consciousness theory was equally mentioned as a supplementary tool of theoretical analysis. It helped highlight information gaps that influence consumer-purchasing decisions.

Conclusion and Recommendations

Conclusion

This dissertation has analyzed the effects of digital marketing techniques on consumer behavior using Netflix as a case study. The recommendation model of advertisement has been the focus of the investigation with unclear views initially presented about its overall effects on consumer behavior and actions. These unclear views formed the basis for the development of the research questions, which guided the study. To recap, the investigation was designed to understand the views of Netflix users regarding its algorithm-based recommendations system and its influence on consumer behavior. A critical part of the investigation sought to understand the extent that Netflix’s algorithm of recommendation influenced consumer cultural preferences on the online streaming platform. Similarly, this investigation sought to find out the extent that the firm’s recommendation system aroused consumers’ curiosity about the streaming service’s recommendation system.

Focus group discussions helped to generate data on the above-mentioned research areas of study. They were supported by secondary research data obtained from published materials. Nonetheless, the views of Netflix users who shared their experiences on the online streaming platforms formed the source of primary data. The findings of the investigation revealed no significant differences between the views presented by the informants and those expressed by other researchers regarding the impact of digital marketing techniques on consumer behavior. Particularly, there was consistency in thought among the respondent group with those of other scholars who expressed their confidence of the effects of algorithms on consumer behavior and cultural preferences. At the same time, the recommendation model played a significant role in moderating the effects of marketing communications on target audiences. Its effects on consumer behavior are unlikely to disappear in future because of the multiplicity of knowledge and content on various digital spaces. Therefore, the recommendation-based marketing model is likely to remain a key feature for Netflix and other online streaming platforms.

Overall, the actor network and critical awareness theories were used to analyze the effects of digital marketing methods on consumer behavior. Their application to the Netflix case study revealed that consumer actions and management decisions are incubated within a broader value ecosystem that contains the interests of different stakeholder groups. Insights about the quality of relationships among these stakeholder groups have been partly represented in this study. Although equal in importance, some of them play a moderating role while others assume a dominant one in keeping the value chain system functional. Overall, it is important to examine the effects of Netflix’s algorithm-based marketing strategy within the broader ANT framework highlighted in this document. Similarly, by using the critical consciousness theory, it is equally important to appreciate the potential of manipulating marketing campaigns to influence consumer behaviors and cultural preferences.

Recommendations

This study was developed by sampling the views of 10 respondents about the effects of Netflix’s algorithm-based marketing model on consumer behaviors. Future researchers could increase the number of respondents to find out whether the same findings will be achieved. This recommendation is premised on the assumption that a small number of respondents are less representative of a consumer target group compared to a larger one. Due to resource limitations, it was difficult for the researcher to reach a larger and more representative group of informants beyond those of CBS. Consequently, future researchers can investigate the same research phenomenon using non-students or a more diverse sample of people that represent Netflix’s customers.

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Appendix 1 – Interview Protocol

How do you perceive the algorithm-based marketing model as a choice-making facilitator?

How does it affect consumer choices?
What about their cultural appeal?

How do you think Netflix’s recommendation model influences their consumers’ cultural tastes?

How do you perceive its role as a cultural information hub?
To what extent has it created a cultural shift?

To what extent does Netflix’s marketing strategy promote customer autonomy?

How did customers operate before the introduction of the digital marketing strategy?
What changes have been observed today?
How about the COVID-19 pandemic? Any effects?

How do different methods of recommendation affect consumer behavior?

How does employing varied technique affect consumer choices?
To what degree can companies influence consumer actions?

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