In a world with infinite options, we risk missing the simple pleasures by
constantly searching for something – or someone – better.
Jill Stark, 2019
TABLE OF CONTENTS
INTRODUCTION ................................................................................................ 5
CHAPTER I – DECISION MAKING AND THE ‘PARADOX OF CHOICE’
................................................................................................................................ 7
1.1
The Process of Decision Making and Its Determinants............................ 7
1.2
Official Dogma and ‘Paradox of Choice’: A Reference Theory by Barry
Schwartz ............................................................................................................ 11
1.3
The Cognitive Cost: Empirical Evidence and Anatomy......................... 14
CHAPTER II – THE ‘PARADOX OF CHOICE’ IN THE DIGITAL ERA: A
FOCUS ON STREAMING PLATFORMS...................................................... 21
2.1
The ‘Paradox of Choice’ in the Digital Era: Relevant Examples ........... 21
2.2
Disney Plus and the Six Pillars ............................................................... 25
2.3
Amazon Prime Video and the Collaborative Filtering ........................... 27
2.4
HBO Max as a Human-First Platform .................................................... 28
CHAPTER III – THE NETFLIX CASE STUDY ........................................... 31
3.1
When Too Much Is Not Good Enough: The Netflix Case ..................... 31
3.2
How to Overcome the Cognitive Overload? The Netflix Solutions ....... 36
3.3
Physical Store vs. Online Store .............................................................. 45
CONCLUSIONS................................................................................................. 49
BIBLIOGRAPHY .............................................................................................. 53
ACKNOWLEDGEMENTS ............................................................................... 61
INTRODUCTION
The thesis is organized into three main chapters. After the introduction, Chapter I,
‘Decision Making and the ‘Paradox of Choice’’ provides an overview on how
decision-making works and it explains the concept of the ‘Paradox of Choice’,
elaborated by the American psychologist Barry Schwartz. I open the chapter with
a theoretical explanation on how decision-making processes function with a
reference to the two systems governing our decisions, System 1 and System 2, and
delineating what the main determinants of choice are. Subsequently, I move to the
discussion of the ‘Paradox of Choice’ theory, outlining why too much choice is an
issue to solve in order to render decision-making processes less demanding. A few
experiments and examples are mentioned to sustain the argument.
The issue is further analysed in the different sections of this academic work, to
then find its solutions in the Netflix case study, through the set of techniques the
streaming service adopts against the ‘Paradox of Choice’. In the third part of the
chapter, I offer empirical evidence about the existence of the Cognitive Cost: first,
analysing a few phenomena that prove the limitations of the human mind and,
second, by examining the anatomy of the visual system.
Chapter II, ‘The Paradox of Choice in the Digital Era: a focus on Streaming
Platforms’, explores the different areas of modern life in which it is possible to feel
overwhelmed by too many options, and it focuses particular attention on the
streaming platforms’ landscape. The first part of the chapter presents data that
shows the constant presence of abundant choice, and it introduces the online
environment and the streaming platforms into the picture. Three sections follow,
which specifically analyse the techniques adopted by the main competitors of
Netflix: Disney Plus, Amazon Prime Video and HBO Max. After a quick analysis
of the three, I finally get to the culmination of the research question: the solutions
adopted by Netflix to reduce the cognitive load, outlined in Chapter III, ‘The
Netflix Case Study’. The answers offered by the platform are examined in detail,
with reference to its recommendation system and the A/B tests carried out on the
service. The last section of the chapter makes a comparison between the shopping
5
window of an online store and the one of the physical store, providing evidence on
how the former is more performing.
Finally, in the ‘Conclusions’, I remind readers of the theoretical framework of
reference that constitutes the rationale for my research. In doing so, I underline the
necessity of finding a solution to minimise the ‘Paradox of Choice’ and I highlight
the power of modern technology in effectively reducing the cognitive cost and
presenting a solution for the human mind to make decisions with a minimised
effort. At the end, a more human approach is addressed, as a result of a study that
offers improvements to Netflix's recommendation system, analysing the journey
users follow before choosing what to watch on streaming platforms. Following is
my personal perspective and a suggestion for integrating Netflix's algorithm and
the machine learning system with a more human-centric strategy.
6
CHAPTER I – DECISION MAKING AND THE
‘PARADOX OF CHOICE’
This first chapter of this academic work is intended to introduce the topic of
decision making related to the information overload, determined by the presence
of too many choices in modern human life. The first part is aimed at explaining the
two systems governing the decision-making process and the main factors that
affect our decisions, with a focus on the cognitive load and the options. In the
second part, the focus will be on the theory developed by the psychologist Barry
Schwartz with regards to what he called the ‘Paradox of Choice’. Finally, in the
last part of the chapter, the analysis will be concentrated on the Cognitive Cost,
proving its empirical evidence and with a focus on the anatomy of vision.
1.1 The Process of Decision Making and Its Determinants
Decision-making and its central role in problem solving has always been one of
the main themes of cognitive psychology but over time has also affected other
disciplines, such as social psychology, organizational psychology and the
psychology of communication and consumption (Zito, 2020). Problem solving is
an analytical process used to identify the possible solutions to the situation at hand,
therefore making decisions is a part of the process. Specifically, decision making
involves the process of selecting the most ‘appropriate’ alternative to the situation
within the range of possibilities (Zito, 2020).
As a discipline that investigates the human brain and the nervous system,
neuroscience is essential to understand human decision-making processes, which
is relevant to companies, whose aim is to help consumers make the most satisfying
choice (Moran Cerf, Manuel Garcia-Garcia, 2017). As humans, we believe that
our decisions are ours alone and that we can explain how and why we made each
choice, but this is not necessarily true. Multiple studies in neuroscience and
psychology have shown that, although we arrive at an outcome every time we
7
make a decision, the narrative or explanation of the path that led to that specific
decision is often beyond our rational grasp (Moran Cerf, Manuel GarciaGarcia, 2017).
Indeed, people are not always able to articulate their choices and may tell
themselves a ‘story’ about their decisions which is not, strictly speaking, real. A
number of authors recognised the existence of two systems for learning, reasoning
and social processing (Epstein, 1994; Evans & Over, 1996; Reber, 1993; Sloman,
1996 in Evans, 2010), named System One and System Two, respectively
(Stanovich, 1999 in Evans, 2010). The two systems are instrumental to identify
and explain the processes that give rise to intuitive judgment, on one hand, and to
more deliberate reasoning on the other. The former is governed by System One
while the latter is governed by System Two.
System One has been associated with a form of cognition that is evolutionary
ancient, linked to animal cognition. It comes into play for easy decisions and it is
considered to have, unconsciously, more influence in the choices that we often
assume. It is fast, automatic, context dependent and it involves concrete reasoning.
The benefit of this kind of decision-making process is the speed at which it allows
us to act in circumstances where delay could present great danger. For example,
the reaction we have to potential danger is the product of a purely System Onebased judgment.
System Two is more recent, slow, and controlled (Evans, 2008 in Moran Cerf,
Manuel Garcia-Garcia, 2017). It is context independent, capable of abstract
reasoning and distinctively human. We have the tendency to think that System
Two is responsible for all of our decisions. In reality, we often overstate its
importance in motivating us to do things and the ‘fast thinking’ of System One
often influences our decisions more than we imagine (Moran Cerf, Manuel GarciaGarcia, 2017).
The factors that affect our decisions are multiple and diverse: culture, emotions,
the physical environment, cognitive load and the concrete availability of options.
Although many of the parameters that affect decision-making are universal, culture
8
still plays a central role in choice behaviour. For instance, the average American
prefers to make decisions individually, while people from other countries may
prefer to defer decision-making to a person they trust (Iyengar 2010, in Moran
Cerf, Manuel Garcia-Garcia, 2017).1
Emotion is one of the primary drivers of our decisions.2 Scientifically speaking,
emotions are biochemical algorithms that developed over millions of years of
natural selection to streamline the brain’s cognitive processes (Harari, 2015 in
Moran Cerf, Manuel Garcia-Garcia, 2017). They are powerful tools, as they often
allow us to make instant or rapid decisions and predictions. An iconic example is
fear: if we see, for instance, a lion, we run or freeze and there is no space for any
further decision-making process. In this context, emotions may serve as a nonconscious bias to guide decision to a more advantageous outcome.
The environment is another determinant of choice; in this sense, cognition is not
strictly confined to the mind. Thoughts can be influenced by physical sensations,
a phenomenon known as ‘embodied cognition’ (Moran Cerf, Manuel GarciaGarcia, 2017). A study from Laurence E. Williams and John A. Bargh (2008)
showed the effect of temperature on judgements of personality. Participants in the
study were asked to hold a warm coffee or a cup of iced coffee; the former resulted
1
To support this thesis, Iyengar (2010) compared Anglo-American and Asian-American
children in San Francisco. The first group of children was allowed to pick out their own
materials for a puzzle task. The second group received the same materials but was told
that they were chosen by their mothers. The third group was told that the materials were
chosen by the experimenter. The Anglo-American children performed best on the task
when they were allowed to choose their own materials, while the Asian-American
children did best when they were told that their mothers chose for them. Iyengar explains
that Americans view choice as a way to define and assert “the self,” whereas Asians have
a collectivist culture, which views choice as a way to maintain group harmony (Iyengar
2010, in Moran Cerf, Manuel Garcia-Garcia, 2017).
2
It refers to a relatively brief episode of coordinated brain, autonomic and behavioural
changes that facilitate a response to an external or internal event of significance for the
organism (Davidson, Scherer, and Goldsmith 2002 in Moran Cerf, Manuel GarciaGarcia, 2017).
9
more likely to rate a target person as ‘warm’ compared to the latter. The researchers
revealed that the physical experience of warmth influenced feelings and
perceptions of interpersonal warmth, without the participants being aware.
It was also proven that hunger and fatigue have a huge influence on decisionmaking processes. In Israel, a study on judges gave evidence that they were more
willing to grant parole at the beginning of the day or right after the break compared
to when they were more tired and hungry (Danziger, Levav, and Avnaim-Pesso
2011 in Moran Cerf, Manuel Garcia-Garcia, 2017). Glucose level can also make a
difference in decision-making: an experiment conducted by Matthew T. Gailliot
and colleagues (2007) discovered that when glucose levels of participants were
low, they had less willpower and more prejudice.
The cognitive load refers to the amount of information that a working memory can
hold at one time. Our memory is limited in capacity and time when it comes to
holding or processing new information (Miller, 1956; Peterson and Peterson 1959
in Pavlo Antonenko, Fred Paas, Roland Grabner and Tamara Van Gog, 2010).
Therefore, the higher the number of interacting information elements a task
contains, the more difficult it is and the higher is the intrinsic load it imposes on
working memory (Pavlo Antonenko, Fred Paas, Roland Grabner & Tamara Van
Gog, 2010). When the cognitive load becomes too large, people’s working
memory becomes overloaded. Indeed, considering that when resources are low our
decisions tend to be automatic and impulsive, this phenomenon is such a powerful
tool for marketers as it stimulates System One. A global study conducted by Live
Person revealed that consumers were more likely to ‘impulse buy’ when in a store
environment than online, due to the cognitive load experienced in shop with the
use of music, fragrances, distractions (Shiv & Fedorikhin, 1999).
The last element that influences decision-making processes is the availability of
options. We often think we have made an important choice independent of the
outside influence, but the number or type of available options always impacts on
our final decisions since it drives us towards one particular direction.
10
Cognitive load and availability of options will be discussed in-depth in the
following section.
1.2 Official Dogma and ‘Paradox of Choice’: A Reference
Theory by Barry Schwartz
People believe that the more choice they have, the more freedom they have. And
the more freedom they have, the more welfare they have. This is deeply embedded
in in our lives that no one would question it (Schwartz, 2005). Choice enables us
to control our destines and to come close to obtaining exactly what we want from
any situation. Choice is essential to autonomy, which is fundamental to well-being.
On the other hand, the fact that some choice is good doesn’t necessarily mean that
more choice is better: there is a cost of having an overload of choice (Schwartz,
2005).
Barry Schwartz states that the assumption that a wide array of options is better is
wrong, and he develops the concept of the ‘Paradox of Choice’ (Schwartz, 2005).
According to him, too much choice provides paradoxically a paralysis rather than
a liberation. With many options to choose from, people find it very difficult to
choose at all. The second point he makes is that, even if we manage to overcome
the paralysis and make a choice, we result being less satisfied with the result than
we would be if we had fewer options to choose from. This is due to several reasons.
First, we may regret the decision. Second, we may be less satisfied because it is
easy to imagine the attractive features of the other alternatives rejected, a
phenomenon called opportunity cost. Third, having a wide range of options, our
expectations increase, and this brings less satisfaction, even if the results are good.
Last, most of the times, if the decision is not good enough, we blame ourselves.
The complexity of the phenomenon is well defined by the expression adopted by
the American psychologist who linked the notion of the ‘Paradox of Choice’ to the
concept of the ‘Official Dogma’ (Schwartz, 2005). This expression refers to
Western Industrial societies, where it is believed that to maximise welfare you
need to maximise individual freedom, and therefore choice. However, according
11
to Schwartz’s opinion, the net result is that, having more alternatives, we do better
in general, but we feel worse; this is because, with a hundred different options,
there is no excuse for failure.
A study conducted by Iyengar and Lepper (2000) supports the thesis of Barry
Schwartz. The research is entitled “When Choice Is Demotivating” and it provides
clear evidence of the matter. The experiment was set in a food store: researchers
set up a display featuring a line of high-quality jams, and customers could taste
samples. In one condition of the study, 6 varieties of the jam were available for
tasting. In the other circumstance, 24 varieties were available. In either case, the
entire set of 24 varieties was available for purchase. The large array of jams
attracted more people to the table than the small one. However, in the moment of
deciding whether to buy or not, a huge difference was highlighted. Thirty percent
of the people exposed to the small array of jams bought a jar while only three
percent of the people exposed to the large one did (Iyengar & Lepper 2000).
The explanation of these results is easily findable in Schwartz’s thesis. Indeed, a
large array of options may discourage consumers because it forces an increase in
the effort that goes into making the decision, so consumers decided not to decide
and didn’t buy the product. Also, a large array of options diminishes the
attractiveness of what people choose, the reason being that we think about the
attractions of some of the unchosen options, which detracts from the pleasure
derived from the alternative chosen. Too much choice meant respondents had to
think harder to differentiate between the options – a process we humans try to
avoid, and one that can have hugely detrimental consequences on decision
motivation (Schwartz, 2015).
We are trapped in what Fred Hirsch called the “Tyranny of Small Decisions”
(Schwartz, 2005). In any given domain, we say a resounding ‘yes’ to choice, but
we never grant a vote on the whole package of choices. We say to ourselves “let’s
go to one more store”, “let’s look at one more catalogue” and not “let’s go to all
the stores” or “let’s go to all the catalogues”. It seems easier to add one more item
12
to the assemblage that is already being considered. Nonetheless, by voting ‘yes’ in
these situations, we are, as a matter of fact, voting ‘yes’ on the package, with the
consequence that we feel unable to manage our decisions (Schwartz, 2005).
Probably, if we would have to choose whether to have choice or not, we would
always opt for choice, but it is the cumulative effect that is causing substantial
distress.
In the 1950s, the psychologist George Miller wrote an essay, entitled “The Magical
Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing
Information” (Miller, 1956). The study he undertook, shows how people can
memorize only seven objects per time. Above this number, everything gets
confused and there is a cognitive overload, which is equivalent to what Barry
Schwartz referred to as ‘Paradox of Choice’. Presenting people with too many
options, means to expose them to a huge cognitive cost which translates in a
difficult and suffered decision. Indeed, while on one hand the products to choose
from continuously increased in number, our cognitive capabilities remained
unchanged (Miller, 1956).
Like Barry Schwartz says, the availability of many options creates an escalation of
needs like shown in figure 1.1. Therefore, we have higher expectations whereas on
the other hand the secret to happiness is lower expectations.
Figure 1.1 shows that as the available choices increase, the satisfaction
decreases.
(Schwartz, 2005)
13
Schwartz et al. (2002) proposed that when making choices, there are two types of
individuals: ‘maximisers’ and ‘satisficers’. The former search extensively through
many alternatives with the goal of making the best choice. Too many options are
very bad for ‘maximisers’: they spend hours, days, weeks studying the excess of
choice, they make their selection and then they regret it when something better
seems to be available. The latter search only until they identify an option that meets
their standards, which they then choose. They are prepared to settle for ‘good
enough’. They seek out what does what they want, and they buy it as soon as they
find it. They therefore suffer less regret than maximisers and waste less time.
The following section of the chapter will illustrate the existence of the Cognitive
Cost with a focus on the anatomy of vision.
1.3 The Cognitive Cost: Empirical Evidence and Anatomy
Research conducted in 2002 by Daniel Simon et al. showed that, in some
occasions, the mind can fail. The study analysed this situation: a guy with a
basketball ball in his hands asks a passer-by for an information. A crowd moves
between the two interlocutors and the basketball ball disappears. It might seem
logic that everyone would notice the missing object which was under their eyes
until a few seconds ago. However, it was demonstrated that only 27% of people
clearly noticed the difference. This phenomenon is named ‘Change Blindness’,
defined as the failure to detect when a change is made to a visual stimulus (Simons
and Levin, 1997 in Attwood, 2018). When missed changes are later pointed out to
the observers, they frequently regard with a sense of disbelief at how something
could have gone unnoticed (Attwood, 2018). The surprising nature of ‘Change
Blindness’ results from a mismatch between the belief that our visual perceptions
are so detailed as to be complete, and the actual ability of the visual system to
represent and compare scenes in real time. This phenomenon occurs when the local
visual transient produced by a change is obscured by a larger visual transient, such
as an eye blink, a saccadic eye movement, a screen flicker, a cut in a motion
picture, or when the local visual transient produced by a change coincides with
14
multiple local transients at other locations, which act as distractions, causing the
change to be disregarded (Attwood, 2018).
‘Change Blindness’ is distinct from ‘Inattentional Blindness’, which occurs when
an individual is blind to the presence of an entire object while performing a
distracting task (Simons & Chabris, 1999 in Perera, A. 2021). The study on
‘Inattentional Blindness’ consisted in participants watching a video, instructed to
count passes among basketball players. In the middle of the video, a person
wearing a gorilla suit appeared (Simons & Chabris, 1999 in Seegmiller, Watson,
and Strayer, 2011). At the end of the video, participants were accurate with their
pass counts, but only individuals with higher working memory capacity were more
likely to report seeing the gorilla (67%) than those with lesser working memory
capacity (36%) (Seegmiller, Watson & Strayer, 2011). Accordingly, even when
people know that they are doing a task in which an unexpected thing might happen,
that doesn't suddenly help them notice other unexpected things. And, once people
find the first thing they're looking for, they often don't notice other things
(Attwood, 2018). Indeed, as the tasks to be completed and the information to pay
attention increase, the probability of leaving out an innumerable series of details
increases too. The perception becomes more and more summarized until it
disappears completely in the peripheral areas of the visual and the cognitive field.
Although we have the feeling of being able to grasp every aspect of reality in front
of us with a single glance, the cognitive resources available are limited, and we as
humans observe everything through a much smaller window than we think (Chater,
2018). Now that we demonstrated the existence of the Cognitive Cost by
highlighting some of the phenomena that derive from it, it is necessary to identify
the causes that make the Cognitive Cost a source of illusions and distortions
capable of affecting the decision-making processes. To explain why Cognitive
Cost exists, it is necessary to focus on the anatomical structures of the organs that
are fundamental to perception processes, in particular the eyes and the brain.
Visual attention plays a fundamental role in cognitive processes and if we want to
understand how far it can extend, we need to understand how the information
15
coming from the outside world passes through a sort of extremely limited window,
consisting of some photoreceptors concentrated in specific parts of the retina
(Chater, 2018). As demonstrated before, if when we look directly at an object we
can fail to recognize substantial changes, it is important to understand the
dynamics according to which this happens. The input for the interpretation of these
complex information sets reaches the brain through two equally complex organs:
the eyes. Vision is one of the most important transmission channels for a human
being and the ocular anatomy, described in Figure 1.2, clearly justifies the
phenomenon of ‘Change Blindness’ and ‘Inattentional Blindness’ described
before.
Figure 1.2 shows the anatomy of vision and the concentration of cone
photoceptors in the fovea.
(Chater, 2018)
Vision depends on the brain as much as on the eyes. The eyes’ main task is to
detect patterns of light, which then work with the brain to turn those patterns into
images. Specific photoreceptors called ‘cone’ cells and ‘rod’ cells are the means
by which everything assumes a shape, a colour and certain features. The teamwork
of these two fundamental components of the visual experience builds that feeling
that everyone has of perceiving a room full of objects, a beautiful landscape, a
university classroom and any other aspect of reality (Chater, 2018).
It is the anatomy of the eyes that prevents the eye from perceiving an entire scene
and at the same time in specific detail. The sensation we get when we look around
16
us is that of detecting simultaneously, and in a detailed way, not a small portion of
space but the entire visual spectrum. Indeed, those special photoreceptors called
‘cone’ cells are the only ones capable of detecting colour and they thicken where
the individual fixes his/her gaze (Chater, 2018). The ‘cone’ cells are concentrated
in a particular ocular area which takes the name of fovea. Directing the fovea
means to decide what we want to focus on, that is, what we want to put our visual
attention on. Knowing this, we can understand why we are unable to notice the
gorilla and why we don’t notice when an object disappears if we are directing our
focus toward something else.
In this sense, there is a real trade-off dictated by the ocular structures when it comes
to attention and visual perception. As the distance from the fovea increases, the
ability to grasp details, decipher texts, notice changes and process complex
information gradually fades and diminishes, as shown in Figure 1.3 (Chater, 2018).
Figure 1.3 shows the relationship between the distance from the fovea and the
relative worsening of the visual experience.
(Chater, 2018)
In summary, it can be said that the sensation of a complete and detailed perception
of reality is illusory and the ocular anatomy confirms this thesis. At this point, it
could be argued that it is enough to direct the gaze toward what needs to be noticed
to be sure that perception does not fail. However, it is not enough. Indeed, even if
17
we direct our gaze to the fraction of space hosting the unexpected stimulus, there
is a probability that it will not be noticed.
An experiment carried out by the two psychologists of perception, Arien Mack and
Irvin Rock, in 1999, clearly demonstrates that when the mind is busy carrying out
a main task, selective attention prevents us from noticing even what is directly
fixed, that is, what falls into the fovea (Mack & Rock, 1999 in Chater, 2018). The
major aim of the experiment was to set a small cross in the centre of a screen,
which was then replaced by a larger cross for a short time, and the participant had
to evaluate which of the two axes was longer. A mask followed the vision of the
larger cross to prevent that a shadow of this remained visible in the eyes of the
participants (Mack & Rock, 1999 in Chater, 2018). The results were remarkable:
when the unexpected stimulus (a small dot) was positioned a few degrees outside
the fovea, 21% of the subjects were unable to perceive it, while when the point
was moved in a way that it fell fully under the gaze and fovea of the respondents,
the percentage of ‘Inattentional Blindness’ rose to 85% (Mack & Rock, 1999 in
Chater, 2018).
For this reason, analysing the dynamics of visual attention is not enough to account
for the existence of the Cognitive Cost in all its forms. It is necessary to dwell on
the ways in which the brain organizes and gives meaning to the information that
comes to it from the surrounding environment in order to understand the results of
the experiment. If each glance builds a small piece of the big puzzle that is reality,
the brain and the dynamics of cognition are responsible for their assembly (Chater,
2018). And just as it is not possible to perceive all the pieces of the puzzle at the
same time, it is not even possible to assemble them (Chater, 2018). Each task or
problem requires us to proceed step by step by absorbing large amounts of
cognitive resources and high levels of attention, not only visual, but also mental
(Chater, 2018). Therefore, visual attention plays a fundamental role in the
processes of perception and cognition as it is the first step in the much more
complex system guided by the human brain. But although the human mind can be
18
seen as a very sophisticated computer that does solve complex problems, it has
several limits of which we provided evidence in this section of the chapter.
Indeed, while on a computer it is possible to carry out many simple activities
simultaneously and quickly, to maximize the potential of the brain one must avoid
any kind of interference and focus on only one aspect of reality at a time. Doing
the opposite would mean to test one's cognitive structure, resulting in potentially
dramatic outcomes. Accordingly, the Cognitive Cost exists due to the information
processing mechanisms for interpreting reality. Thus, whenever the attention is
focused on a task, activity, conversation, film or even a sound, hundreds of billions
of neurons, forged by past experience and memory, ignite and exchange electrical
impulses on vast interconnected networks that interpret, organize, and share the
input that reaches them through the senses. The more complex the input, the worse
the result of the process will be. Hence, it is possible to think, see and perceive in
detail only one thing at a time.
After having proved with evidence the existence of the Cognitive Cost and having
outlined why it exists, the next chapter of this academic work will concentrate on
the ‘Paradox of Choice’ applied to the Digital Era, the one in which we are living,
with a focus on streaming platforms.
19
CHAPTER II – THE ‘PARADOX OF CHOICE’ IN
THE DIGITAL ERA: A FOCUS ON STREAMING
PLATFORMS
This chapter will explore and discuss the techniques used by a few streaming
platforms to solve the issue of the ‘Paradox of Choice’. The cases that will be taken
into consideration are: Disney Plus, Amazon Prime Video and HBO Max, each
presenting some peculiarities in the methods implemented for filtering content.
Specifically, this chapter also aims to provide a proper introduction to the final
one, which will focus on the discussion of the ‘Paradox of Choice’ applied to the
Netflix case study, which constitutes the colossus with the most effective
techniques in reducing the Cognitive Cost.
2.1 The ‘Paradox of Choice’ in the Digital Era: Relevant
Examples
In the past, much of human progress has involved reducing the time, energy and
number of processes we have to engage in and think about in order to obtain the
necessities of life (Schwartz, 2005). In the past few decades, though, the long
process of simplifying has been reversed. Increasingly, the trend moves back
toward time-consuming mechanisms.
Novelist and existentialist philosopher Albert Camus stated that everything in life
is a choice (Schwartz, 2005): every second of every day, we are choosing. Today
we face multiple choices in all areas of life: education, career, friendship, sex,
romance, parenting, religious observance, home utilities, retirement plans and so
on. As choice is abundant in what concerns each of these areas, we can take into
21
account Maslow’s Hierarchy3 of Needs (1954), represented in Figure 2.1, to
concretely notice how each section is affected.
Figure 2.1 represents Maslow’s Hierarchy of needs graphically.
(Maslow, 1954 in McLeod, 2018)
Starting from the bottom of the pyramid, we have basic needs, including
physiological needs as food, water, clothing and warmth, and safety needs, such as
security and safety (Maslow, 1954 in McLeod, 2018). For this area we have a
remarkable example in supermarkets: Walmart sells over 160 million different
products online and in stores, Conad and Coop sell, respectively, 3,500 and 4,500
products just considering the items of their own brands, and Esselunga offers more
than 15,000 products per category online. Another significant example is the one
of utilities. We are presented with more and more providers for electricity and gas,
3
Psychologist Abraham Maslow’s hierarchy of needs is a motivational theory in
psychology comprising a five-tier model of human needs, often depicted as hierarchical
levels within a pyramid. The lowest levels of the pyramid are made up of the most basic
needs, while the more complex needs are located at the top of the pyramid. According to
Maslow, when a lower need is met, the next need on the hierarchy becomes our focus of
attention.
22
and even phone providers multiplied in the last few years. Food delivery is another
instance. We have, indeed: Just Eat, UberEATS, Deliveroo and Glovo, each of
them offering a huge array of options.
We then have psychological needs, including belongingness and love needs, and
esteem needs. With regards to the first category, the most relevant example is in
relationships. Back in the day, our grandparents had a limited pool of options for
who they could date. Without the Internet, they had to rely on meeting people inperson and the number of single people they met within a suitable age-range was
not very large. Today we have websites like Match.com and Tinder, with millions
of members. It seems certain that users will eventually find ‘the perfect match’.
However, as a result of the ‘Paradox of Choice’, people seem less likely to commit
or spend the necessary quality time getting to know someone. With all these
options come more opportunities to regret our decisions and once we see a little
flaw in a date, we think that there must be someone better out there for us (Moran
Cerf, Manuel Garcia-Garcia, 2017).
To conclude the pyramid, even from the self-fulfilment perspective we experience
the same issue. For instance, today there is a huge choice for universities which
offer a wide range of possible courses. People can decide what to study going
deeper and deeper into a sector and careers are built differently, it is not a onepath-only anymore. As a result, seeking personal growth may seem easier but it
also becomes more demanding and paradoxically confusing.
Online and offline merge more and more into each other. Whereas in the past
retailers were dividing online and offline into two separate areas, in today’s Digital
Era it’s more about bridging the gap between the two, in order to provide an
omnichannel experience that exceeds customer expectations (Kotler & Stigliano,
2018).
However, if in the physical environment we have a limited space, in the online
environment the storage is nearly to infinite, and the options proliferate more
quickly. An iconic example of cognitive load in the digital era concerns the
23
streaming platforms’ landscape. The most popular include Netflix, Disney Plus,
Amazon Prime Video and HBO Max. All of them offer, in turn, a vast array of
options. Netflix offers about 3,600 movies, and more than 1,800 TV series (Just
Watch, 2022). There are several movie genres, but it is still very difficult to decide,
even though between 2014 and 2019 the company reduced its catalogue of about
40% (Just Watch, 2022). Netflix represents the perfect merger between online and
offline, since it started as a DVD rental company and it still offers the service
together with the streaming platform model it became. Disney Plus offers more
than 740 movies and more than 280 TV shows are available to subscribers (Just
Watch, 2022). We talk about 20% less of Netflix’s catalogue, but its titles are more
appreciated according to IMDb (Internet Movie Database4). Amazon Prime Video
offers more than 26,000 movies and more than 2,700 TV shows, and HBO Max
includes about 2,000 movies and more than 580 TV (Just Watch, 2022).
While it might seem that providers are in competition with regards to which
company is able to offer the widest possible choice, it becomes apparent that too
many options might, in turn, transform into the ‘Paradox of Choice’ situation,
something that emerges as a real threat to companies: for this reason, marketers
should be careful to not overload consumers. However, if a company does not want
to give up its range of possible choices, there are specific techniques they can apply
to avoid customers to experience the ‘choice overload’ condition.
In the following sections we will focus on the strategies that Disney Plus, Amazon
Prime Video and HBO Max chose to implement in order to reduce the ‘Paradox of
Choice’. We will, then, complete the circle in the last chapter, outlining Netflix’s
strategies, which represent the most advanced in terms of algorithms and machine
learning implementation.
4
Website for entertainment information, with features designed to help fans explore the
world of movies and shows and decide what to watch. It provides information about
millions of films and television programs as well as their cast and crew.
24
2.2 Disney Plus and the Six Pillars
Disney Plus is an American subscription video on-demand streaming platform,
owned and operated by the Media and Entertainment Distribution division of The
Walt Disney Company5 (DMED Media, 2022). The service primarily distributes
films and television series produced by The Walt Disney Studios and Walt Disney
Television. The platform also offers original films and TV series (DMED Media,
2022). Disney Plus was first launched in the late 2015, under the name of
DisneyLife. This was part of an experiment conducted in the UK to test the
streaming platform (DMED Media, 2022). Disney Plus was then officially
launched in November 2019 in the United States, Canada, and the Netherlands,
and expanded to Australia, New Zealand, and Puerto Rico a week later. It became
available in all Europe in March 2020. Upon launch, it was met with positive
reception of its content library, but it was criticized for technical problems and
missing content. Alterations made to films and television shows attracted media
attention too (Business Insider, 2019). Ten million users had subscribed to Disney
Plus by the end of its first day of operation and the service has now 137.7 million
global subscribers6 (Statista, 2022).
From a more technical perspective, we can observe that the main characteristic of
the Disney Plus interface is that it groups its content into six pillars: Disney, Pixar,
Marvel, Star Wars, National Geographic and Star. The interface is shown in Figure
2.2. This method of categorization can be considered one of the main aids in
finding content on the platform, which reduces by far the possibilities of
experiencing a ‘Paradox of Choice’.
The Walt Disney Company is an American multinational mass media and entertainment
conglomerate, headquartered at the Walt Disney Studios complex in Burbank, California.
5
6
As of the 2nd of April, 2022.
25
Figure 2.2 shows Disney Plus interface, subdivided into the six categories
which can be easily noticed as soon as you enter the homepage.
(Disney Plus, 2022)
The platform also makes use of machine learning and algorithms to categorize
content for its users. Between the major drivers that power recommended content,
there is, for example, what users watched and what they didn’t watch, meaning
what was shown and what they actually clicked (Forbes, 2022). The
recommendation system also considers the time of the day in which one’s was
watching, his/her viewing history and how well it matches other people's viewing
history (Forbes, 2022). Indeed, if a user’s history looks a lot like another one’s
history, they would probably like the same programmes. In this sense, it's not just
your own viewing history but also how people are using the product in general
(Forbes, 2022). Disney Plus is also constantly looking at the particular context of
the experience. For instance, if the user is watching a short, he might be in the
mood to watch another short, and not necessarily invest into a long movie (Forbes,
2022). This set of factors represents the main criteria on which the
recommendation system of Disney Plus works. Of course, the employed algorithm
varies according to the user experience with the product (Forbes, 2022). Hence, it
differs between a user who just subscribed and a user who, on the contrary, has a
rich history on the platform.
26
In general, we have to say that Disney Plus has the benefit of a more condensed
content assortment compared to other streaming platforms (Stead, 2022). This
feature allows the platform to require a less demanding recommendation system.
Indeed, thanks to the more limited offer in terms of titles, the efforts Disney Plus
must take against the ‘Paradox of Choice’ cannot be compared to the ones of other
platforms with almost infinite options. (Stead, 2022). For instance, Amazon Prime
Video, HBO Max and Netflix, which are the other streaming platforms analysed
in this academic work.
2.3 Amazon Prime Video and the Collaborative Filtering
Amazon Prime Video (APV) is an American subscription video on-demand
streaming and rental service owned by Amazon7. It is offered either as a standalone
service, or as part of Amazon's Prime membership (Winston, 2022). The service
primarily distributes films and TV series produced by Amazon Studios and MGM
Holdings or licensed to Amazon, as Amazon Originals. The service also hosts
material from other sources, content add-ons, live sporting events, video rental,
and purchasing services (Winston, 2022). Amazon Prime Video is one of the
streaming platforms with the widest availability of choices (Just Watch, 2022).
The service was launched in the United States in September 2006, under the name
of Amazon Unbox. It grew with an expanding library and it added the Prime Video
membership upon the development of the Prime subscription. It launched
worldwide in December 2016.
In order to defeat the ‘Paradox of Choice’, the approach adopted by Amazon Prime
Video was the same used by Amazon in selling products as of 2012. Nevertheless,
it turned out that Amazon trained its algorithms to recommend ‘safe bets’
(Roettgers, 2019). In particular, in the case of Prime Video, classic movies were
Amazon.com, Inc. is an American multinational technology company which focuses on
e-commerce, cloud computing, digital streaming and artificial intelligence. It is one of
the world's most valuable brands: it is one of the Big Five American information
technology companies (Google, Amazon, Meta, Apple, and Microsoft).
7
27
recommended, which were very likely to be appreciated by users, for instance
Breakfast Club or Casablanca. However, while these movies had high ratings with
audiences and critics, they weren’t exactly what those consumers in specific
wanted to watch during their movie night (Roettgers, 2019). After this crisis,
Amazon’s engineers refined the algorithms, which are now trained on Amazon’s
entire catalogue. This knowledge has also been applied to new titles to recommend
the latest releases and the result was a huge improvement over filtering (Roettgers,
2019). Indeed, Amazon Prime Video has an advantage point as it can benefit of all
of the data about user ratings, interactions and perhaps new user attributes relevant
to different domains, for example books. This data is precious in understanding
customers’ preferences according to movie genres and TV series, and allows the
recommender system to work more effectively. Therefore, Amazon Prime Video
centres its recommendation system mainly on a collaborative filtering8 method,
which looks at what other users are doing (AMT Lab, 2021).
2.4 HBO Max as a Human-First Platform
HBO Max is an American on-demand streaming platform owned by Warner
Media9 (Business Insider, 2022). The service was launched in the United States in
2020, and it is built around the libraries of HBO, Warner Bros Entertainment, and
their related brands (Business Insider, 2022). Today HBO Max is only available in
the US and a select number of European, Caribbean, and Latin countries
(Pressroom, 2022).
8
The collaborative filtering method is based on past interactions that have been recorded
between users and items. This technique looks for what similar users like in order to
classify them into clusters. It will then make recommendations to each user according to
its cluster’s preferences.
9
Warner Media is a leading media and entertainment company that creates and distributes
premium and popular content from a diverse array of talented storytellers and journalists
to global audiences through its brands, including: HBO, HBO Max, Warner Bros., TNT,
TBS, DC, Cartoon Network and others.
28
In relation to filtering content, HBO Max tried to take an alternative approach that
utilizes a hybrid of algorithm and human curated content, with a particular focus
on the human touch. Indeed, the service is positing itself as a human-first platform
(Alexander, 2020). Instead of supposing what you might like by looking at your
viewing history and the shows beloved by people who have similar interests,
HBO’s algorithm crawls Twitter and compiles interviews with real people to
determine which shows are popular and then collects them on one site where
viewers have the option of choosing what they wish to watch (Watercutter, 2019).
HBO Max intends to have both employees and celebrities alike creating lists for
users (Watercutter, 2019). We must say that this feature is a big point of
differentiation from other service providers, which are instead ruled by
recommendation algorithms designed to predetermine what people want (Glenday,
2019). Sarah Lyons, HBO Max’s Senior Vice President of Product, believes that
this advantage is critical, and she stated that in the future the platform intends to
incorporate even more human-focused curation by connecting users with other
human recommendations like friend-to-friend ones (AMT Lab, 2021).
All the aforementioned streaming platforms present peculiarities in how they
recommend content to users: Disney Plus is characterized by the grouping of
content into the six pillars, while Amazon Prime centralizes its recommendations
on the collaborative filtering method, based on users’ past interactions. In turn,
HBO Max offers a more human centric approach, taking into consideration
opinions on social media and preferences of real people.
In the next chapter, our focus will shift to the Netflix company, which is exemplary
in order to understand how machine learning and algorithms can really win over
the ‘Paradox of Choice’.
29
CHAPTER III – THE NETFLIX CASE STUDY
This third chapter is intended to analyse the Netflix case study in relation to the
‘Paradox of Choice’ phenomenon. In the first section, I will present the company
and outline its history, with a focus on the wide arrange of choice it offers and the
advantages but also the disadvantages that derive from it. The second part of the
chapter is aimed at presenting the solutions adopted by Netflix to prevent
consumers to experience a choice overload. I will discuss in detail Netflix
recommendation system and the algorithms and techniques implemented. I will
conclude the chapter with a comparison between the physical and the online store
assortment in relation to the ‘Paradox of Choice’. In particular, this last section
will highlight the importance of using these techniques in an online environment
in order to build loyalty and customer retention.
3.1 When Too Much Is Not Good Enough: The Netflix Case
Netflix Inc. is a streaming entertainment service company which provides
subscription service streaming movies and television episodes over the Internet
and sending DVDs by mail (Forbes, 2022). It operates through the following
segments: Domestic Streaming, International Streaming and Domestic DVD10
(Forbes, 2022). For the scope of this analysis, the focus will be exclusively on the
streaming categories.
Netflix was founded in 1997 by Wilmot Reed Hastings and Marc Randolph and
its corporate headquarters are in Los Gatos, California (Forbes, 2022). In 1999
Netflix began to offer an online subscription service through the Internet.
Subscribers could choose movies from the Netflix’s website, and the shows were
then mailed to them in the form of DVDs from one of the one hundred Netflix’s
distribution centres, in prepaid envelopes. While Netflix had thousands of movie
10
The Domestic DVD service concerns the United States only.
31
titles in its catalogues, the number of DVDs to be possessed at one time was limited
to the subscription plan (Britannica, 2022).
In 2007, Netflix began offering subscribers the option to stream some of its movies
and television shows directly to their homes through the Internet (Netflix, 2022).
For most subscription plans, the streaming service was unlimited (Britannica,
2022). By 2016, its streaming service was available in more than 190 countries.
While its streaming services became the biggest revenue generator with more than
200 million subscribers in 2021, the rental division remained profitable
(Britannica, 2022).
Netflix begun funding its own original programming in 2013, with the episodic
drama series “House of Cards”, offering video content produced specifically for
its streaming service (Fernandez, 2022). Such content became a major focus of
Netflix, and by the end of 2021 it had offered more than 2,400 original titles.
Notable
series
include
“Unbreakable
Kimmy
Schmidt”, “Stranger
Things”, “Narcos”, “The Crown”, “Bridgerton” and “Squid Game”. Netflix also
started producing many movies, for example “Roma” (2018), which won three
Academy Awards (Britannica, 2022).
The business model of Netflix is subscription-based, and it offers three plans:
basic, standard and premium (Cuofano, 2022). In simple terms, the more
subscribers, the higher its revenue (Chong, 2021). Netflix generated over $29.6
billion in 2021, with an operating income of over $6 billion and a net income of
over $5 billion (Cuofano, 2022).
Netflix offers a large collection of TV shows and movies, including critically
acclaimed originals. This makes it one of the best video streaming services to date
(Key & Minor, 2022). Although its streaming service started as a niche of the
company's DVD-mailing service, it is now the front-runner in the category (Key
& Minor, 2022). The platform has been positioned in the first place by the famous
website Cnet.com, for its wide variety of familiar network shows and more original
series, films and documentaries (Rayome, 2022). However, as a wide choice can
32
result in a competitive advantage, it can also lead to a state of confusion and
indecision that brings to a situation of cognitive overload. The more choices we
have, the more we struggle to choose. Known as the ‘Paradox of Choice’, it creates
decision fatigue for Netflix’s users (Anderson, 2021). Like Barry Schwartz stated
in his book11, it is evident that with the increasing of the number of options,
satisfaction decreases.
This dissatisfaction can derive from a combination of factors (Anderson, 2021).
First, we take mental responsibility for what we choose. It has been found that this
is even more true if others are influenced by our decisions, for instance in the case
we are in a group of people and we are the ones in charge to choose. Second,
decisions are harder with more options, and mistakes are more likely. We take
much longer to make decisions and then we are angry with ourselves for having
selected the wrong option wasting a considerable amount of time. Last, we imagine
the perfect alternative and we set our expectations to that. This phenomenon is
called ‘counterfactual thinking’: we construct a so-called dream alternative, which
in reality might not even exist (Anderson, 2021).
In relation to the time-consuming component, we can better state that users
experience the Hick’s Law, a theory according to which the more options you have,
the more time you need to respond. Figure 3.1 offers a visual understanding of the
matter.
11
Schwartz, B. (2005). The Paradox of Choice: Why More is Less. New York, United
States: Harper Perennial.
33
Figure 3.1 shows that with the increasing of the number of options, the time
taken to respond increases exponentially.
(Laurent, 2022)
“The virtue is that users want the power and control of the product. But along with
that power and control comes that frustration that can soak up precious watch time:
‘I’m browsing too long, and I’d rather actually be watching right now.’”
(Glen Davis, in Laurent, 2022)
This sentence explains in concrete terms how users feel: a wide assortment makes
them feel powerful and free to choose but, at the same time, it makes them
experience frustration and they would happily give up some freedom for a less
time-consuming choice (Laurent, 2022). The Netflix case sustains this thesis.
Indeed, the interactions with Netflix decreased over time, because of its too wide
availability of options, as Figure 3.2 shows. (Anderson, 2021).
34
Figure 3.2 shows how Netflix’s Average User Interaction decreased between
2016 and 2018.
(Anderson, 2021).
Fortunately, in 2019 Netflix performed much better compared to the data presented
in Figure 3.2, thanks to the solutions implemented in order to avoid consumers to
experience the ‘Paradox of Choice’ (Anderson, 2021).
Netflix functions according to the logic of todays’ consumers society, presenting
a situation in which the offer overcomes by far the demand (Flamigni, 2021).
Therefore, the human mind blocks in front of too much information that has to be
processed, often resulting in giving up the choice (Flamigni, 2021).
Another obstacle for Netflix is the continuously growing target market. If before
Netflix was addressed mainly to early adopters (individuals passionate about TV
series and movies), it now reaches out to a more generalist crowd with different
tastes and interests (Flamigni, 2021). Trying to satisfy a big target market like this
becomes more and more difficult, as the two divisions of people are very different
between each other (Flamigni, 2021).
35
Due to fierce competition in the market, streaming platform companies need to try
to ensure that customers are satisfied and spend the least amount of time on content
search (Oat, 2013). In regard to competition, Disney Plus is the closest competitor
of Netflix (Rayome, 2022). This competitor represents a threat to Netflix, as it
offers very attractive titles but a more restricted choice12, which favourites the
satisfaction of users (Flamigni, 2021). Amazon Prime Video constitutes, on the
other hand, the biggest competitor in terms of very wide choice, but as we saw in
the previous chapter, it just mainly relies on a collaborative filtering method.
Meanwhile, Netflix implements better refined metrics, relying on numerous
algorithms and considering multiple criteria, making the decision-making process
far easier for its users. These solutions will be discussed in detail in the following
section of the chapter.
3.2 How to Overcome the Cognitive Overload? The Netflix
Solutions
Consumer research suggests that a typical Netflix member loses interest after 60
to 90 seconds of choosing, having reviewed 10 to 20 titles (perhaps three in detail)
on one or two screens. The user either finds something of interest or the risk of
abandoning the service increases substantially. The recommender problem is to
make sure that, on both those two screens, each member in Netflix diverse pool
will find something to view and will understand why it might be of interest
(Gomez-Uribe and Hunt, 2015).
In order to understand how important for Netflix is to minimise the cognitive cost,
it is exemplar to mention that, in 2006, the company offered one million dollars to
whoever would have been able to improve the accuracy of the recommendation
algorithm of 10% (The Netflix Effect, 2016). One of the aims was to forecast the
12
While Netflix offers about 3,600 movies and more than 1,800 TV series, Disney Plus
offers about 740 movies and 280 TV (Just Watch, 2022).
36
rating a user would have given to a movie, which is pivotal to simplify choice to
consumers and to give the opportunity to build customer loyalty in an easier way
(The Netflix Effect, 2016). Considering that the shift from DVD rental company
to streaming online platform brought an exponential growth of titles, with no
filtering it would be impossible to choose. Thus, anticipating customers’ choice,
building personalized home pages and limiting the number of alternatives becomes
vital.
“I don’t want our brand to influence our programs, and I don’t want the programs
to influence our brand. Netflix is about personalization. [..] If you ask five people
what they love about Netflix, they will give you five dramatically different
answers. So, we have to be really careful to ensure our brand is really about the
shows you love, not about the shows we tell you about.”
- Ted Sarandos, Chief Content Officer, Netflix (Frey, 2021).
Here, Netflix’s CCO Ted Sarandos explains the commitment of Netflix in being
about personalization. He stresses the point that Netflix does not want to influence
programmes in any way, but it just tries to represent what’s best for the user at
issue (Frey, 2021). Indeed, Netflix cannot be limited to the grouping of products
according to popularity, but it needs to adopt several techniques to avoid users to
experience the ‘Paradox of Choice’ through customization. This need is fulfilled
with the use of machine learning and algorithms which help in transforming users’
behaviour into predictions of preferences. The strategies used by Netflix to guide
its users give life to the recommendation system: a set of elaborated data able to
inform about which contents are consumed by each user, on which device and in
which moment (Chong, 2021).
This recommendation system takes into account several variables, such as the
interactions of users with the system, the time of the day in which they are
watching, the device they are using and for how long. Netflix also groups
subscribers who seem to share similar interests and preferences, in order to filter
contents in an easier way. This set of information constitutes the inputs elaborated
by the algorithms (Netflix, 2022).
37
Netflix’s recommender system transformed from a regression problem predicting
ratings to a ranking problem, to a page-generation problem, to a problem
maximising user experience. For instance, maximising the number of hours
streamed and personalising most of its features (Basilico, 2019). Indeed, 80% of
stream time is achieved through the recommender system, which is a highly
impressive number (Chong, 2021). Moreover, Netflix believes in creating a user
experience that will seek to improve retention rate, which in turn translates to
savings on customer acquisition (Chong, 2021).
A primary component of the strategy is the Personalized Video Ranker Algorithm
(PVR). Netflix utilises a two-tiered row-based ranking system, where ranking
happens within each row, with strongest recommendations on the left and across
rows, and on top (Chong, 2021). There are about 40 rows13 on each homepage and
up to 75 videos per row; these numbers vary across devices because of the
hardware and user experience considerations (Frey, 2021). The videos in a given
row typically come from a single algorithm. Genre rows such as ‘USA TV
Dramas’, shown on the left of Figure 3.3, are driven by the Personalized Video
Ranker (PVR) algorithm.
13
The organization in rows offers an advantage both for the company and for users. As a
user, with coherent groups of videos in a row and a meaningful name for each row in a
useful order, you can quickly decide whether a whole set of videos in a row is likely to
contain something you are interested in watching. It allows members to either dive deeper
and look for more videos in the theme or to skip them and look at another row (Alvino C.
& Basilico J. 2018). As a company, it is easier to collect feedback. In a right-scroll on a
row would indicate interest whilst a scroll-down (ignoring the row) would indicate noninterest (not necessarily irrelevance) (Chong, 2021).
38
Figure 3.3 shows how content is organized on Netflix’s home page on a PC
device and gives a visual representation of how the Personalized Video
Ranker Algorithm works.
(Netflix, 2022)
As its name suggests, this algorithm orders the entire catalogue of videos (or
subsets selected by genre or other filtering) for each member profile in a
personalized way (Gomez-Uribe & Hunt, 2015). The resulting ordering is used to
select the order of the videos in genre and other rows and is the reason why the
same genre row shown to different members often has completely different videos.
PVR is used widely, for this reason it must be good at general-purpose relative
rankings throughout the entire catalogue. However, this generic feature limits how
personalized it can be (Gomez-Uribe and Hunt, 2015).
An evolution of this algorithm is the Top N Video Ranker, which tries to find the
best few personalized recommendations in the entire catalogue for each member,
as shown in figure 3.4 (Gomez-Uribe & Hunt, 2015). Accordingly, it is optimized
and evaluated using metrics and algorithms that look only at the head of the
catalogue ranking that the algorithm produces, rather than at the ranking for the
entire catalogue (as it is the case with PVR) (Gomez-Uribe & Hunt, 2015).
39
Figure 3.4 shows the ranking created by the Top N Video Ranker Algorithm.
(Netflix, 2022).
However, the Top N Ranker and the Personalized Video Ranker algorithms share
similar attributes. For instance, they combine personalization with popularity, and
they identify and incorporate viewing trends over different time windows ranging
from a day to a year (Gomez-Uribe & Hunt, 2015).
Another game changing algorithm is the Trending Now Ranker. It captures
temporal trends which Netflix deduces to be strong predictors, like it is shown in
Figure 3.5.
Figure 3.5 shows the trending movies and TV series of the moment according
to the Trending Now Ranker algorithm on Netflix’s home page.
(Netflix, 2022)
Other than personal preferences, a key role is indeed played by collective
preferences. These short-term trends can range from a few minutes to a few days.
They are typically events that have a seasonal trend and repeat themselves such as
Valentine’s Day or one-off, short-term events like pandemics or other disasters,
leading to short-term interest in documentaries about them (Chong, 2021). In the
example of Valentine’s Day, Netflix adapts its home page to the trend, which in
this case would be romantic and sentimental movies.
Therefore, data used by Netflix can also be short-lived in order to adjust the home
page to users’ emotional states daily. They embrace two dimensions: the individual
experience (preferences, interests, expertise, motivations, personality, interactions
40
with the system) and the external and situational dimension (social context, device
in use) (Gomez-Uribe & Hunt, 2015).
Two other algorithms are the Continue Watching Ranker, also named Video-Video
Similarity Ranker (showed in Figure 3.6) and the Because You Watched (BYW)
algorithm. They are based on choices made beforehand and they both use an itemitem similarity matrix (Chong, 2021). The former is aimed at distinguishing the
videos which have been interrupted due to a lack of interest from the ones the user
wants to resume watching. The latter identifies subsets of titles which are similar
with films who have been already watched (Gomez-Uribe & Hunt, 2015). The
outputs of those algorithms give life to a home page which is entirely personalized,
able to guide the user in his decisions, allowing to have a vast array of titles but
minimising the cognitive cost.
Figure 3.6 shows the Continue Watching Ranker Algorithm, which selects
just a few TV series and films, and not all the interrupted ones.
(Netflix, 2022)
Each of the above algorithms go through the row generation process seen in figure
3.7.
If the Personalized Video Ranker Algorithm (PVR) is seeking for Romance titles,
it will locate candidates who meet the genre while also providing evidence to
support the presentation of a row, such as previously seen Romance films (Alvino
& Basilico, 2018).
This is an algorithm of Evidence Selection which is incorporated in every other
ranking algorithm listed above to create a more curated list ranking of items. This
algorithm uses all the information Netflix shows on the top left of the page,
including the predicted star rating (which was the focus on the Netflix’s prize
41
offered in 2006) and other pieces of information displayed about the video, such
as won awards, cast and crew or other data (Alvino & Basilico, 2018).
Furthermore, Netflix also uses this system of Evidence Selection in order to select
the images believed to be more efficient in attracting the specific user, representing
another method of information skimming (Gomez-Uribe & Hunt, 2015).
Figure 3.7 shows the row generation process used by algorithms on Netflix.
(Alvino & Basilico, 2018)
Netflix also recently added the function “Play Something” to find something to
watch based on previously watched programs and to eliminate, or at least reduce,
the decision fatigue felt by users (Laurent, 2022). This new shuffle feature is
optional: users can select something on the actual home page or click on the “Play
Something” button as shown in Figure 3.8. In this case, the Netflix matrix will
choose something to watch and it will briefly explain the choice while the movie
starts (Laurent, 2022).
42
Figure 3.8 shows the function introduced recently by Netflix, available on the
Netflix TV app and on the Android mobile app.
(Netflix, 2022)
To launch this new viewing mode, the production team of Netflix conducted user
research using psychological principles to understand decision fatigue in order to
offer this new viewing mode (Laurent, 2022).
The function, however, was inspired by traditional TV. Indeed, with traditional
TV, the decision fatigue is reduced as you just turn on the TV and the program is
there. The choice may be difficult because of the multiplication of channels, but
users cannot change what is programmed. With Netflix and this new function, it is
possible to have both: you can choose or let the matrix choose for you (Laurent,
2022).
Relevant to mention is also Netflix’s Top 10 Daily Ranking (Figure 3.9). A show
is considered viewed each time a Netflix account watches it for at least two
minutes; this two-minute window removes the impact of content’s length from the
metric. The number one show on the daily list is the one that accumulates most
views, within the last 24 hours, in the country in which the user is watching. The
rest of the list is organized based on this metric too.
43
Figure 3.9 shows Netflix’s Daily Top 10 in Italy updated on 19th April 2022.
(Netflix, 2022)
In light of what we discussed about the two instrumental systems for learning,
reasoning, and social processing, we can say that the top ten most-watched films
in the country, as well as the "Play Something" function, are System One and
System Two nudges whose aim is to reduce cognitive effort. The System One
dynamic refers to movies that are highlighted, while the System Two function for
the movies that are chosen from a 'social' rating of the most-watched movies.
Finally, the Page Generation algorithm uses all of the algorithms above to
personalize which rows will appear and in what order (AMT Lab, 2021).
While the algorithms alone are impressive, Netflix also runs about 250 A/B tests
every year on around 100,000 users (AMT Lab, 2021). The goal is to not rely on
the algorithms but to use real data to figure out what is working. More recently,
data from Netflix Party, a browser extension that allows users to watch with other
users, has been incorporated into the service. The data from this browser allows
the company to expand the knowledge of users’ preference outside of what they
are watching (AMT Lab, 2021).
Among all the streaming platforms, Netflix is the most transparent in terms of the
machine learning and algorithms implemented, as the company clearly speaks
about them (AMT Lab, 2021).
The next section of this academic work will offer a comparison between the
physical shop window and the digital one, which can be personalized, as in the
example of Netflix.
44
3.3 Physical Store vs. Online Store
“Good businesses pay attention to what their customers have to say. But what
customers ask for (as much choice as possible, comprehensive search, navigation
tools, and more) and what actually works (a few compelling choices simply
presented) are very different.”
(Gomez-Uribe & Hunt, 2015)
Conventional wisdom suggests that larger assortments are beneficial to customers
because more options imply a greater likelihood that consumers will find an
alternative matching their preferences (Baumol & Ide1, 956; Hotelling, 1929;
Kahneman, Wakker & Sarin, 1997 in Chernev & Hamilton, 2009). However, we
know that making a choice from a larger assortment requires greater cognitive
effort than choosing from a smaller assortment because it involves evaluating more
options (Iyengar & Lepper, 2000). Larger assortments are likely to be more
confusing for consumers, even if they do not realize it (Huffman & Kahn 1998;
Sood, Rottenstreich, & Brenner 2004 in Chernev & Hamilton, 2009). The
important issue companies must face, here, is to combine what people ask for with
what they really want, which are, unfortunately, two different things. Companies
need to find a way to blend a wide assortment, meaning a high level of freedom
perceived, with a minimum cognitive cost (Gomez-Uribe & Hunt, 2015). This is
pivotal to reduce the probability of having consumers experience a choice overload
(Gomez-Uribe & Hunt, 2015).
In a Physical Store, it is possible to make the products that have more chances to
be chosen well noticeable. However, this works only for the items that are more
popular (Chernev & Hamilton, 2009). Another help is represented by the expertise
of the store staff, which can help clients simplify the choice. Although this might
help, it still remains a partial solution. When advertising and marketing strategies
are good, consumers look at each product on the shelves without being able to
create an order of preference and this often results in a no-choice. Besides, the help
45
of the store staff requires high levels of trust between buyer and seller, which is
not always the case (Chernev & Hamilton, 2009).
On the other hand, the Internet offers better tools to solve the issue, as we saw with
the Netflix case study. The new tools available in the digital era made the shift
from popularity of products to personalization possible. While in a physical
environment the shop window is the same for everyone, through the Internet it is
possible to personalize content for each user. For instance, in the case of Netflix,
the window shopping is customized, and it represents a great opportunity for
people to visualize content according to their taste and preferences. In fact, while
stated preferences are in most of the cases uncertain, preferences which derive
from behaviours are objective and indisputable: they even reveal aspects
individuals are unaware of (Gomez-Uribe & Hunt, 2015). Algorithms work in
function of previous behaviours, and they make predictions possible (GomezUribe & Hunt, 2015).
Presenting people with personalized products in a physical store would mean to
expose them to a cognitive suicide: not even an infinite number of shelves would
be able to show all the possible options and not even an infinite number of neurons
would be able to analyse them all (Gomez-Uribe & Hunt, 2015).
Internet systems can both personalize and filter products in a very effective way,
without overloading consumers. This concept is similar to ‘nudging’: a gentle push
towards a good direction which simplifies decision-making (Thaler & Sustein,
2014). Netflix, unlike a physical store, can position non relevant options at the end
of the list where they are not even perceptible, something that is not possible in a
physical shop as these products would be inevitably visible on the shelves. Each
user is exposed to a shop window that meets their needs. The better algorithmic
systems are, the better the single user experience and the higher the probability that
users will remain loyal to the brand (Gomez-Uribe & Hunt, 2015).
A 2013 global study conducted by Live Person showed consumers were far more
likely to ‘impulse buy' when in a store environment than when shopping for the
same products online. Part of this effect is driven by stronger System One
46
responses that arise from the greater amount of brand and purchase cues in-store
(Moran Cerf, Manuel Garcia-Garcia, 2017). Rather than just the text and images
available online, in-store decisions are influenced by physical interaction with
products, visual displays, audio cues and fragrances. The social pressure of the instore environment also means that people are less likely to back out of a purchase
once it has been started, whereas the absence of this in an online context makes
basket abandonment a huge risk (Moran Cerf, Manuel Garcia-Garcia, 2017).
Netflix does not have an online basket as it does not directly sell a product, but it
offers a service under a monthly payment and if users are not satisfied, the platform
loses subscriptions (Cuofano, 2022). Thus, it is pivotal to repeatedly refine
algorithms to avoid having users leaving the platform, as in an online environment
it is not possible to exploit some of the techniques available in a physical store.
Furthermore, when users are satisfied with the service, they develop loyalty and,
hence, are more willing to stay, without switching to other platforms.
47
CONCLUSIONS
Affirming that having a wide availability of options is detrimental, is not a simple
statement to make. The analysis undertaken in the first chapter aims at promoting
a real comprehension of how decision-making processes take place. A few
phenomena have been taken as an example to explain the limits of the human mind.
As we observed, neural biological computation has the defect of generating a
selectivity in attention due to both the slowness in processing the information of
the neurons individually taken, and to the structure of visual perception that allows
a detailed view of an extremely limited number of aspects of reality. On the other
hand, human cognition can count on a vast number of neural networks that,
working in a coordinated manner on different aspects of a single problem, are able
to solve complexities better than any form of artificial intelligence currently
existing. The goal, therefore, becomes to humanize decision-making problems, as
the human mind allows to think, see and perceive in detail only one thing at a time.
Vast arrays of options do not help in this case. However, we are overwhelmed by
them in every aspect of our everyday life and solutions need to be applied. The
research, in general, provides multiple evidence to the fact that consumers alone
have been found to prefer small assortments. The need for solutions is, hence, felt
both by individuals alone, and by the anatomy of the brain and of the visual system.
The second objective of this dissertation is to show that, in a digital environment,
it is far easier to fight against the ‘Paradox of Choice’, as it is possible to
personalize content. Streaming platforms are one of the most iconic examples in
relation to infinite choice possibilities. Netflix results in being the platform that
takes more seriously the ‘Paradox of Choice’ and its commitment in continuously
finding solutions is evident in the outcomes it produced, and continues to produce.
In fact, Netflix’s recommendation system has decreased customer churn by several
percentage points, and it is saving the company about one billion dollars a year for
effectively achieving customer retention. In this sense, what we want to prove in
this academic work is that personalized content is key to reduce the Cognitive Cost.
49
Although Netflix results are clearly performing, there’s always room for
improvement. In analysing the techniques implemented by some of its competitors,
it emerged that, for instance, HBO Max adopts a more human approach to
recommending contents, providing suggestions which do not derive from
algorithms or machine learning, but from real people. This feature represents an
interesting one for future studies on the Netflix case. In this regard, UX Planet, a
website which can be considered a one-stop resource for everything related to user
experience, conducted a study in 201914 on how Netflix could better manage the
‘Paradox of Choice’ issue.
The first aim of the study was to understand how users make choices of a show or
movie in a real-world scenario. The study included a sample of fifteen Netflix
users, which make use of the platform on a daily basis. Participants were presented
with a structured survey which would have helped to construct the user journey,
with the aim of discovering what steps people follow before choosing a show on
Netflix. The main parameter analysed included the sources from where
respondents got informed about content, including all the steps followed to
understand if that content is suitable for them. Among the sources from which to
choose
content,
the
options
taken
into
consideration
were
friends’
recommendations, Netflix recommendations, IMDb (Internet Movie Database)
recommendations, Google search, and trending over the Internet. What emerged is
that most users undertake a similar journey, which can be summarized with a
diagram, as showed in Figure 4.1:
Pareek, A. (2021, December 13). Netflix case study — Breaking paradox of choice UX Planet. Medium. https://uxplanet.org/breaking-paradox-of-choice-netflix-casestudy-7f29107d1e2b
14
50
Figure 4.1 shows the journey users follow before choosing what to watch.
(Pareek, 2021)
We can observe that, on average, users assess a new show on three or four
parameters before finally making a choice (Pareek, 2021).
To understand if the market effectively requires a recommendation system which
leaves more space to the human touch, further studies will be needed. The survey
conducted by UX Planet, could, indeed, offer a good starting point.
As we can observe in Figure 4.1, people tend to discover about new TV series and
movies from their friends. Hence, we can suppose that they get suggestions from
them, and as people usually trust their peers, it is easy for them to find out what is
worth to watch and what is not. This suggests that, in the Netflix scenario,
incorporating a few human-centred traits into the company's algorithms and
machine learning system would be a winning strategy.
In concrete terms, I think that an interesting point could be to implement to the
Netflix’s strategy an option that allows to see what friends are watching. This
feature would require the design of a new interface and a new system of
connections. Everyone would have the possibility to add to their profile some
friends and see part of their movements around the platforms, so that they would
not just be moved by their own taste (as the algorithms follow one’s path only) but
they would also be inspired by their friends’ activities on the platform.
51
As we already mentioned in this academic work, Netflix has recently added to the
platform ‘Netflix Party’. Data from this browser extension has been incorporated
into the service to expand the knowledge of users’ preference outside of what they
are watching. This effort shows that the company is already making some
significant progress in the direction of considering what people close to the
individual user are doing. Thus, implementing the feature I suggested, would just
be a further step to move towards a strategy that considers a more human
perspective. Additionally, it would allow users to better perceive that they are not
just guided by the platform alone, but that they can actively take part in what their
friends are doing. As a result, they can personally decide whether to watch the
movie/TV series or not, because they would know if the person's tastes are similar
to theirs.
52
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ACKNOWLEDGEMENTS
Vorrei innanzitutto ringraziare sentitamente la Professoressa Carmen Spanò, per i
suggerimenti, le preziose indicazioni e il sostengo che mi ha dimostrato in questi
mesi. In particolare, tengo a ringraziarla per la passione dimostrata nella sua
materia ‘Consumer Psychology and Neuromarketing’ e per la capacità di aver
suscitato in me interesse in tal modo da voler approfondire gli argomenti trattati
tramite questo elaborato.
Un grazie speciale va ai miei genitori, che mi hanno sempre sostenuta in ogni mia
scelta e che hanno sempre creduto in me, aiutandomi sia economicamente che
emotivamente. Grazie a loro ho potuto percorrere e concludere al meglio questo
cammino. Nello specifico, ringrazio mio papà per avermi trasmesso un po’ della
sua imprenditorialità e la sua costante voglia di fare e mettersi in gioco. Ringrazio
invece mia madre per avermi trasmesso la sua bontà, la sua calma e parte della sua
pazienza. Grazie anche a mia sorella maggiore Ilaria, per i valori che ogni giorno
mi trasmette e per il suo sostegno in prima persona nell’elaborazione di questa tesi.
Grazie ai miei nonni, ai miei zii e ai miei cuginetti, per l’affetto che non mi hanno
mai fatto mancare e per essere sempre stati orgogliosi di me e di ogni mio piccolo
traguardo.
Grazie a Fabrizio, il mio compagno di vita. La persona che più di tutte è stata
capace di capirmi e che mi ha insegnato che gli ostacoli esistono solo per essere
superati. A lui devo gli ottimi risultati che sono riuscita ad ottenere in questi tre
anni, grazie al suo continuo supporto e la sua immensa pazienza.
Un grazie va anche ai miei amici, in particolare a Beatrice, Francesca, Giulia,
Martina, Francesco e Matteo, sui quali posso sempre contare e senza i quali non
61
sarei la persona che sono ora. Se sono potuta arrivare fin qui, lo devo anche a tutte
le esperienze che ho condiviso insieme a loro.
Grazie anche ai miei compagni di corso, in particolare Asia, Francesca, Giulia,
Sara e Niccolò, con cui ho condiviso questi tre anni, tra incontri per i progetti,
videochiamate durante la didattica a distanza, risate e disperazioni e per ogni
sessione di esami affrontata insieme.
Infine, vorrei ringraziare tutti coloro che hanno reso possibile questo mio
traguardo, non soltanto amici e parenti, ma anche i professori che con dedizione e
volontà mi hanno trasmesso la passione per lo studio delle loro materie e hanno
contribuito all’arricchimento del mio bagaglio culturale.
62