ERASMUS UNIVERSITY ROTTERDAM

Erasmus School of Economics Bachelor Thesis International Bachelor Economics & Business Economics

An exploration of the applications of artificial intelligence to digital marketing strategies

Name student: Alexandre F.G. KrugStudent ID Number: 448529Supervisor: Professor VerbekeFinal Version Submitted: 09/08/2019

The views stated in this thesis are those of the author and not necessarily those of Erasmus School of Economics or Erasmus University Rotterdam.

Abstract:

This dissertation discusses novel digital marketing concepts which have revolutionized and

transformed marketing. The most pertinent concept of this dissertation is search engine

optimization (SEO), which consists of methods that when applied, idealizes a website with

respect to search engine algorithms.

When you search for something online, a search engine such as Google produces a search results

page, of websites, in a ranked order. If you are a salesperson, you want your website to rank first

on the results page when someone makes a search related to the product or service you are

offering. The extent to which one is successful in achieving this, depends on one’s ability to

apply the techniques discussed in this dissertation.

The structure of the dissertation is based on ideas, which have been broken down into a

descriptive set of search engine optimizing steps, available to website designers. For that reason,

this dissertation may be perceived as a guide for an entrepreneur who intends to create a digital

marketing platform for a product or service. Included, is a discussion of the risks and

opportunities that together form of the implications of these digital marketing developments.

Abbreviations:

AI: Artificial IntelligenceSEO: Search Engine OptimizationSERP: Search Engine Results Page

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Table of Contents:

Introduction 3Methodology 7Introduction to SEO Concepts & Contextualization of Research: 9

Link-Based Algorithms: 9Changes in the search engine algorithm and its consequences: 10

Image Recognition: 11 Implications of Image Recognition 12Introduction to the different types of searches: 13How Searches Turn Into Leads: 15SEO Techniques: 16

Black Hat Techniques: 19Behavioural Signs: 20Accelerated Mobile Pages: 20Website Security: 21

Website Content: 21Computer Vision and Pattern Recognition: 22Client relationship management: 25Evaluating Success 26Implications of ‘Click Bait’ 29An Innovative Way to Increase Sales 29Conclusion 30Sources Cited 32

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Introduction:

Deep learning consists of artificial neural networks, which learn from large amounts of input

data. It is the basis from which machines solve complicated problems, and is a subset of machine

learning (Marr, 2018). Machine learning is the study of algorithms and statistical models that

computer systems use to effectively perform specific tasks, using inference and patterns as

opposed to explicit instructions (Landman, 2019). It is seen as a subset of artificial intelligence

(AI). AI refers to the ability of a computer program or a machine to think and learn (Oxford

Dictionary). This technological advancement has an increasing number of implications, one of

which is the action of promoting and selling products and services, also known as marketing.

The use of deep learning, machine learning and other forms of artificial intelligence in marketing

fall into the field of study called digital marketing.

One of the fundamental concepts of marketing is customer experience, which consists of the

perceptions or feeling of the customer as a result of their interaction with a company’s system,

channel or products (Ameyo, 2019). The customer experience can be broken down into three

distinguishable elements; the journey, the touchpoints of the brand, and the environment that the

customer experiences (Thompson, 2004). This dissertation will focus on the customer journey,

which can be further broken down into different stages, beginning with a need or desire. This

need or desire transforms into the identification of a product or service which is purchasable and

has the potential to fulfil these desires. This stage may translate into engagement with regards to

the search of selling points of this product or service. A set of criteria is established by the

customer for the product or service. If we consider the digital customer journey as a funnel,

which is a common exemplification used by marketing experts, the process begins with a number

of outlets competing for the attention of the customer.

From the marketers’ point of view, the online customer journey starts when a potential client

arrives on their landing page. A landing page is the first page that a website visitor will see upon

clicking on google adword or a link directory that links them to your website (Unbounce, 2019).

The number of website visitors per unit of time is known as website traffic, and the website

visitor itself is considered to be a lead. There is a pipeline from here on where some of the

visitors will proceed to search elsewhere, and others may have changes in their desires resulting

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in them not furthering their search. Those visitors move away from the website and become

obsolete. The ones that continue to navigate the website will narrow down their search, moving

from the landing page to a more specific position from where they will be able to access the

specific product or service they desire.

This process of narrowing down leads to the second stage of the customer journey pipeline from

the viewpoint of digital marketers. The customers, at this stage of the process, are known as

prospects. The process of transforming a prospect into a client is known as conversion.

Conversion entails the convincing of a prospect to act on their desire, as well as the securing of

funds from the sale of the good or service. The order of the steps in the customer of journey

constitute the order in which they are discussed in this dissertation.

The objective of this paper is to analyze the various implications of AI with respect to the

customer journey of product purchasing. This pipeline procedure constitutes the fundamentals of

structure applied in this literature review. From the aforementioned ideas, the following research

question was formulated:

‘What are the various stages of the customer journey and how can artificial intelligence be

applied in promotional development marketing strategies with respect to these stages?’

I have further broken down this overarching question into the three sub-questions, which

together form the basis for the model upon which my thesis is organized:

‘What are digital search engines and how do they work?’

‘What are the applications of novel technologies with respect to the creation of marketing

content?’

‘What implications do modern marketing strategies involving the use of artificial intelligence

have on human behaviour?’

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Someone marketing a product or service, will want to appear at the top of the results page when

potential customers search for keywords related to the product or service on a search engine. One

of the primary goals of digital marketing is to increase the number of leads. One way to achieve

this is by actively trying to elevate your position in the search results. This process is known as

search engine optimization (SEO). The first step to achieving this is to understand how the

algorithm that ranks the results works. With an ever-growing online marketplace, and

expectations for this trend to continue, the implications of this algorithm are increasing in

importance.

The amount spent on SEO has risen substantially over the last couple of years. According to

Nikolovska, $65.26 billion was spent specifically on the optimization of websites with respect to

search engines in 2016. “This number has risen to $72.02 billion in 2018 and it’s expected to

jump to $79.27 billion by 2020” (Nikolovska, 2019). A majority of this; approximately two

thirds, is spent by startups and small businesses as opposed to large scale companies. The

tremendous amounts being spent on this process demonstrate the importance of SEO and its

potential capability for generating revenue.

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According to the graphic on the previous page, by McKinsey Global Institute 2017. The sectors

that are currently leading in AI adoption are also the ones with the intention to grow their

investments the most (Mckinsey, 2017). The use of AI has allowed these industries to reap the

benefits, furthering their awareness. This has stimulated more investment as the potential

applications are made increasingly evident by its current uses.

Currently the most used search engine worldwide is Google, which began AI search innovation

in 2015. This commenced with the introduction of a learning-based machine algorithm known as

RankBrain (Tilak, 2019). Since then, it has become common practice for ecommerce oriented

platforms, for example Amazon, to incorporate AI into their search engines to enhance the

product searching process. It is used to determine correlations between products which helps the

algorithms to suggest relevant searches, similar products and correct mistakes (Tilak, 2019). This

innovation enhances the process of engagement, part of the customer journey. Other innovations

have implications for additional parts of the customer journey, which will be elaborated on

throughout this dissertation.

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Methodology:

To answer the guiding research questions outlined for my thesis, a search strategy was employed

which makes use of the search algorithm that I decided to conduct research about; PageRank.

Google’s search engine algorithm was used to help me determine the relevance of articles. The

relevance of the articles is based on the frequency of the keywords in my search, as well as the

number of hyperlinks. To search for scientific literature research, Google scholar was the

primary resource. Keywords used during my search included: search engine optimization,

customer experience, artificial intelligence, digital marketing, and marketing innovation.

To assess the credibility of web pages, certain criteria were used for evaluation. Among these

criteria were the number of times that the paper has been cited and the publishing platform of the

article. Additionally, the history of the author of the website or article was investigated. The date

at which the articles were written and published was taken into consideration and an emphasis

was made to review more recently published articles. This was done due to the rapid evolution of

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digital marketing which has seen the various approaches used, such as ‘search engine

optimization’, developed and transformed substantially in recent years.

The articles encountered through the search strategy were categorized based on similarity of

content. This was done for the various branches of study being investigated, which include but

are not limited to: SEO, image recognition, and targeted social media campaigns. Upon grouping

the articles in categories, the most relevant articles to the sub-topics of my thesis were identified

and focused upon. The other relevant articles were used for additional facts and figures to

illustrate and quantify ideas.

For example, research was conducted on the way that the algorithm PageRank categorizes and

sorts data to output the search engine results page. The order of these search results is based on

the relevance score. The relevance score is influenced by the way corporations have utilized

marketing strategies, for instance, they may focus on their web design. Therefore, whilst

conducting research, articles which touch on this particular topic were collated together,

reviewed and the most relevant ones identified and focused on.

In addition to the search strategy discussed above, interviews were conducted with informants;

these are key people who are aware of or have directly experienced the effects of AI such as

Google’s search engine algorithm PageRank, visual search and image recognition. From these

discussions, conclusions were drawn, and the effects of various social media metrics were

evaluated.

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Introduction to SEO Concepts & Contextualization of Research:

Link-Based Algorithms:

The google search engine works on an algorithm called PageRank, named after Larry Page, one

of Google’s founders (Google, 2001). In simplified terms, PageRank works by counting the

number of hyperlinks there are to a webpage, and by assessing the quality of those links. This is

done to determine how relevant a website is. PageRank is the first algorithm that was used by

Google, and is also the best known (Sullivan & Danny, 2007). The assessment of the link quality

creates a numerical weighting to the page, used as a measure of its credibility. The rank value is

an indication of the importance of a page. A hyperlink may be perceived as a vote in support for

the linked website. All hyperlinks and rank values are taken into consideration by the PageRank

algorithm.

Within the factors that affect PageRank’s algorithm ranking system, there are practical

techniques. Amongst these are organic techniques, which fall within the principles and standards

on which the algorithm was formed. There are also practices which deceive the system and thus

falsely influence it. Google and other search engines are big investors into fighting deceitful

websites and documents, with the goal of ignoring or banning them. (Brin & Page, 1998) There

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are many other algorithms which rank web pages on a hyperlink based system. These include but

are not limited to ‘IBM Clever’, the ‘Hummingbird’, ‘TrustRank’ and ‘Hits’, invented by

Kleinberg (Sullivan, 2013).

Google has a variety of platforms from which it generates revenue, one of them is called

AdSense. Adsense is a webmaster platform which allows you to integrate your ads into another

person’s website. Google's spiders crawl the site, which means they navigate it, and they analyze

the content. They will then implement an ad on the site based on what is most relevant,

determined primarily by looking at the use of keywords. The ad revenue, is generated every time

someone clicks on the advertisement, and is divided between Google and the website where the

ad is placed.

Changes in the search engine algorithm and its consequences:

Over time, Google and other search engines update the determinants of their engine’s algorithms.

These changes have various impacts on the businesses who use it. The ice cave touring company

(Goecco) and Icelandic Ice Cave Guides in Iceland is an example of a company that suffered the

consequences of such updates. One of the criteria of a website’s relevance to a keyword, is the

amount of times that a keyword is present on the website. To move up the search engine results,

in addition to the keywords used in the main body, the website designer at Goecco placed

keywords in white text, and put them on a white background. This made the text invisible to

viewers, but detectable to Google’s crawlers. This increased the similarity results of the Goecco

website with that keyword, increasing their search result position.

Google changed the algorithm and implemented a ban on the practice of invisible text. This

means that text in white on a white background, text behind an image, or text with a font 0, were

now considered deceitful. They announced that practicing this keyword matchup technique to

increase relevance score, would be penalized. The penalty would be to be placed on “the black

hat, which means being moved far down the search results page. GOECCO, unaware that this

reform had taken place, left the hidden text on the website and subsequently sufferered the effect

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of the penalty. They only realized this when noticed that they were receiving a lot less traffic

from one moment to another. They figured that they had been moved down several positions on

the search results page.

On the 10th of April 2019, Thornsteinsson (Goecco employee) told me in an interview, “our

website was getting almost no views around March & April. However, that’s when our winter

season ends so we knew it was normal to get less views, but getting almost no views was odd.

We thought our competitors who have big marketing teams and much bigger budgets were just

spending more on keywords. We didn’t realize until early May that we had gotten the black hat. I

think they changed the rules around January, and since the other companies also try to sabotage

their competitors, someone from their marketing teams reported our page”.

Image Recognition:

ASOS is a British online fashion and cosmetics retailer with a revenue in excess of £2.4 billion

in 2018 (about ASOS, 2019). The company was founded in 2000 in London, and is primarily

targeted at young adults. The website sells over 850 brands, as well as its own range of clothing

and accessories, and ships to over 200 countries from fulfilment centers in the UK, US and

Europe (about ASOS, 2019). Recent technological advancements in image recognition have led

to the development of a new visual search tool, ASOS’ style match, that allows shoppers to take

pictures or upload images allowing them to search the ASOS product catalogue for items similar

to those contained within the images. This new technology is revolutionary for the way that

shoppers search for clothing products, and encourages the idea that shoppers should treat

retailers as go-to destinations for items they might see in a magazine or whilst “out and about”.

These tools help customers shop for “the perfect product”, even when they don’t have a complete

vision of what it is (Pinterest, 2015).

For fashion archivists; magazines, pattern cutting guides and autobiographies are their point of

reference when trying to locate where a particular item of clothing may have originated. For

someone less versed and less connected to such resources, AI allows them access to comparable

breadths of information, without having had to study and compile research. This is a game

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changer for the way customers identify products that they wish to purchase and the way these

products are marketed.

Lori Goldberg wrote, “AI based clustering and interpreting of consumer data, paired with profile

information and demographics allows these AI based systems to continually adapt to your likes

and dislikes and react with new recommendations tailored in real-time” (Sentance, 2019). This

has a variety of implications. For example, Sky, a UK telecommunications company, has

implemented a machine learning model that is designed to recommend content according to the

viewer’s mood. Jian Li, the principal data scientist at Sky said: "We have lots of content and a

large database, so how do we use the latest tech to surface that to customers?" (Carey, 2018).

Sky is asking their customers what mood they are in and is using machine learning to make

recommendations based on responses. They are going beyond the simple recommendation

engine for content.

This new technology has a multitude of potential uses. Currently, they are able to adapt their

recommendations in response to the client’s mood. Based on research, the viewer’s mood can be

predicted. For example, if the viewer is watching a football match, and his or her preferred team

loses, the next recommendation will be sorrowful. A solemn themed movie which illustrates the

hardship someone has endured, might help the viewer put the loss into perspective, and feel

better about the situation. This is an example specific to the potential benefit that digital

marketing can have emotionally.

Implications of Image Recognition:

One of the many benefits of the technology of image recognition is the ability to further

investigate the legitimacy of an advertisement. An example of this is how Javier Berbegal, a

student from EUR, used it to avoid a scam when looking for an apartment to rent in Vienna. He

told me in Rotterdam in April that whilst conducting online research, he came across an

apartment that appeared to match his criteria. It was well located, spacious and within his budget.

Skepticism regarding the ad’s legitimacy entered his mind and he decided to investigate further.

He compared the picture of the apartment with search results for that picture on Google.

Subsequently, he found that the picture was of an apartment in the Alps, and was able to

conclude that the advertisement was a scam.

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Another application of the image recognition technology is visual social listening. Image

recognition allows marketers to find uses of brand products and logos that aren’t referred to by

name. This can be done for one’s own brand, creating the ability to identify visual trends,

through the analysis of customer interactions with their brand, logo and product. This process is

commonly referred to as visual social listening.

Revolution of Search Standards:

A way through which technological interactions are being revolutionized is through

modifications of user interfaces. Currently, when making a search, it is common to use a

keyboard to type. However, there is a recent shift towards voice controlled searches. Voice

controlled searches, as well as image based searches are undergoing rapid development. These

systems are going to have an important effect on the effective criteria for SEO. Optimization

with respect to the search engine is done to ensure a high position on the results page for a given

search query. Changing the search query method thus has implications, especially with regards to

image based searches.

Voice control works by detecting what an individual is saying and translating this into written

text. This is similar to what one might have typed. The potential is for the technology to develop

insight into the emotions portrayed in someone's voice and adapt its searches accordingly. This

has already been put into effect to some extent with Google’s “I’m feeling lucky” search button.

This search brings you directly to the first page of the results of your search, with an adaption of

the results based on the emotion portrayed in the way that the button is clicked.

Voice search commands are becoming an integral part of search queries and thus digital

marketing. Smart speaker marketing is one of the major upcoming developments in the field of

digital advertising. The outlining of these revolutions brings us into the discussion of the motives

behind digital searches.

Introduction to the different types of searches:

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Google receives an average of 63,000 searches every second, which per day, amounts to 5.6

billion searches (Aleksandra, 2018). The sheer number of Google searches goes some way to

explaining why someone might use it to increase their website traffic. To effectively market a

product or service through a search engine, it is important to understand the ways that search

engines are used. 39% of all global ecommerce traffic comes from search (Nikolovska, 2019).

Deductively, operating a successful ecommerce platform requires a thorough understanding of

searches. To rank well in the search engine results page, tailored content is necessary, meaning

that the content should be adapted to the people viewing it.

To better understand internet searches, three main categories are distinguished; informational,

navigational, and transactional. Informational searches, are simply searches conducted to obtain

information about something. To further one's understanding, or find instructions such as a

recipe (Bourque, 2012). Navigational searches involve web browsing in search of something

specific, a domain name, website, company or person. The result of the search is identified and

the search is conducted to locate it.

The third type of search is transactional. This is conducted by people searching for something,

often specific, and whom are prepared to make a purchase. Webpages oriented towards these

kinds of searches should focus on the available products, and have links evoking a clear call to

action. Ideally, each website visitor should be provided with a customized experience with

respect to the information available and what it is that s/he is looking for.

43% of consumers do online research while in a store (WordStream, 2016). These searches fall

into the transactional category, as there is motivation for making a purchase. The reason for

which people conduct in store searches is to compare prices and quality between companies. For

a vendor, these searches are an opportunity to sell oneself and to be most visible, it is essential to

appear near the top of the search results page. Search visibility refers to how prominently a piece

of content is displayed on the search engine results page (SERP). “A recent article reveals that

62% of search engine users click only on results that appear on the first SERP and less than 10%

of users click on results that appear after the third page” (Cutts, 2006). These statistics show that

it is not sufficient for a product to be viable and offered at a competitive price. The online

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platform through which the product is sold must appear at or close to the top of the SERP. To

achieve this, it is important to invest in SEO.

Investments in SEO have the potential to generate higher returns than traditional marketing

investments. There are several reasons behind this, one of them being the reach of a digital

marketing campaign. This has been exemplified by the aforementioned number of search queries

that the Google search engine receives. Additionally, a digital marketing campaign has the

potential to be more effective in its targeting. The amount of data generated by companies such

as Google and Facebook makes it possible for advertisers to set specifications to the type of

person that they wish to display their advertisement to. Digital marketing campaigns may also be

more effective than traditional marketing because of their ability to generate data with regards to

returns. This data allows for decisions to be taken, which improve the marketing strategy.

According to the online search marketing company Wordstream, $2.32 is the average payment

for a click on the search network, and $0.58 on the display network (Wordstream, 2016). The

payment is higher on the search network because the clicks are more valuable, as they come

from people who have made a relevant search query. This makes them more likely to make a

related purchase. These quoted numbers represent the monetary value that can be put onto the

click of a hyperlink, directed at a website. The marketer’s willingness to pay this amount serves

as an example of the benefit of turning a potential customer into a lead. Increasing the number of

leads; people visiting your website, is likely to increase the number of sales. To increase the

number of leads, there are a number of techniques that may be applied. It is first of all important

to understand how a search becomes a lead.

How Searches Turn Into Leads:

In order to increase the number of searches that become leads, it is important to appear as one of

the first options on the SERP. To appear as one of the first results, the various dimensions and

entities of SEO must be conformed to. During the first phase of SEO, a list of keywords and/or

phrases are identified and developed, which relate to the content and concept of the website.

These keywords are the terms a user would type into the search engine that would lead to the site

appearing in the SERPs. In addition to developing a list of words and phrases, the SEO

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professional will often determine how competitive each term is and how often each term is used

in a search.

The second phase deals with getting the site indexed by the search engine. This is especially

important for a new website and can be accomplished by submitting the URL directly to the

search engines. URL stand for (Uniform Resource Locator), the address of a World Wide Web

page (Lee, 1994). It can also be accomplished by submitting a site which is already indexed, and

that includes a link to the target site. Black hat methods, which are considered unethical, are

sometimes used to speed up this process.

During the third step, the Webmaster or SEO professional will manipulate various on-page

components, such as page content, meta-tags and elements of site navigation. For example, a

number of researchers, including Malaga, Raisinghani, and Dimitroff have found that sites that

use of Meta tags properly, achieve better results (Malaga, 2008). They also found that sites with

keywords that appear in both the site title and throughout the site’s text, achieve better search

engine rankings than sites that only optimize the title.

Finally, all of the major search engines take into consideration the number and relevance of links

from external sites to the target site. “SEO projects usually include a link building phase (also

called off-page optimization). During this phase optimizers request links from Webmasters and

may use link building programs” (Malaga 2008). This leads to the following subsection which

discusses various SEO techniques in more detail.

SEO Techniques:

A variety of factors are taken into consideration by the algorithms of search engines, and these

are constantly being updated based on the engine team’s ideas of what works best. The specifics

of the algorithm’s relevancy score grading process are reserved to the employees of the search

engines. This is the case, as it is believed that sharing it would diminish the competitiveness of

the firm. Another reason for it being kept secret, is to avoid malevolent practices of people

aiming to abuse these systems for their own benefit. Nevertheless, there are suggested

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breakdowns with respect to the prioritization of features the algorithm considers in creating a

relevancy score. It is also worth noting that the features considered and the weighing of these

features change regularly, with implications as discussed with Thorsteinsson (GECCO

employee).

Whilst the extent to which the different factors are taken into consideration by the search engine

are disputed, certain factors are known to be considered. The most pertinent factors will be

identified in this section, and an explanation provided as to what can be done to optimize the

website with respect to that factor. Strategies and techniques which when employed, optimize the

website for the search engines to boost the website’s position on the search results page for

certain keywords will be elaborated upon.

There are various ways to break down the dimensions of SEO. One way, is by distinguishing two

of the main categories; off page SEO and in-page SEO. This segregation is sensible due to the

fact that on-page content is much more directly influenced by its content creators. Whereas off-

page optimization has the potential to be influenced, but to a much lesser degree due to the fact

that it is fundamentally based on spider crawlers creating links between websites.

On page SEO refers to practices that the creators of the website’s content can utilize. It refers to

the creators making use of the elements that the website is comprised of. This includes creating

detailed metadata for each of the website’s pages, the inclusion of images, and relevant URLs.

URLs can be classified into two types; static and dynamic. A static and unique URL will boost

the website in the search results. A static URL means that the website content stays the same

with respect to the website’s hard code, often HTML. Alternatively, a dynamic URL arises as a

result of the display from a search within a website’s database, without any change in the code.

Consequently, static URLs are favoured by search engines as they can be indexed faster than

dynamic ones (Anton, 2013). Dynamic ones also tend to use less keywords than static ones,

further contributing to “search engine favouritism”.

Other on page factors that will boost the ranking of a website include the use of keywords

throughout the website, particularly in the heading and subheading. Keywords should only be

used where relevant given a penalty for “keyword cramming”. Clean code, is another factor

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which boosts the website’s ranking. HTML stands for Hypertext Markup Language. It is the

standard program for designing documents for web browser display. To use clean code means to

create a code which is robust. This is achieved by not repeating unnecessary code, and keeping

functions small, amongst other things.

Including a name, address, and phone number (NAP) on the website increases relevancy score.

This is a straightforward, yet important part of SEO, due to the fact that search engines such as

Google’s incorporate this data when considering which companies to show for geo-targeted

searches.

Off page SEO refers to techniques which influence the ranking, away from the website’s content.

These techniques revolve around the creation of an online presence for the website. This can be

achieved by creating a social media group, and by including the site in online directories.

Another common off page SEO strategy is link building. In October 2016, Cemper C.C the

founder of LinkDetox; a company that helps websites to search engine optimize, posted on

Twitter “Ranking without links is really, really hard”.

Common BackLink Tools, of which there are many, can be used to boost a website. A common

tool is the recovering of dead back-links to the website. This is a valid strategy if a website has at

some point changed its URL. This can be particularly strategic bearing in mind the frequent

changes in URLs. A dead backlink is a link to a website which directs to missing content,

leading to an error page ‘error 404’.

Obtaining a link from a website linked to by a competitor, is to obtain an indirect from the

competitor. Often, these links are from authoritative sites in your niche, which makes them more

impactful on the search engine results. In March 2016, upon being asked what the most

important features are for optimizing a website for the search algorithm Andrey Lipattsev;

Search Quality Senior Strategist at Google said, “It is content and it’s links pointing to your site”

(Schwartz, 2016).

Amongst the most critical factors of SEO are link signals. Link signals have a variety of

subcategories, one is inbound anchor text. Inbound anchor text refers to “text that is highlighted

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and underlined on a website, that points to another website. For an image, the anchor text is

contained in the alt tag, which is visible in the HTML” (Pear Analytics, 2010). Link signals also

consider the domain authority. Domain authority and page authority work on a score ranked out

of 100, brand new websites begin with a score of 1. Through evaluation of factors; linking of

root domains and the number of total links, the score advances from its initial state. The number

of links is important, as well as the quality of the site which has the link. Domain Authority

works as a relative metric in comparison to the link profiles of the other websites. It is made up

of a sum of the metrics and link data that impact the authority score. In an attempt to improve the

domain authority score of the site, effort should be made to obtain more links from other well

linked to pages.

For back-links to be effective, search engine spiders must visit the website. Blog pinging can be

used to increase the rate at which this happens. It works by establishing a high number of blogs,

often in the hundreds. Then posting a link to the website on each of the blogs. This results in the

creation of hundreds of links to the website. Then comes the pinging, which means to

automatically send messages to a number of servers to inform them that the blog has been

updated. When a page is updated, the spiders of the search engine receive an invitation to check

it out, to identify what has been changed. The spiders then end up on the blog, and through the

blog find the link to the website. It has been shown that this method attracts the search engine

spiders in a few days (Malaga, 2008). Although many people use this technique ethically, it is

sometimes considered to be a black hat method.

Black Hat Techniques:

In SEO ‘Black hat’ techniques refer to anti-competitive methods. Black hat techniques are

utilized by websites to distort rankings or mislead users with respect to related algorithmic search

results (Malaga, 2008). These techniques are often overlooked by smaller enterprises. Some of

this anti-competitive behavior is considered ‘illegal’ by the search engine. Thus, a system has

been set up to penalize engaging in it. There are different kinds of black hat techniques, which

include cloaking, doorway pages, and the use of invisible elements. BMW’s German website

(www.bmw.de) was banned by Google in February of 2006, for using a doorway page (BBC,

2006). Using a doorway page means that they were showing one page to the search engines, and

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a different page to the humans visiting it. This technique essentially creates a difference in the

content of the pages, one which provides certain content only to the spiders, and the other to

actual users, which is often completely different.

A reason for engaging in this behaviour is that the content used to achieve high rankings, may

not be accommodating to good site design and a high conversion rate. To show different content

to the spiders and to other users, it is necessary to monitor the requesting IP addresses. “Since the

IP addresses for most of the major search engine spiders are well known, the optimizer can serve

the appropriate page to the correct spider” (Malaga, 2008).

It is also common for black hat optimizers to use techniques which engage in behaviour, in the

goal of penalizing or banning their competitors. This can be achieved by reporting the

competitor’s website. The most common reports of competitor websites are for keyword or

comment stuffing, invisible content, and google bombing. Google bombing refers to achieving

high levels of ranking for a topic, which is unrelated or irrelevant. It is achieved through heavy

linking. Duplicate content and article spinning are also black hat techniques. Article spinning is a

method for creating new content by rewriting it solely to avoid the issues that arise from

duplicate content.

Behavioural Signs:

Intriguingly, search engine algorithms take into consideration behavioral signs of the website

visitor. The way the individual interacts with the website impacts the relevance and quality score

of the website. These behavioural signs, composed of different website interactions, include the

click through rate. The click through rate is defined as “the proportion of visitors to a web page

who follow a hypertext link to a particular site” (Saleh, 2016). A hypertext link is a “reference to

data that the reader can follow by clicking or tapping. A hyperlink, points to a whole document

or to a specific element within a document.” (Lin, 2016)

Another behavioural sign that is considered by the algorithm is the number of mobile clicks to

call. This refers to the feature called call extension, which is when you include your phone

number on the website and an extension is created which allows viewers to click on the number

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and directly call. The mobile clicks to call is therefore the number of people who use this

extension to contact the phone number. The higher the number of people who do it, the higher

the rank for behavioural signs.

Accelerated Mobile Pages:

Using the Accelerated Mobile Pages (AMP) is a way to boost the webpage’s ranking. AMP is a

software of which the original source code is made freely available. This means it is open source,

and can be both redistributed and modified. It is used to help web pages load faster and

smoother, which boosts the page in the search results ranking. AMP is based on the coding

language HTML which is the standard markup language for creating Web pages. HTML stands

for Hyper Text Markup Language (Refsnes Data, 2019). AMP prioritizes the user experience by

improving the website’s loading speed. It makes the application of HTML to a website a manner

with which to quality score is improved, and thus the ranking of a website on the search engine

results page goes up.

Website Security:

Security is a highly important feature of a successful website and for this reason Google has

made an effort to promote it. To advance the adoption of online security, Google announced in

2014 that it was going to include HTTPS into its relevance score algorithm. HTTPS stands for

HyperText Transfer Protocol Secure. This announcement increased the implementation rate of

HTTPS, which benefited Google as it made the online financial world safer, increasing usage

rates. What HTTPS does is allow transcription of information through a website to be secure. It

is a protocol for communication which encrypts the information shared on a website. It’s a two-

way transcription; bidirectional, which allows for information to be shared between two parties.

These two parties are the website and user, and prevents any third parties from intervening the

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information to steal or tamper with it. This is essential to the conservation of online identities,

secure financial transactions and more.

As of April 2018, over 57% of the world’s top 130,000 websites use HTTPS in a secure way.

Additionally, 27% of the top 10 websites by organic rank use SSL security (SSL Pulse, 2018).

SSL stands for secure sockets layer, and it is an encryption technology for servers and browsers.

The functionality of the security and its use popularity suggest that these can be considered

security standards for websites that want to rank at the top of the search results, particularly those

involving financial transactions, which is assumed in the marketing model.

Website Content:

The website content part of my thesis discusses the aspects the important parts of website content

with respect to optimization for search engines. As well as ways in which AI can be used to

create content for marketing purposes. On page SEO considers elements of the websites design

in order to boost your quality score and ranking on the search engine results page. This part

focuses more specifically on the landing page and navigational patterns. Additionally,

explanations of innovations that allow for the creation of marketing content which you can

display on your website will be provided. The goal of marketing content made through the use of

recent AI innovations is to increase the conversion rate of leads into customers.

The landing page is an important factor regarding the quality score of a website. It is the page

which an individual arrives on when they click a link which directs them to a website. It is

generally the first page of a website that a potential client will come across. It has a strong

influence on the customer’s decision to pursue his product or service search. Consequently, the

search engines consider the landing page experience of the user to be important in determining

rankings. Once a customer has landed a page, things to do include; directing them towards a

purchase, and convince them to stay on the webpage, navigating through it. One way this can be

achieved, is by presenting them with original and eye capturing content. This content can be

created through the use of modern technologies, including the ‘Neural Talking Head Model’ and

the NVidia AI, introduced in the following subsection.

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The aforementioned applications of artificial intelligence in marketing, were in the form of

algorithms which extract information about people. Using that information to extrapolate the

most efficient choice of advertisements to display to that person, in the hope that they make a

purchase. Alternatively, artificial intelligence in marketing can also be used in the creation of the

advertisement, which is then displayed to the person of choice. Until recently, it was common to

take certain quotes out of context to paint a different picture of the person making them. This

could be done with images as well as videos, with careful selection of those chosen to illustrate

the argument in a supportive manner. The technological advancement ‘Few-Shot Adversarial

Learning of Neural Talking Head Models’ takes this idea to a new level.

Computer Vision and Pattern Recognition:

This is a technology which was innovated in 2019 by a group of people including Zakharov,

Shysheya, Burkov and Lempitsky. This technology makes it possible for the computer’s

technology to apply a pattern it has recognized to a new model. This pattern that is being applied

constitutes of face landmarks based on the position which is determined by analyzing a model.

These position-based-landmarks are extracting and then applied off shelf to different people. The

initial video from which the landmarks are extracted serves as an avatar. The landmarks are the

basis of the video created through the talking-head technology which means that the more

training frames are used, the more realistic the created video becomes. What is revolutionary

about this technology is that it functions for new angles which are not shown in the video with

the avatar. This means that it works for angles which are not visible in the original video.

It more technical terms, the technology is based on a meta learning model, which operates on a

large database. It functions with a technology known as VoxCeleb2. (VoxCeleb, 2019). It

operates on three networks, and uses an embedder to match the frames that are linked together in

a chain with the landmarks. The concatenated frames and landmarks are from there transformed

into vectors which contain pose independent information. These vectors are then used to

initialize the parameters of adapted layers into network which works as a generator, which

synthesizes the landmarks into new videos. This is then filtered through a discriminator network,

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which assesses the realism of the newly created video, with a focus on identity preservation.

Additionally, there exists a trainable embedded vector inside the network with tens of millions of

parameters. One of the technology's founders, in his description of the YouTube video “Few

Shot Adversial Learning of Realistic Neural Talking Head Models” Zakharov Egor described it

as “shifting a part of human life-like communication to the virtual and augmented worlds”

(Zakharov, 2019).

There are several conclusions that can be drawn with regards to this technology. It is now

possible to emulate people talking whom it may have been impossible to access. For example, a

person who is difficult to reach, or expensive to hire in the use of an advertisement.

Consequently, it is no longer necessary to hire or contract this person to say something about

your product or service whilst they are being filmed, instead you can use the neural talking head

models adversarial learning technology to recreate that person based on the analysis of the face

landmarks in a video of another person. Additionally, we can conclude that the content of a video

or image is no longer sufficient for it to be deemed legitimate and to be treated as factual. What

we can trust however, are certain sources and companies which do the sorting for us and evaluate

what is truthful and what is not, choosing to display only the non-fake content.

It has been shown that a majority of the websites that rank at the top of the search results for a

given keyword include some kind of image on their landing page. Little information about the

role that image plays in the algorithm exists, therefore we cannot affirm if the images cause the

websites to rank highest or it is merely a correlation. Either way, the trend suggests that a

website with high quality content as judged by Google’s algorithm, includes images. For this

reason, the other technology I have chosen to address is the new software made by NVidia AI,

which turns sketches into realistic images (Burns, 2019). This software is an image creator which

works on generative adversarial networks, allowing users to create sophisticated images from a

small number of clicks. Burns described it as “MS Paint for the AI age.” The software has been

named GauGAN and is designed to create the image in a manner similar to the way in which a

human would paint. The software has three drawing tools; a pen, a pencil and a paint bucket. It’s

a multimodal software which means that identical inputs will result in different outcomes. It runs

on a Tensor computing platform and after a demo was introduced around March 2019, it is

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expected to soon be released commercially. This software is an example of the kind of

technology that we can expect to find increasing applications for.

These technologies may be applied to social media brand positioning, for example Instagram.

Positioning done through social media is often based on the use of cultural intermediaries. A

cultural intermediary is a person, generally an influencer as a result of being on the higher end of

the cultural scope, perhaps a music producer or artist. These people act as mediators between the

consumer and the brand. These people will affiliate themselves with specific other people

through the use of tags.

Instagram can be considered as an image machine; it is a platform which allows blink regimes to

reveal themselves. A blink regime is a person with Instagram fame who constantly take pictures

of themselves to refine the angles at which they look best (Navare, 2019). This brings to light

one of the dark sides of consumption. People get enticed into it as for example the way that it is

often a medium through which celebrity chefs promote their cookware. They do this by

convincing their audience with advertisement. An example of this being done is the Vikas

Kahnna-chef, who posed in front of the golden temple in India for his new movie. His idea was

to play on the notion of being true to your roots, allowing him to create links between his brand

and the people from his origins.

Client relationship management:

Another marketing application of AI relates to the client relationship management of businesses,

which is currently being revolutionized by AI’s applications. Customer relationship management

is a way of dealing with current and prospective customers. This makes it pertinent and closely

linked to the field of marketing. Customer history data is analyzed in a forward-looking

approach, to improve business relationships with clients. There is a focus on retaining customers

and driving sales growth. Expectations are that call centers are expected to be fully automated in

the next decade or so (Busbee, 2019).

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There are a multitude of reasons for wanting to automate this process. There are significant cost

savings, as the labour costs previously spent on employees in the call center desist. Additionally,

there is potential for AI to become better at executing the job. This improvement in function lies

in the ability of the software to rapidly run through a database which contains all of the client’s

previous interactions with the company. Customer history with the company could be known and

used to optimize the interaction between the client and the customer support. Catering to a

person’s demand adequately has always been reserved to behavioural psychology and limited to

human ability. Technological development in this regard is allowing for responses to specific

demands. To the point where it will be able to cater to the individual’s needs better than its

human counterpart. One of the reasons for this, is that having an understanding of the customer’s

history allows for more specific levels of catered service. For example, at a call center, an

artificially intelligent source would be able to instantly access all of your account’s history.

Another important part of client management revolves around data analytics which plays an

important role in business decision making. Text mining is a technology for which marketing

applications have recently been discovered. It consists of a process in which high quality

information is comprised from a piece of text. The quality information derived from the text is

typically done through statistical pattern learning which identifies patterns and trends in the text.

Text mining in marketing has applicability specifically in the analysis of customer relationship

management (Coussement & Van den Poel, 2008). Using text mining to improve the analytical

predictive capabilities, allows for improved customer attrition.

Simply evaluating your customer base through the data analytics provided to you by the software

employed in the marketing strategy provides limited insight. Rather large amounts of data about

the customer base needs to be collected as it allows for more specific catering of marketing

campaigns to the target market. However, this insight does not provide all of the information and

often fails to grasp the whole picture created by the context. This has resulted in the creation of

new fields of study such as computational anthropology. This field of study is relevant to the

situation presented as it is a method of creating a context for the data collected by implementing

domain information where pertinent and including meta data, as well as other factors (Backe,

2016). It is a field of study which combines data computation and analysis with anthropology.

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In data analytics, it has been found to be difficult to conduct courses of action on hard data with

little context. It is important to understand what the data implies to develop marketing strategies

based on the data. This has resulted in the development of the term ‘thick data’ which is a set of

qualitative data which provides additional information into the regular emotions of the customers

(Cook, 2019).

To be able to reach conclusions from data sets, data scientists are going to need new skills to

extract data from databases. These databases include but are not limited to SQL Server, Oracle,

and NoSQL platforms. Another essential ability is to consume web services in a programmatic

manner, parsing data from text files. “The winners and losers in the emerging data economy will

be determined by their data science teams” (Bhatia, 2019). These novel demands have resulted in

the creation of new positions such as that of the User Experience (UX) Specialist role. According

to the Massachusetts Institute of Technology these specialists are “responsible for setting up,

running, and reporting on all aspects of qualitative and quantitative user experience research”’

(MIT, 2015). According to Backe “The goal of computational anthropology is to uncover

systems of knowledge, shared identities, and underlying cultural markers that are wrapped up in

the larger datasets.”

Evaluating Success:

The final stages of the digital marketing process revolve around evaluating the success of the

strategies employed. This is the stage at which point degrees of success are determined with

respect to the various strategies. Knowing this is valuable for making decisions of which

strategies to pursue in the future. The challenging part about evaluating success is that there are

many possible metrics, which can be used to measure this. Therefore, a judgment of what are the

most applicable ones must be made. These must then be selected to effectively evaluate the

individual success of a strategy. Customer journey mapping can be helpful for this, it is “one of

the most widely used tools for service design, it has been utilized as a tool for visualizing

intangible services” (Brown, 2016).

The customer journey mapping process consists of creating a map representative of the journey

that the customer experiences when searching, navigating, and purchasing from a company.

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Doing this helps the sellers to further their understanding of how the various touchpoints are

used. A touchpoint in this context refers to the channels, or alternative forms of communication

that a customer uses to interact with a company. Development in the understanding of the

communication channels allows companies to enhance their design, to optimize the experience

of customer groups. This moves towards the establishment of a competitive advantage, essential

to a successful and sustainable business.

Different metrics are employed at the various stages of the customer journey mapping to evaluate

the success of various parts. The click through rate is a metric, commonly used to evaluate how

effectively an advertisement has been targeted at its market. The click through rate is defined as

the proportion of people who follow a hyperlink, which leads them to a specific webpage, upon

being exposed to an advertisement (Saleh, 2016). This metric is applied to the advertisement

campaigns conducted through Google’s Adwords, as well as Facebook’s paid advertisement. A

higher click through rate suggests a more effective targeting of the advertisement. The high

number of clicks suggests that the people exposed to the advertisement are interested in it.

One of the most commonly used methods for evaluating success is to look at reviews. It is

important to analyze reviews in order to be responsive to the demands of consumers. Successful

marketing strategies are responsive to the demands of the market. One way in which a responsive

strategy can be implemented is through the analysis of customer feedback and reviews. Upon

evaluating them, effective strategies will adapt their product and make changes to their

marketing strategy. In the case of widespread products, it is often the case that there are a high

number of customer reviews for the company to work with. The problem then becomes, how to

interpret such a large number of reviews. The solution to the interpretation of a large amount of

text lies in technology capable of transforming reviews into data, which is interpretable and thus

constructive to the company.

A review can often be interpreted as the story of a customer’s experience with a product or

service. One way you could work to break down a story, is to obtain the most important and

emphasized upon points. This can be done via a code that works its way through the narrative,

and identifies how many times each word comes up. A programming language such as Python,

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which is a multipurpose coding language could achieve this. Upon listing the words that come

up, a list is created, with each of the words that appear, and a count of the number of times they

appear. By downloading a module of a dictionary, a comparison could be made with respect to

common meaning, by looking at synonyms. Doing this would bring together similar words under

one meaningful heading, decreasing the segmentation of the list, making the data more

interpretable. Ideally, contextualization of the words would be considered, and they would be

segmented based on the context in which they are used.

This method could be used to identify keywords, and key meanings of the story at hand. Further

analysis may be conducted, by going through sentences where the keywords come up, to better

understand the context in which they are being provided. This technique would allow the most

meaningful part of a set of narratives to be deduced, without taking the time to read through

them. This method can be applied to a set of reviews regarding a certain product. In a marketing

context, this would provide useful as the most important aspects of a large number of reviews

can be highlighted in a short period of time. This analysis would provide an overview of what is

important to the customer, providing insight into the features and benefits of the product valued

by the customer. Knowing this can be useful to create ads, position messages, and develop new

products. The method discussed above is based on a basic set of programmable codes. It could

and has been developed further, to create a more meaningful insight into the narratives at hand.

One of the issues with transforming a set of reviews into a set of data is that some of the context

is lost.

Implications of ‘Click Bait’:

The implications of clicking on a website or article go beyond simply opening the article to read

on the topic. It makes the clicked-on link more relevant to the algorithms, thus more likely to be

recommended to others. In the case of an article, the marketing of the journalist lies in creating a

‘clickbait’ title, which is a title which incites people to click on it due to the provocation of

emotions it causes. This clickbait title generates a higher number of clicks, meaning that the

article reaches a large number of people. In the case of an article discussing a recent school

shooting this can have impacts far beyond the widespread knowledge of the event itself. The fact

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that this kind of message reaches such a high number of people is arguably what incentivizes

people to commit such an act.

If there is one thing you could do today that would guarantee you fame by tomorrow, it is to

shoot a number of civilians. This is not attractive to most people, but there are a few outcasts,

who often lead meaningless and unfulfilling lives, and doing this guarantees them attention they

would otherwise have no way of accessing. This is an implication of both the click bait article

headings regarding such acts of terror, as well as the implications of people clicking it and giving

it attention. The journalist is simply fulfilling its function of generating articles which people

click on and read. Additionally, the innocent victims of these acts deserve recognition and

grievance, however the cost at which this comes should be considered. My point is that, in a

world, specifically a world wide web, it is important to consider how your actions influence

those of others. It is unknown to many that clicking on a website and navigating it increases its

relevance score and thus makes it more likely for others to be recommended it.

An Innovative Way to Increase Sales:

Alternative applications of technology exist in the marketing sector, with the implication of

increasing sales, as well as improved quality of life. In this context, it makes a lot of sense to

evaluate the applications of these technologies and incorporate them into our systems in ways

that benefit the masses as supported by the following case study.

One way that technology and AI are revolutionizing the supply chain is by creating refutable

identities for products which can be checked, which subdues the possibility for counterfeit

products. A handful of new startups are focusing on tracing medications on blockchain type

technologies, as well as arming consumers with instant information about what they’ve bought.

The counterfeit medicine industry is valued at approximately $200bn annually (Lock, 2019).

This technology thus has the potential to target these counterfeit products and would have a

significant increase in the amount sold of the actual medicine. This has subtler marketing

implications, but is directly related to the sale of non-counterfeit products and thus can be

incorporated into a marketing strategy aimed at increasing sales. This goes beyond simply

increasing for profit sales as it has various ethical implications related to the sale and

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consumption of counterfeit products, most notably in the medical industry where numerous lives

could be saved through the use of the revolutionized supply chain technology.

Conclusion:

This dissertation provides timely and valuable research findings, given how business operations

are becoming increasingly digital. Businesses are meeting their customers online, and so having

the knowledge and skill-set to secure this meeting, as well as to ensure that it is a fruitful,

becomes critically important. Throughout the dissertation, concepts are explored and elaborated

on, providing the reader with insight into the expertise required to be successful in the field of

digital marketing.

As the dissertation explains, the customer pipeline begins with leads and ends with conversions.

In between these stages, is the customer experience which results from the interactions between

customers and companies. This experience is key to every purchase decision. It allows for

strategies to be developed with respect to each step of the experience. Novel technologies have,

for example, facilitated the creation of digital marketing content, such as images and videos. This

content is aimed at improving the customer conversion rate. Various technologies are relevant to

the different customer experience stages. One digital marketing tool relevant throughout the

customer journey is SEO. It does not consist of a straightforward set of steps, but rather a set of

concepts and ideas with various approaches to them.

The most obvious application to marketers of these novel digital technologies, lies in their

potential to increase sales. From the seller or marketers’ point of view, the customer journey

begins when the potential client arrives on their landing page. This is website traffic, which is

made up of website visitors, who are leads. The pipeline filters out some visitors who continue

their search elsewhere or have changes in their desires. Those website visitors move away from

the website and become obsolete. The remaining ones narrow down their search, moving deeper,

from the landing page of the website to a more specific position, from which they will be able to

acquire the specific product or service they desire. This narrowing down of the website leads to

the second stage of the pipeline customer journey from the viewpoint of digital marketers. The

customers at this stage of the process are known as prospects. The process of moving a customer

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from a prospect to a client is known as conversion. Conversion entails the convincing of a

prospect to act on the desire and securing the funds from the sale of the good or service.

This set of steps highlight two of the most important and relevant features of digital marketing. A

primary goal of digital marketing is to increase the number of leads, of which SEO is the

fundamental approach. The second important feature is the conversion rate, the goal here being

to increase it, to increase sales. Conversion rate deals with the type of content you expose

prospective customers to, to convince them to purchase the good or service on offer. These

elements are the basis of discussion throughout this dissertation and form the fundamentals of

digital marketing campaigns.

A profound and historical implication of digital marketing is how power over regulations in the

field of marketing are shifting from nation states to tech companies. Consequently, companies

such as Google hold tremendous power, of which many consumers and businesses are unaware.

In particular small-scale businesses must pay close attention to the regulations which can present

a minefield for them, in managing their daily operations in an increasingly competitive and

complex environment. In short, it is imperative that awareness is increased about the

sophisticated ways in which AI is being applied to leverage digital marketing. In this context, up-

to-date guidance and support is needed for consumers and companies alike, to help them in

navigating their experiences and online engagements. This dissertation provides timely and

critical insight of the state-of the art, today, knowing full well that the landscape may well have

shifted dramatically tomorrow.

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