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NOVA – School Of Business And Economics Retargeting – the case of Landing.jobs
Miguel Pereira, 2592 1
Appendix A – tech professionals (developers, designers and data scientists)
A software developer is someone whose job is centred around the creation of software - these
individuals are also called programmers. Usually, a tech professional knows more than one
programming/markup language, and depending on the type of programming languages and
software those professionals master, they can be classified as web developers (front-end and
back-end developers), data scientists, designers and/or mobile developers.
Front-end developers and designers
Front-end developers work on the so-called ‘client side’ actions – the components of a website
that users will actually see and experience when they use the site or app. “Front-end developers
might work on: the functions of buttons, the layout of pages, the menus and structures of an
application or website, and the organization and user experience within a shopping cart.” (What
The Dev, 2015). A front-end developer should know how to code in at least one of the following
languages: HTML, CSS, jQuery, Javascript, Hive, Postgres, Pig, XML-based languages,
XHTML, HTML5, Angular.js, Node.js, Cofeescript. More recently, some companies expect
front-end developers to have designing skills – since this type of developers work on the visible
site of the digital product, makes sense that apart from the coding skills they also hone image
and video editing skills. In this cases, web designers must know how to work with specific
designing toolkits, like Adobe Illustrator and Adobe Photoshop.
Back-end developers and Data scientists
Back-end developers work on the ‘server side’, to ensure everything runs properly – this type
of developers aren’t concerned with a website’s and/or application appearance and aesthetics,
only that it performs perfectly. “Here are things that a back-end developer might be responsible
for: ensuring that the customer data is saved and organized correctly; keeping a website secure;
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having shipping costs calculate automatically in your shopping cart; scraping search engines
for data; sending out emails automatically from your server.” (Zephoria, 2015). A back-end
developer should know how to code in at least one of the following languages: Perl, C, C++,
SAS, R, Python, SQL, Visual Basic, MySQL, Matlab programming, PHP, Java, Ruby, SQL,
Candle, ASP.Net. Data scientists on their turn, are “part mathematician, part computer scientist
and part trend-spotter” (SAS, 2016; Udacity, 2014). A data scientist’s job is to dive deep into
raw data and analyse it, so that business insights can be withdrawn from its analysis. In order
to do this, the data scientist should have a solid background in mathematics and algorithms, as
well as a good understanding of human behaviours and the nature of the markets and industries
the analysed dataset affect. These tech professionals ideally should be familiarized and know
how to code in languages such as SAS, R or Python, manage their way around with databases
and data visualization and reporting technologies (SAS, 2016).
Mobile developers
This is the type of developers who develop, maintain and optimize mobile applications. In the
digital technology space, mobile refers to “pertaining to or noting a cell phone, usually one with
computing ability, or a portable, wireless computing device used while held in the hand, as in
mobile tablet; mobile PDA; mobile app.” (Dictionary, 2016). Mobile developers can therefore
be of several sub-types, depending on a specific job or task they are assigned to, which means
that they can either be, at the same time, front-end developers and/or back-end developers.
However, usually they are expected to master specific programming languages, such as Swift
or Objective-C (to develop mobile applications for iOS apps).
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Appendix B – LJ’s job curation process and product range
Both the process and product range are from the author of this study, based on Landing.jobs
(2016).
The process
The company has a three-stage process to filter down candidates applying for one of its
client’s job opportunities:
1) Attract – in this stage, LJ focus its attention in raising brand awareness, via employer
branding, content marketing and referral rewards (people who refer a developer are
rewarded a certain amount of cash, paid by the company who launched the job opportunity,
if the person recommended effectively gets the job). LJ also does not accept every
organisation on its platform – all companies that would like to be featured go through a
curation process and only the ones who meet LJ’ requirements enter the company’s client
pool;
2) Evaluate – in this stage, through their own technology and external advisors, the company
uses scoring and smart filters, as well as specific tech challenges for the position at stake
and also making use of its tech curators network to narrow down the potential hiring list;
3) Engage – in the last stage, the company helps its clients who successively hired a candidate
to manage their new employees, providing on-going assistance through smart notifications,
career advisory, in-app messaging and also an applicant tracking system for candidates.
Product range
The company currently offers two products to its customer base:
1) Landing.applications (pay per post) – includes job offers posting and curation, matching
and scoring algorithms, smart filters, smart notifications to candidates and access to LJ’
external tech curators network. In order to purchase this product, clients are required a
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minimum of 150 €, that includes five vetted applications, being able to pay 30 € per
additional vetted application, charged on a monthly basis. According to the company, on
average, 20 applications turn into a hire;
2) Landing.hires (pay per hire) – includes all of the features of the Landing.applications
product and also an account manager, proactive sourcing, pre-screening calls, tech testing
and candidate engagement. LJ charges 11% of the Gross Annual Sallary (GAS) of the
hired candidate or 9% of the GAS if the client pays an upfront retainer of 500 €. LJ also
offers a guarantee in which if the candidate leaves within 90 days from the starting date, a
replacement will be found, free of charge.
Appendix C – How to set up a Facebook campaign
In order to set up a Facebook campaign, the platform demands the advertiser to follow a three-
layer particular structure:
1) Campaign – It is the top layer of the campaign and contains one or more advert sets and
adverts. Every campaign holds one and one only main advertising objective for each
campaign;
2) Advert set – The second layer of the campaign that contains one or more adverts. On
this layer the advertiser can define its targets, budget, schedule, bidding and placement;
3) Advert – The final layer which represents the ad that is actually going to be displayed
and this is where the advertiser can upload an image or video, include call-to-actions,
links and text.
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Appendix D – Facebook audiences
Types of audiences
a) Fans and followers
Every Facebook user who “likes” a Facebook page is called a fan. However, not all fans are
followers of the page. Once someone “likes” a Facebook page, the user automatically becomes
a follower of the page too. A follower is a user who is going to receive notifications from the
page every time there is an update. It is even possible to set a preference on a particular page,
by manually selecting the “See first” option on the newsfeed section – when selecting this
option, every time the page posts something this post will appear on the top of the user’s
newsfeed, thus being in a highlighted position. However, users can “like” a page and not follow
it at the same time – they display interest on the page but do not want to be constantly notified
on the most recent activities. This is quite an important distinction, since the engagement
between a Facebook page and a follower is usually much higher than the one of a user who
only “likes” the page, for obvious reasons (WeSellLikes, 2016).
b) Custom audiences
According to (Facebook, 2016c), custom audiences represent a very effective and efficient way
to target Facebook ads. Through this type of audience, it is possible to create a very high niche
user base from several types of user data, such as lists of email addresses, phone numbers,
website visitors, fan page members and even engagement. There are four types of custom
audiences: standard custom audience, website custom audience, app activity custom audience
and lookalike audience.
1) Standard custom audience
Audience created based on a list of emails, phone numbers, or Facebook User Ids. Then,
Facebook is going to match those users with its own users, which according to Adspresso this
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matching is usually of 60-80% (that is, from all of the users’ contacts imported from the
company’s data warehouse, 60-80% will be accurately match with current Facebook users).
2) Website custom audiences
Website Custom Audiences can be built using the Facebook Pixel on the company’s website.
Using the pixel, it is possible made of every user that has visited a specific page the company’s
website during a set time period of up to 6 months (180 days). Essentially, this is a retargeting
custom audience: a user base of people who have already visited your website and that are still
relevant to the company, there is a chance that by reengaging with these users new conversions
may arise.
3) App activity custom audiences
This is a type of audience made of users who have interacted with a company’s app in some
way (simply opened it, made a purchase, filled out a form, among many other types of
interactions).
4) Lookalike audiences
According to (Facebook Business, 2016), lookalike audiences are a type of audience of people
who have a similar online navigation and action behaviour to your current audiences. Facebook
will look for patterns and characteristics the company’s current users have in common (such as
demographics and interests) and generate from that a much larger group of identical users. This
type of audiences can be created based from several distinct types of user data, like the
company’s Facebook page fan base and frequent website visitors.
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Appendix E – interview guide and answers (summary of both interviews)
TABLE 1 - interview guide and answers (summary of both interviews)
Topics Key insights
1) Retargeting in the case of LJ Yes, it does make sense. Setting up a strategy of this kind for a digital company can cut down costs
dramatically, mainly because it automates some key business processes (in their case, prospecting
for new candidates). Moreover, the entire process and respective results can be continuously
optimized and measured, thus allowing the company to be focused on the core aspects of its
business.
2) Resources This is a very subjective question that depends on a lot of different factors, so there is no absolutely
correct answer. Everything boils down to the marketing objectives, available budget and timeframe
to conduct the tests. One should not spend much money in the beginning – would rather make
small investments on a few posts and get some insights and only later, eventually, start increasing
the spend. Considering the amount of constraints they have, one should try to keep things as simple
as possible, not only on the structure of the posts but also on the way these are analysed and
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optimised. This makes it is easier for them to understand and allowing them to continue with the
current strategy.
3) Strategy, tactics and best practices The most important metrics one should take into account when devising this type of strategy are
1) amount of unique users per time period (usually, months); 2) CTR, CR, CPM and CPC
(depending on the specificities of the campaign); 3) demographics; 4) traffic sources. Both experts
had never used academic literature (for example, scientific papers) in their work and they were
also unaware of anyone in this field who uses it too. There are a lot of companies that often publish
their own studies, which come from their own experience and daily work with their customers,
with insights on industries, practices, as well as regarding any other relevant topics. The most
reputed ones are Kissmetrics, Adroll, MOZ, Hubspot, among others – there are plenty of highly
reliable sources out there, the amount of information is tremendous. These are the ones they go to
whenever they want to get an update on the latest breakthroughs.
Regarding the strategy itself, the experts suggested to focus on simplicity above everything else.
If LJ’s team does not have someone with the required expertise to effectively put a very complex
digital strategy in place and if they are also not planning in having one in the short/medium-run,
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as soon as the author leaves the project all the tests that were ran, as well as the future ones, need
to be as clear as possible for them to understand.
The experts also suggested to start with small tests with a small budget (somewhere between 5-15
€ per post) during two weeks and at the end of those two weeks one should look at the results and
optimize the ads based on those outputs.
In their opinion, this is the best way to go since spending a lot of money on these posts will
probably make the frequency cap go through the roof: the audience is very niche, so if putting a
lot of money into those posts, Facebook will have no alternative than to show the same ad a lot of
times to the same people because there is not more to people to target. Moreover, investing that
much right in the beginning is not an efficient marketing budget allocation if your goal is only to
test some hypotheses. Investing heavily on Facebook makes sense when you want to get actual
results in a short period of time, which is not the case.
I would also not include too many settings in what regards targeting all at once – for example, one
should try instead including gradually different interests to test what works best and what doesn’t.
One should do this because in this way it is easier to test what works best and what does not – by
putting all of them at the same time, it will be harder for you to measure the impact of each variable
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on the performance. Additionally, even though one should be as specific as possible in what comes
to targeting, which is one of the techniques that usually leads to the best possible results, due to
the fact that the audience is niche, including too many specificities may generate an insignificant
amount of users.
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Appendix F – expert profiles
Expert #1
Expert #1 works for one of the largest retailers in Portugal, as a Conversion Specialist, while
doing research on that retailer’s Innovation Lab simultaneously. The expert contacts on a daily
basis with similar problems and using these sort of platforms is part of the expert’s job. The
expert is also a tutor at EDIT, arguably one of the best Digital Marketing Academies in
Portugal, teaching courses on Digital Strategy and how to use specific digital marketing-related
software toolkits. On top of that, the expert also has relevant international experience in
business-related activities: took an Executive Course at the Harvard Business School; holds a
Bachelor’s degree in Management from one of the best Portuguese business schools; took an
Executive Education program from one of the best Portuguese business schools; worked for
Rocket Internet and co-founded one of the largest ecommerce food delivery businesses in Asia.
Considering both the academic and professional background of the expert, it was believed that
the expert would provide relevant and reliable insights on the topic, thus enriching the author’s
knowledge on the subject, leading to a better informed of the problem.
Expert #2
Expert #2 works as a Senior Performance Marketing Manager at the Lisbon offices of one of
the world’s biggest and most important digital marketing agencies. The expert contacts on a
daily basis with similar problems to the one of LJ and using these sort of platforms is part of
the expert’s job. Prior to that, the expert also worked for a smaller advertising agency, as a
Digital Business Manager, and also two of the world’s largest retailers. The expert holds a
Bachelor’s degree in Marketing from one of the best Portuguese school of communication and
media students, currently seeking a Master’s in Management from one of the best Portuguese
business schools. Considering both the academic and professional background of the expert, it
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was believed that the expert would provide relevant and reliable insights on the topic, thus
enriching the author’s knowledge on the subject, leading to a better approach of the problem.
Appendix G – list of abbreviations
The following definitions are from the author of this study, based on Shopify (2016).
CPA – cost per action (investment / total number of actions). An action is often considered a
conversion;
CPM – cost per one thousand impressions (investment / (total number of impressions / 1000));
CPC – cost per click (investment / total number of clicks);
CTR – click through rate (total number of clicks / total number of impressions);
CR – conversion rate (total number of actions / impressions);
Impressions – Designation used for every time an ad is exhibited (printed);
Avg. – abbreviation for the word “average”;
Ad – abbreviation for the word “advertisement”.
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Appendix H – previous Facebook posts from LJ
TABLE 2 – EXAMPLE OF AN AD IN WHICH THE RETARGETING TECHNIQUE WAS NOT USE (AVERAGE PERFORMANCE)
Previous ad #1 - representative of the average performance of LJ's posts (that is, on average, LJ's ads displayed similar results to
this one)
Optimisation Performance
Results (conversions) Cost (per conversion) Budget Periodicity Reach Impressions Clicks Avg. CPM Avg. CPC CTR Frequency cap CR
10 1,00 € 10,00 € Daily 1655 2119 32 4,719 € 0,3125 € 1,51% 1,28 0,60%
TABLE 3 – EXAMPLE OF AN AD IN WHICH THE RETARGETING TECHNIQUE WAS NOT USE (LOW PERFORMER)
Previous ad #2 - low performer (in terms of CR)
Optimisation Performance
Results (conversions) Cost (per conversion) Budget Periodicity Reach Impressions Clicks Avg. CPM Avg. CPC CTR Frequency cap CR
14 1,07 € 15,00 € Daily 3742 4390 77 3,417 € 0,1948 € 1,75% 1,17 0,37%
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TABLE 4 – EXAMPLE OF AN AD IN WHICH THE RETARGETING TECHNIQUE WAS NOT USE (TOP PERFORMER)
Previous ad #3 - top performer (in terms of CR)
Optimisation Performance
Results (conversions) Cost (per conversion) Budget Periodicity Reach Impressions Clicks Avg. CPM Avg. CPC CTR Frequency cap CR
31 0,24 € 7,50 € Daily 1522 2289 51 3,277 € 0,1471 € 2,23% 1,50 2,04%
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Appendix I - Naming Structure – Facebook posts
The naming of the Facebook posts goes according the following structure: “[type of audience]” + “_” + “[desired programing language(s) for the
job opening]” + “_” + “[audience group]” + “_” + “[number of the ad]”. The “number of the ad” field corresponds to its respective order in the set
of ads – the first ad was CustomW_Java_G_1, the second one CustomW_Java_G_2 and CustomW_Java_G_3 the third and final one. This structure
was the one being used by the company, so in order to comply with LJ’ procedures, it was the one used for the subsequent tests:
TABLE 5 – NAMING STRUCTURE OF LJ’S FACEBOOK POSTS
Naming structure – Facebook posts
Types of
audience
Tag Description
Website custom
audience
CustomW_Java_G
Custom (Custom) audience built up from the user data collected by the Facebook pixel, on people that
may be suited for the job opening, due to his/her expertise in Java (Java) development and that have
visited the Landing.jobs website (W, Website Custom Audience) over the last 30 days (G).
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TABLE 6 - CustomW_Java_G_1 (INITIAL SETTINGS)
Sub-period #1 – initial settings
Post
Conversion Budget & schedule Audience Placement Optimisation and delivery
Conversion
event
location
(type)
Daily
budget
Schedule
start
Schedule
end
Custom
audience
Locations Age Gender Languages
Detailed
targeting
Device
types
Optimisation for
advert delivery
Bid
amount
CustomW_Java_G_1
Website or
Messenger
(purchase)
Daily
budget
Nov 1 Nov 14
Visits 30
days No
Conv_1
PT 21-35 M/F
PT, EN1,
EN2
None
Desktop
only
Conversions Automatic
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Appendix J - Facebook posts – settings and respective results
CustomW_Java_G_1
Settings (characteristics of each post):
Conversion type: Purchase – actual application to one of the job opportunities.
Daily budget: 7,50 € (estimated reach of 200-500 Facebook users).
Schedule start: Nov 1.
Schedule end: Nov 14.
Custom audiences: Visits 30 days No Conv_1 – everyone who visited https://landing.jobs in
the last 30 days before the ad started to run, but who did not apply to any job opening (initial
set of users).
Locations: PT (PT stands for Portugal). Rationale: The main target was users who are currently
living in Portugal. Although there may be people outside of Portugal that could be interested
and suited to the job openings, since those are likely very few users, including those prospect
candidates would dramatically increase the size of the audience while reducing exponentially
the accuracy of the targeting at the same time. For example: if users who live in Spain were
included in this audience, its size would increase tremendously. However, it is unlikely that
those targeted Spanish users know how to communicate proficiently in Portuguese, are willing
to come to Portugal to work or even that they would be interested in those particular job
openings. Therefore, even though the size of the audience would become larger, the efficiency
and effectiveness of the ad would be much smaller, especially in what concerns cost per
conversion.
Age: 21-35. Rationale: It is hard to believe that anyone at the age of eighteen already has
relevant professional experience in performing similar roles to the ones of the considered job
openings. Moreover, since a bachelor’s degree in computer science (or any equivalent
bachelor’s degree) takes never less than three years, the minimum age set was twenty-one.
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However, due to the scarcity of tech professionals who hone skills like Java development,
someone coming straight out of university (assuming it completed its studies by twenty-one)
may already be suited for the job. Thirty-five was set as the maximum age, as usually, for this
type of positions, someone who is older than thirty-five occupies a highly senior role in an
organisation and has a minimum of ten years of experience. This fact makes this type of
professionals extremely expensive for the company and for this particular position it was not
necessary someone with that level of expertise. These insights were a suggestion from LJ’s
team, from their own experience.
Gender: M/F (M stands for male, F stands for female).
Languages: PT, EN1, EN2 (PT stands for Portuguese, EN1 stands for English from the United
Kingdom, EN2 stands for English from the United States of America). Rationale: Even though
the position is for a native Portuguese speaker and also to work on the Lisbon offices of the
client, this setting refers to the language of Facebook page set by the user. A lot of Portuguese
users have English as the selected language for both their browsers and Facebook account,
which means that if the language chosen was only PT, the ad would not be displayed to users
with English as the selected language. Furthermore, some users prefer English from the United
Kingdom over English from the United States as the selected language and vice-versa and that
is why both EN1 and EN2 were included.
Detailed targeting: None. Rationale: For the initial post it was considered only the
characteristics of the custom audience, so other settings could be considered later as a result of
the performance outputs of the post to test new hypotheses.
Device types: Desktop only. Rationale: To submit an application, there is a lot of information
that the applicant must provide, including completing the registration on the platform as part of
the process. Therefore, it is fair to assume that users will not do this on their phones – this
insight was also a suggestion from LJ’ team, from their own experience.
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Optimisation for advert delivery: Conversions. Rationale: Facebook will deliver the ad in the
way that maximizes conversions according to its ad delivery algorithms.
Bid amount: Automatic. Rationale: Facebook will automatically adjust the bid amount for
optimal ad delivery (maximum number of clicks for the lowest price possible) via its ad delivery
algorithms.
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TABLE 7 - CustomW_Java_G_1 (RESULTS)
Sub-period #1 - Results
Post
Optimisation Performance
Results (conversions) Cost (per conversion) Budget Periodicity Reach Impressions Clicks Avg. CPM Avg. CPC CTR Frequency cap CR
CustomW_Java_G_1 28 0,27 € 7,50 € Daily 1474 2060 52 3,41 € 0,1442 € 2,52% 1,40 1,9 %
CustomW_Java_G_2
TABLE 8 - CustomW_Java_G_2 (INITIAL SETTINGS)
Sub-period #2 – Interests (new settings)
Post
Conversion Budget & schedule Audience Placement Optimisation and delivery
Conversion
event
location
Daily
budget
Schedule
start
Schedule
end
Custom
audience
Locations Age Gender Languages
Detailed
targeting
Device
types
Optimisation for
advert delivery
Bid
amount
CustomW_Java_G_2
Website or
Messenger
-> Purchase
Daily
budget
Nov 15 Nov 30 Visits 30 days
No Conv_1
PT 21-35 M/F PT, EN1,
EN2
Set of
interests
Desktop
only
Conversions Automatic
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All of the previous settings used in CustomW_Java_G_1 (conversion type, daily budget,
schedule start, schedule end, languages, device types, bid amount, age and optimisation for ad
delivery) were maintained except detailed targeting and custom audience. The new custom
audience was “Visits 30 days No Conv_2” - everyone who visited https://landing.jobs in the
last 30 days but who did not apply to any job opening (counting from the first day the ads using
the custom audience Visits 30 days No Conv_1 started running). In order to increase
granularity and cut down the conversion costs, a set of relevant interests was included in the
targeting section, narrowing down the total size of the target audience – the new target audience
is made of everyone that had already been seen the ad in the previous period and that also was
interested in the set of interests included.
Detailed targeting: Set of interests: “Java”; “Java development”; “Back-end development”.
Rationale: “Java” alone is a bad interest to use, since the word is also the name of an island in
Indonesia. Besides this, Java is one of the most famous programming languages and there are
a lot of people who follow Java-related pages due to its popularity, even if they did not have
any previous contact with coding in this language (or with any programming language at all).
Therefore, some similar terms were included, the ones mentioned. Users who have liked pages
or interacted with ads that are related in some way with those subjects will narrow down the
initial audience, making it more specific, which may lead to a decrease in the conversion costs.
Consequently, the target audience will be made of people who visited https://landing.jobs in
the last 30 days before the ad started to run, did not apply to any job opening (initial set of
users) and also displayed an interest Facebook pages and ads who are in some way related with
the topics “Java”, “Java development” and “Back-end development”.
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TABLE 9 - CustomW_Java_G_2 (RESULTS)
Sub-period #2 – Interests (results)
Post
Optimisation Performance
Results (conversions) Cost (per conversion) Budget Periodicity Reach Impressions Clicks Avg. CPM Avg. CPC CTR (%) Frequency cap CR (%)
CustomW_Java_G_2 19 0,39 € 7,50 € Daily 974 1658 44 4,524 € 0,1705 € 2,65% 1,70 1,95%
CustomW_Java_G_3
TABLE 6 - CustomW_Java_G_3 (INITIAL SETTINGS)
Sub-period #3 – Updated audience and new interests (new settings)
Post
Conversion Budget & schedule Audience Placement Optimisation and delivery
Conversion
event
location
Daily
budget
Schedule
start
Schedule
end
Custom
audience
Locations Age Gender Languages
Detailed
targeting
Device
types
Optimisation for
advert delivery
Bid
amount
CustomW_Java_G_3
Website or
Messenger
-> Purchase
Daily
budget
Dec 1 Dec 15
Visits 30
days No
Conv_2
PT 21-35 M/F PT, EN1,
EN2
New set of
interests
Desktop
only
Conversions Automatic
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All of the previous settings used in CustomW_Java_G_2 (conversion type, daily budget,
schedule start, schedule end, languages, device types, bid amount, age and optimisation for ad
delivery) were maintained except detailed targeting and custom audience. The new custom
audience was “Visits 30 days No Conv_3” – updated group of users who visited
https://landing.jobs in the last 30 days (1 month after Visits 30 days No Conv_1 started running)
but who did not apply to any job opening).
Detailed targeting: set of interests: “IT Jobs”; “IT Jobs in Portugal”; “IT Job opportunities”;
“Oportunidades na área das TI”. Rationale: With this set of interests, the target audience will
be made of people who visited https://landing.jobs in the last 30 days before the ad started to
run, did not apply to any job opening (new set of users) and also displayed an interest Facebook
pages and ads who are in some way related with the topics “IT Jobs”; “IT Jobs in Portugal”;
“IT Job opportunities”; “Oportunidades na área das TI”. I decided to not include in the same
test a mix of that already limited audience (website visitors) with people who display interested
in pages related in some way with “IT Jobs”, “IT Jobs in Portugal”, “IT Job opportunities”,
“Oportunidades na área das TI” and also “Java”, “Java development” and “Back-end
development” because the target audience would then become too niche, thus possibly
eliminating the referral reward effect on attracting people who may know other people who
may be suited for the job.
NOVA – School Of Business And Economics Retargeting – the case of Landing.jobs
Miguel Pereira, 2592 24
TABLE 6 - CustomW_Java_G_3 (RESULTS)
Sub-period #3 – Updated audience and new interests (results)
Post
Optimisation Performance
Results (conversions) Cost (per conversion) Budget Periodicity Reach Impressions Clicks Avg. CPM Avg. CPC CTR (%) Frequency cap CR (%)
CustomW_Java_G_3 21 0,36 € 7,50 € Daily 1210 1777 58 4,221 € 0,1293 € 3,26% 1,47 1,74%