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MOBILE ATTRIBUTION & MARKETING ANALYTICS
Getting Started with
2016 Edition
Chapter
INTRODUCTION .......................................................................3
THE BASICS OF APP MARKETING ..................................6
2.1 Mapping the Mobile Marketing ecosysteM .................................................. 62.2 coMMon pricing Models .................................................................................102.3 the iMportance of non-organic installs ................................................... 112.4 breaking down Mobile analytics ................................................................ 12
MOBILE ANALYTICS UNDER THE HOOD ....................16
3.1 windows of opportunity ............................................................................... 173.2 attribution Methods how does it work? ............................................ 193.3 the deeplinking dive ......................................................................................263.4 integrated partner ecosysteM .....................................................................273.5 unbiased vs. biased attribution providers ................................................313.6 Mobile retargeting attribution ..................................................................353.7 tv attribution .................................................................................................37
HOW TO SQUEEZE THE DATA LEMON WITH ATTRIBUTION & MARKETING ANALYTICS ............... 38
4.1 in-app events to get started with .......................................................... 404.2 retention - keep users coMing back for More ..................................... 444.3 cohort analysis ............................................................................................. 464.4 one forMula to rule theM all ................................................................. 484.5 reporting - dashboard, pull & push apis .............................................. 494.6 Mobile ad fraud - no longer the elephant in the rooM ................... 50
THE 10 COMMANDMENTS OF MOBILE ADVERTISING ANALYTICS ............................................... 53
CONCLUSION ......................................................................... 54
1
2
3
4
5
6
PageWhats in the Guide
INTRODUCTION
CHAPTER 1
Before the dawn of the mobile era, digital (web) marketers were able
to accurately measure the impact of their campaigns and make smart
decisions about their ad spend. Thanks to the cookie, advertisers were
able to anonymously identify users in the browser, and that completely
redefined traditional spray-and-pray and contextual online advertising.
But then the mobile revolution turned things upside down and the cookie
began to crumble because it wasnt supported on applications and was
deactivated by default on Apples Safari browser. That left the mobile
cookie with extremely limited reach.
IDFA
IDFA
IDFA
IDFA
IDFA
Advertising
ID
AdvertisingID
Getting Started with Mobile Attribution & Marketing Analytics
3www.appsflyer.com
http://www.emarketer.com/Article/2-Billion-Consumers-Worldwide-Smartphones-by-2016/1011694
The problem with mobile is that it is deeply fragmented: There are different
operating systems (iOS, Android, Windows Phone), different environments
(in-app, mobile web) and different identifiers (Apples IDFA, Google
Advertising ID, Google Play Referrer, etc).
Can targeted, personalized advertising work and thrive in a post-cookie
and mobile-first world? The answer is a resounding yes. This guide will
show you how.
The mobile world is deeply fragmented.
Getting Started with Mobile Attribution & Marketing Analytics
4www.appsflyer.com
E R I C S C H M I D T
THE TREND HAS BEEN
MOBILE WAS WINNING.
IT' S NOW WON.
THE BASICS OF APP MARKETING
CHAPTER 2
2.1 Mapping the Mobile Marketing Ecosystem
The mobile ecosystem can be divided into the following main groups:
BUY SIDE SELL SIDE
Brands App Developers
Agencies
Ad Exchanges
DSPs
Publishers
SSPs
ANALYTICS
OTHER
MarketingAnalytics
Independent Attribution
Mobile MarketingAutomation
App Store
Anti-FraudSpecialists
In-App
Ad Networks
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6www.appsflyer.com
Brands. Companies that usually have an established presence on the web and/or offline. They are often classified by verticals such as
retail (like Macy's or Amazon), travel (American Airlines, Hotels.com),
entertainment (HBO, Netflix) and CPG (Coca Cola, Kraft).
App Developers. App owners whose only business is mobile apps (like Supercell, Tinder, Clean Master).
Agencies. A company that creates and manages ad campaigns for multiple advertisers (usually brands).
DSPs. A Demand Side Platform is an advanced software that connects multiple ad exchanges together, automatically making
complex media buying decisions to determine how much to pay
for a single ad impression mostly through a process called real
time bidding (RTB).
Buy Side
Getting Started with Mobile Attribution & Marketing Analytics
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Publishers. A publisher is a mobile website or app that sells ad inventory on its property.
SSPs. A Supply Side Platform helps publishers automatically and
efficiently manage the selling of their inventory to multiple buyers
in order to maximize their inventory yield.
Ad Networks. A company that matches supply from mobile websites and apps to the demand of advertisers. A key industry
player.
Ad Exchanges. An automated marketplace that connects advertisers and publishers to buy & sell mobile ads, mostly in real time.
Connecting the buy side to the sell side
Analytics
Independent Attribution Analytics. Unbiased providers serving as de-facto ecosystem regulators that are able to accurately attribute
credit for an install or a re-engagement to a single source (or multiple
sources in the case of multi-touch attribution). By definition, truly
Independent Attribution Providers are impartial and do not engage
in the buying or selling of mobile ads. This is why they are trusted
both by players on the buy and sell side to measure and report on
campaign performance, and settle reporting discrepancies.
Marketing Analytics. By connecting attribution to in-app activity, marketers can understand which source (network, campaign, publisher
and even creative variation) delivered real value (engagement,
revenue, lifetime value, return on investment). Attribution firms
commonly include marketing analytics in their offering.
Sell Side
Getting Started with Mobile Attribution & Marketing Analytics
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Mobile Marketing Automation. Providers that develop software that simplifies communication with customers and prospects. In
mobile, this involves an ever-growing set of channels such as push
notifications, in-app messaging, wearable device notifications,
and SMS (or MMS).
Anti-Fraud Specialists. Firms that develop advanced data-driven
mechanisms to combat the threat of ad fraud, as it applies to
mobile (particularly install fraud and in-app event fraud).
Other
App Store Analytics. A category divided into (1) the app stores themselves that offer basic analytics on installs, and (2) specialized
companies that provide more advanced analytical tools & data
slicing.
In-App Analytics. Companies that specialize in measuring and analyzing app usage patterns. Such insights can be used to improve
user experience, retention rate, and user lifetime value.
Getting Started with Mobile Attribution & Marketing Analytics
9www.appsflyer.com
2.2 Common Pricing Models
HIGH PERFORMANCE
Cost PerAction
Cost PerClick
Cost PerInstall
Cost PerMille
Payment Terms: Pre-determined price paid for every in-app action defined by advertiser (revenue or engagement related).
Pros: Pure performance model thats connected to users real value actions; adopted by the savviest data-driven advertisers.
Cons: Can be more costly; may hurt reach as networks are more cautious buying media as they assume heightened risk.
Payment Terms: Pre-determined price paid every time a user installs the application on his or her device.
Pros: Performance model and reduced risk; organic uplift following increase in CPI-driven campaign installs.
Cons: Risk of non-transparent networks driving a high volume of low quality or incentivized traffic to drive installs; in hyper-competitive app economy the install KPI is losing ground to engagement and retention-driven KPIs.
Payment Terms: Pre-determined price paid every time a user clicks on an ad
Pros: Easier to analyze user engagement through ad creative A/B testing; primary model used by tech giants Facebook, Google and Twitter.
Cons: "Fat fingers" phenomenon means you risk paying for unintended clicks and damaging your brand name with awful user experiences; higher cost than CPI if you dont have the resources to optimize click-to-conversion path; lack of robust analysis tools; vulnerable to fraud.
Payment Terms: Pre-determined price for every 1,000 impressions (cost per mille mille being the Latin term for one thousand).
Pros: Maximal brand awareness, reach, lower cost.
Cons: Non-performance model; greater chance of fraud and non-transparent networks sending low quality impressions.
LO
W A
DO
PT
ION
HIG
H A
DO
PT
ION
LOW PERFORMANCE
CPA CPI
CPCCPM
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2.3 The Importance of Non-Organic Installs in Todays App Economy
Paid user acquisition: Running campaigns with ad networks, agencies etc.
Owned channel promotion: Running campaigns on a brands owned properties
such as email, SMS, and QR codes - all part of an advertisers overall mobile
marketing mix
Other than increasing your user base, non-organic installs will also increase the
volume of organic installs thanks to improved App Store Optimization (ASO).
ASO is the app store equivalent of SEO as it determines the order of apps
presented to users as they search and explore a store.
App store algorithms use a variety of parameters to determine rankings. These
include title, keywords, visuals, ratings, reviews, editorial review, uninstalls and,
as mentioned above, the actual number of installs an app has.
Ultimately, successful apps grow by investing in marketing and ASO to drive the
highest number of both organic and non-organic installs.
With about 1.5 million apps on both iTunes and Google Play, the chances of
making it on organic installs from app store discovery alone are about as slim
as winning the lottery. Thats why apps need to invest in marketing to drive a
volume of installs, also called non-organic installs.
Non-organic installs can be divided between:
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2.4 Breaking Down Mobile Analytics
The big data era in marketing is here.
Were surrounded by megabytes, gigabytes,
terabytes, petabytes and exabytes of data.
But even all the data in the world isnt worth much,
especially to marketers, if it cant be aggregated
into meaningful patterns that lead to actionable insights.
App-Store Analytics
In-App Analytics
Predictive Analytics
App Store Analytics (App Stores i.e. iTunes, Google Play)
In a NutshellProvides a basic understanding of an apps success, but thats about it.
KPIs Downloads, rankings, device, geography, revenue
What Its Good ForA starting point for beginners that provides anoverview of an apps success. It also delivers thehighest accuracy since its from the source itself.
Successful data-driven marketing on web or mobile lives or dies
on the ability to gather insights and act upon them. And it all comes
alive in the analytics dashboard which has become the digital
marketers best friend.
When we zoom-in on mobile app analytics, were actually looking at
several categories. Of course, these include attribution and marketing
analytics, but this has an entire chapter of its own further ahead so
well briefly outline the others first:
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In-App Analytics
In a NutshellAn extremely important source of data that measures what users are actually doing inside the app and how they are engaging (or not engaging) with it.
KPIsScreens viewed/exited, buttons clicked, time spent per page, levels cleared, purchases made, slicing data by device type & user demographics
What Its Good For
These analytics help you optimize the user experience and improve your retention rate by knowing exactly how your users interact with your app. It can help maximize your users lifetime value and isolate the reasons behind success or failure by mapping your most common conversion and exit paths.
App Store Analytics (Providers i.e. App Annie, Appfigures)
In a NutshellOffers advanced analytics that app stores do not offer mainly comparing your app to the competition and supporting multiple slicers and filters.
KPIsRevenue, downloads, updates, ranks, reviews, rank per keyword
What Its Good For
This category of analytics provides items such as competitive & vertical analyses, in-depth reports (installs, rankings, geographic trends, alerts), and aggregated data per store, which is a must if you have several apps or different app versions.
Getting Started with Mobile Attribution & Marketing Analytics
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Predictive Analytics
In a Nutshell
The up-and-comer on the mobile big data block, these analytics apply big data and machine learning to predict how a single user will react to a specific offer, how much to bid for that user, and which pros-pect to target based on his or her resemblance to your top customers.
KPIs Expected LTV, expected revenue, expected CTR
What Its Good For
These analytics are good for improving retention and lifetime value, conducting look alike modeling for user acquisition, increasing ROI, and driving heightened efficiency via increased bidding accuracy.
Getting Started with Mobile Attribution & Marketing Analytics
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MOBILE ATTRIBUTION ANALYTICS UNDER THE HOOD
CHAPTER 3
Todays mobile environment runs on a last-click attribution model,
yet each advertiser runs with multiple ad networks (some of the big
ones have dozens). In such a reality, theres an undeniable need to
use a trusted source by both networks and advertisers to rule
which network gets credit for reaching a defined goal.
The only way to accurately make it stick is by having a birds eye
view of the click-to-install path across all integrated networks and
informing them in real time, via what is known as a postback, that
they have been credited.
Without this view, each network could bill you for a click that led to
an install, regardless of whether it was the last click or not. What this
means is that different networks can claim credit for the same install.
Proper attribution saves you
money as it prevents you from
being double or triple charged.
attention
marketers
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3.1 Windows of Opportunity
The business side of install attribution is based on pre-determined timeframes,
aka lookback windows. That means a period of time in which a user's action that
precedes an install whether ad click or view counts as having an impact on
the decision to download an app. There are several types of windows:
CLICK-BASED ATTRIBUTION
1) 7-day standard: If a user clicks on an ad served by network x, and then installs the app within 7 days of that click, it would get the credit for that
install - assuming there wasn't another click that followed (under the last
click rule).
2) 24-hour fingerprinting attribution: If there is no device ID or referrer, an install can be attributed based on fingerprinting. Its accuracy level is highly accurate in
the short term and therefore its only open for 24 hours (see page 24 for more).
3) Facebook, Google & Twitter: The three tech giants have their own rules in place: For Facebook and Google, attribution windows are fixed (non-configurable)
at 28 and 30 days, respectively. Twitter enables advertisers to choose between
1/7/14/30/90 days. Because they mostly work on a CPC basis, these media
sources will charge for any click that occurred within their window regardless
if it was the last click or not. Regardless, they are informed by the attribution
provider that there was an install and use that data for their own optimization
purposes.
4) Configurable window: Gives networks and advertisers greater flexibility: networks want the longest possible windows, while advertisers want to have
the ability to normalize their data and compare apples to apples when they run
their analyses. For example, since effective CPI is very much dependent on the
duration of a window, proper comparison is not really possible when comparing
networks with different windows.
A configurable window can also be of use when running campaigns that are
limited in time. For example, a taxi apps 24-hour special campaign promising
installers the first ride for free in such a case it would be of great value to get
reports only on installs that occurred within a 1-day window.
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Some agreements include giving credit to an install based on an ad view. In such
a case the window is short usually up to 24 hours. However, since the click
is mightier than the view, it always wins (assuming it occurred within its own
window of course). Facebook and Twitter will also charge advertisers based on
a 24-hour view-through attribution window.
Let's look at the following example to better illustrate the rules of attribution
windows:
WHICH NETWORK WINS? The answer: Network B
1) Network B had the last click within a lookback window.
2) Click wins over view even if the latter is within its window.
?
Clicknetwork
B
Clicknetwork
A
Network A: 7-day click window
Network B: 15-day click window
Network A: 1-day view window
Viewnetwork
A install
VIEW-THROUGH ATTRIBUTION
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3.2 Attribution Methods How Does it Work?
Despite the fragmentation of the mobile space, cookie-less mobile
measurement has come of age. It relies on advanced technology that
uses a variety of highly accurate identification methods at scale.
When cookies are not available,
there are 3 main methods to
attribute credit using different
identifiers:
Google Play Referrer
ID matching
(Googles Advertising ID or Apples IDFA)
Fingerprinting
attention
marketers
Getting Started with Mobile Attribution & Marketing Analytics
19www.appsflyer.com
1) Google Play Referrer:
A standard and highly reliable and accurate method to attribute
conversions through Google Play (but not Android out of store). It
enables the attribution provider to send tracking parameters to the
store, which then passes them back to the source when the app is
downloaded.
The tracking provider will most likely use the referrer method as it
only depends on itself to create this match - it simply uses publicly
available data from the referral source.
start here
OPEN
Ad
Po
stb
ack
Click(referralparams)
Referrer +unique identifier
sent to store
Attribution provider Generates referrer from
referral params + unique identifier
Google Play
App installedGoogle adds referrer +
identifier to app
First app openProvider SDK reports that the app was
opened and matches it with thereferrer + identifier in provider's database
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2a) ID Matching: Google Advertising ID (GAID)
Another rock solid identifier in terms of accuracy. This device ID is
used to measure Android installs (for Google Play and Android out
of store).
The problem is that advertising IDs do not exist in the mobile web
and even in the app space they are not always available (requires a
users consent and the ad network to support and pass them which
is why the referrer method is vital).
Ad
Po
stb
ack
Click(ad ID)
Attribution providerRecords the device's GAID
for the specific app being advertised
Google Play/Android out of store
App installed First app openProvider SDK reports that the app was
opened and matches it withthe GAID that was recorded via the click
OPEN
start here
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21www.appsflyer.com
2b) ID Matching: Apple's IDFA
Apples highly accurate and privacy-sensitive device identifier that
replaced UDID. The attribution method works in the same way as
with Google Advertising ID.
Ad
Po
stb
ack
Click(IDFA)
Attribution providerRecords IDFA for the
specific app being advertised
App Store
App installed First app openProvider SDK reports that the app was
opened and matches it with theIDFA for the specific app
it has stored in its database
start here
OPEN
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22www.appsflyer.com
4) Fingerprinting:
Fingerprinting, aka device recognition, is an identification method
that uses publicly available parameters (i.e. device name, device
type, OS version, platform, IP address, carrier, to name just a few), to
form a digital fingerprint ID that statistically matches specific device
attributes.
Ad
Po
stb
ack
Click(deviceparams)
Attribution providerGenerates fingerprint ID based on
the specific device parameters
Play/App Store
App installed First app openProvider SDK reports that the app was
opened and matches it with thefingerprint ID in the provider's database
(In
teg
rate
d
Netw
ork
)
OPEN
start here
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4 things you need to know about fingerprinting
As its less straightforward, some information on this method:
1) Fall-back method
Fingerprinting is a probabilistic model and therefore is not 100% accurate. Thats
why its only used when deterministic identifiers like Google Play Referrer and
device identifiers such as IDFA or Google Advertising ID are not available for
attribution (e.g. when the click comes from the mobile web, or when the data
is not passed by an ad network).
2) Highly accurate in short term
The longer the time gap between a click and an install, the greater the chance
that the user would change a setting in his/her device, thereby creating a new
fingerprint (this is especially true with IPs as mobile users constantly change
their location). Thats why the attribution window is short, usually 24 hours. In
app install campaigns, the vast majority of clicks, installs and first app opens
occur within 1-2 hours, in which case the fingerprint is extremely accurate.
3) Used primarily in iOS
As the referrer method is not available in iOS (and almost always available in
Android via Google Play), fingerprinting is used more often on Apple devices.
With only one deterministic option to work with, theres a greater likelihood to
use the fallback method.
4) Anonymous
A mobile fingerprint is privacy compliant and does not include any personally
identifiable information.
Getting Started with Mobile Attribution & Marketing Analytics
24www.appsflyer.com
Multi-identifier logic
FingerprintingFall-back
Android (Google Play)iOS
PrimaryOption
Android out of store
IDFA Referrer GAID GAIDOR
Powerful engines enable attribution companies to choose between available
identifiers in real time, and to do so billions of times a day based on the
following logic:
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3.3 The Deeplinking Dive
In the beginning, the app world was a world of its
own. A walled garden. Links and data did not pass
through apps or between the mobile web and apps.
And that made ad tracking and optimization very
difficult, not to mention the frustrating user experience
it generated when a user clicked on one thing only to
end up at another (or nowhere at all). And thats how
deeplinking was born. Since then innovation kicked
in, offering robust solutions to connect the islands
together through the following elements:
1) Any Environment, One Smart LinkDifferent platforms, environments app stores, channels (including email, push
message, SMS, etc.) can all be configured in a single link which avoids set up
nightmares and many broken links
* Whereas deferred deeplinking has become an industry standard, the ability to
open a specific screen with the right content in real time (or near real time) is a key
aspect that separates the different providers from one another.
2) DeeplinkingUser has app installed, right screen opens
3) Real Time*Deferred Deeplinking
User clickson promotion
If not... or
InstallRight screen opens on 1st launch
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3.4 Integrated Partner Ecosystem
When exploring attribution providers, youll probably run into each one
flexing its muscles by boasting a large number of integrated partners.
What is this and why is it important? There are three main reasons:
Reason #1: Universal SDK.If advertisers want to work with an ad network the attribution provider
is integrated with, they dont have to add its SDK. It all goes through
the tracking companys SDK that sends a postback filled with data
back to the network.
This solves a major headache for marketers (and their developers)
who have a hard time involving their IT to integrate every single
network they want to work with; not to mention the impact on app
performance when running with a universal SDK.
The more integrations an attribution provider has, the easier it becomes
and the more options you have to choose from.
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Reason #2: Optimization.Attribution companies can often work with any media
source and get data they need to attribute an install
through the click. But reporting the conversion
back to the non-integrated network, if indeed it
happened, wont be supported.
Theres no postback. Without it,
the network wont be able to properly
optimize the campaign and will miss out
on key insights.
Reason #3: Stamp of Approval.Running campaigns with integrated
partners has become a standard.
Advertisers will often refuse to run
with networks that are not integrated
with a trusted tracking system simply
because they place their faith in the
latter as the impartial judge who
makes the rulings - which are then
used as the basis for billing.
28www.appsflyer.com
Official Partnerships with the Tech Giants
As dominant forces in the mobile advertising space, Facebook,
Facebook-owned Instagram, Google and Twitter have a market share
of over 50%. They've built robust solutions and more importantly a
powerful position that enables them to manage the data.
In most cases, an advertiser will not use a networks SDK and
measurement, but instead require the network to work with a trusted
mobile attribution company. As such, the network is dependent on
the measurement provider to send the data.
But when it comes to the big players that measure their own campaigns
and are trusted by advertisers to do so, the data travels the opposite
way from the network to the attribution provider.
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http://marketingland.com/wp-content/ml-loads/2015/03/us-mobile-display-ad-marketshare-emarketer-march2015.jpg
In order to control the passage of this data, Facebook, Google (with
AdWords and DoubleClick), and Twitter have selected a very limited
number of measurement partners. All of these partners have gone
through an extensive due diligence process, and adhere to strict rules
(For example, Facebook decided to remove Tune, formerly known as
Mobile App Tracking, from its Mobile Measurement Partner program
because they did not comply with Facebooks data safety rules).
The bottom line is that there are only two ways to measure campaigns
on Facebook, Google and Twitter: to embed their own SDK or work
with their official measurement partners.
Some claim that Facebook campaigns can be measured with deep
links but thats actually a false claim since theres no timestamp on
a deeplink, making last click attribution impossible.
Click here to see Facebooks list of official partners and here for
Twitters (Google doesnt publish the list publicly).
Download the following guide to dive deeper into app measurement
and optimization on Facebook:
How to OptimizeYour FacebookApp Campaigns
GET THE GUIDE
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30www.appsflyer.com
https://facebookmarketingpartners.com/marketing-partners/#ad=&co=&in=&la=&qss=&sp%5B%5D=Measurementhttps://business.twitter.comhttp://get.appsflyer.com/fb-mobile-measurement-guide/
3.5 Unbiased vs. Biased Attribution Providers
The mobile attribution analytics ecosystem can be divided among
the following players:
End-to-end (or multi) solution firms. These companies buy media,
measure campaigns, and optimize them. They are therefore biased
attribution providers. As media companies, their core business
from which they earn most of their revenue involves buying and
selling media. Attribution is therefore a secondary part of the
business.
Attribution-only companies. Providers whose only business is
attribution and are therefore in a position to assume the role of
ecosystem regulators, raising the flag of neutrality, transparency
and reliability. As such, they are unbiased.
MediaCompany
Biased solutions indeed bring value as they
reduce overhead and simplify workflows. Busy as
they are, this appeals to many digital marketers.
However, what theyll probably find less appealing
are the inherent conflicts of interest such a
marriage may bring. Lets explore some of the
major ones you should be aware about.
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Data-sharing.
Attribution analytics providers are big data companies. They collect
petabytes upon petabytes of data about ad campaigns, installs and
in-app events. If a media company owns an attribution solution, it
has a trove of data at its disposal that it could use to optimize and
monetize other campaigns.
An unbiased provider would never ever use
your data or sell it to third-party companies.
Preferred Networks.
A biased attribution provider may try to sway
the advertiser towards other networks on its
list, whether owned or preferred. Integrating
with the network requested by the advertiser
would either drive traffic to a competitor if
the company actually owns a network, or may
lead to a drop in revenues if it runs with a new
partner with whom it has less favorable terms.
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Discrepancies.
When you ask a biased provider to investigate
a discrepancy in attribution data, it may
decide not to invest in such a probe but
rather rule in a way that would best serve
its interests (for example, if it owns a network
it would make a hefty sum if attributing most
of the credit to its own network).
Little or no support.
Advertisers seeking support from a biased
attribution provider may find that if they
are not a major source of revenue, support
of their account would suffer. It simply
wouldnt be economically viable to invest
in such accounts if another part of the
business generates more revenue.
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But wait There's a third type of attribution company: 'traditional' desktop providers
These companies argue that they can offer cross-channel coverage,
but in fact they dont go full circle. Coming from the cookie-based
web, mobile web is familiar territory.
However, since mobile is almost entirely cookie-less, they partner
with cross-device measurement companies that offer probabilistic
identification to connect the dots. Using log-in data can also work,
but its scale is extremely limited. Either way, a complete solution it
is not.
Developing mobile attribution requires a massive investment that
can take years to come to fruition, even for large firms like some of
the desktop companies. Heres a partial list:
Thousands of integrations with media partners
Robust algorithms that can work with multiple identifiers
Ability to support the tracking of billions of in-app events and
connecting them to the attributed sources
Dealing with the fragmented mobile landscape
Developing a deep-linking solution to connect different mobile
environments (a non-issue on the web)
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3.6 Mobile Retargeting
The use of retargeting in mobile has all but become mainstream now.
In fact, the percent of marketers retargeting on mobile jumped from
54% to 82% in 2015, according to AdRoll.
Advances in deep-linking technology certainly helped push retargeting
forward on mobile because it allows a user to be directed to a specific
page thats personalized to fit his/her interests a pair of Adidas
soccer shoes, or an incentive to pass a specific level of a gaming
app that a user abandoned. After all, knowing that your users were
interested in something specific and then directing them to the home
screen of your app would be a complete waste.
Mobile Retargeting Attribution
Mobile retargeting, aka re-engagement attribution,
occurs when an existing user that has the app
installed engages with the retargeting campaign
and opens the app. In this case, matching is done
by deeplinking (which has an attribution provider's
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35www.appsflyer.com
https://www.adroll.com/sites/default/files/resources/pdf/report/AdRoll-State-of-the-Industry-2016.pdf
Re-engagement Attribution Window
The number of days in which an event can be attributed to a retargeting
campaign. It starts when the actual retargeting attribution occurs (after
click and app open), and finishes at the end of the re-engagement
attribution window. The default re-engagement attribution window
is 30 days, but this can be configurable.
Retargeting attribution can get tricky when trying to understand
how it interacts with Install attribution. Lets explore the following
example:
The re-engagement campaign will be credited for re-engaging the
user after he/she clicks and opens the app. When the user goes
on to purchase items, the LTV of both the UA and re-engagement
campaign increases as follows:
UAcampaign
click
30-day re-engagement attribution window
Lifetime install attribution window
installRe-engagement
campaignclick+app open
+3 days$10 Purchase
+10 days$12 Purchase
+32 days$21 Purchase
If you are wondering about duplication, you are right. Thats why any
events that occur within the re-engagement window can be marked
as Primary to enable simple deduplication.
Campaign Event A (+3 days)AttributionEvent B (+10 days)
AttributionEvent C (+32 days)
Attribution LTV
UA True True True $43
Re-engagement True True False $22
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3.7 TV Attribution
TV campaign budgets for apps (not to mention standard brand
advertising) are on the rise. In fact, both Clash of Clans and Game
of War: Fire Age paid a cool $4.5 million to win national attention
in the US during Super Bowl 2015.
The newest form of install attribution is used to measure organic
installs by examining the impact of TV ads on immediate actions in
the app stores. Basically this is done by matching data on the airing
time of each ad with an app install that occurs within a time frame
(in minutes) determined by the advertiser. Post install analytics
such as retention, LTV and Cohort reports are then calculated to
measure effect.
Television is the next attribution frontier. A marketer can already
use a deferred deeplink to the app store, and then launch the app
with a TV-specific promotion in the first screen (i.e. Install the app
and get 1000 free coins, or Install the app and get 10% off your first
purchase). And we havent even mentioned smart TVs.
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HOW TO SQUEEZE THE DATA LEMON WITH ATTRIBUTION & MARKETING ANALYTICS
CHAPTER 4
Now that we understand the ins and outs of how attribution works,
lets turn our attention to the business value of attribution, best
practices and pitfalls to watch out for. Obviously, this topic has to it
many angles but in this introductory guide well focus on the basics.
App marketing is an evolution in progress but the direction is crystal
clear: performance. What started with CPM and CPC is currently
dominated by CPI, and gradually moving to CPA (see page 10 for
more details).
There may be an ad network thats delivering tons of new users, but
when you take a closer look, you realize that theyre low quality. Your
user base may have grown, but many of these users may not have
had any active app sessions nor completed any in-app actions.
A properly attributed campaign
thats tied to in-app events allows
marketers to better optimize
and make informed decisions on
which ad networks to work with.
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38www.appsflyer.com
Volume of installs is obviously very important as it pushes an app
up the ranking in the app stores. But it also serves as the baseline
from which youll find the best users. If its a matter of percentage,
the greater the baseline, the greater the number of loyal users youll
win who will engage and spend. Its simple math.
The key lies in your ability to significantly shoot up the percentage of
loyal users. How so? For one, by effectively managing your ad spend
investing more in media sources and channels that have shown that
they can deliver not only an install but also a loyal user who meets
your goals whether engagement or revenue-related.
So you want to use an attribution provider that not only tells you
where a installs came from, but also more importantl, tells you where
the best install came from. This is done by continuing to follow a
users post-install activity and measuring his in-app events, and then
tying these events back to the acquiring channel/network/campaign/
publisher/creative - depending on how deep you want to go.
Not sure which KPIs to focus on?
Take a look at some examples on the next page.
Multiple Channels
Clicks Install
Connect value back to acquiring channel & optimize
Value-DrivenIn-App Activity
App Stores
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39www.appsflyer.com
4.1 In-App Events to Get Started With:
BASIC
VERTICAL SPECIFIC
Install/loyal user, conversion rate
App opens (for retention)
Revenue
Average Revenue Per User (ARPU)
Number of purchases
Travel
Hotel/flight/package searched/viewed
Add to wishlist
Registration
Initiated checkout
Hotel/flight/package booked
Logged-In
Gaming
Tutorial completion
Facebook registration
Achievement unlocked
Level passed
e-Commerce
Product viewed
Product added to cart
Registration
Product Purchased
Logged-In
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40www.appsflyer.com
Lets take a look at some examples of in-app KPIs from
AppsFlyers own dashboard.
Table 1: Sorted by Number of Installs
If volume is what youre after, Network 1
clearly delivers. But if its post-install value,
it delivers a loyal-user-to-install ratio thats
well below average. When focusing on this
KPI, Networks 5 and 2 reign supreme.
attention
marketers
Getting Started with Mobile Attribution & Marketing Analytics
41www.appsflyer.com
Table 2: Sorted by ARPU (Average Revenue Per User)
This app has a very solid client base with
owned channels Email and SMS leading the
pack. The table also shows that, while user
value delivered by organic traffic is below
average, it delivers the majority of the apps
revenue.
attention
marketers
Getting Started with Mobile Attribution & Marketing Analytics
42www.appsflyer.com
Table 3: Sorted by Purchased Items (Unique Users)
Campaign B of a travel app is obviously
delivering massive scale but when it
comes to the average order value (AOV), it
actually comes in last. Although Campaign
A delivered only 43 purchases, it had the
highest AOV.
attention
marketers
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43www.appsflyer.com
4.2 Retention Keep Users Coming Back for More
Your users have downloaded your app. Thats an important step.
But dont rest on your laurels yet most of the work is still ahead.
Now you have to make sure they open the app, use it regularly,
and actually drive real value for your business.
So how do you get a user to open your app and not the dozens
installed (on average) on his device? Not to mention the thousands
of potentially relevant apps for that user in the app stores just
waiting for their chance to take your place.
User retention is a major topic in app marketing. You can read
more about it here and here. In this guide, we focus on attribution
analytics, and how they can improve your retention rate and
maximize lifetime value.
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44www.appsflyer.com
The retention rate is calculated as the unique number of users acquired
by a specific network who were active on a specific day/week out of
the total number of unique users who first launched the app in the
selected timeframe.
The following chart from the AppsFlyer dashboard clearly shows
that Network 1 has the highest retention rate from the get go, while
Network 2 starts strong and remains strong although not as good
as Network 1. Network 4 has the lowest retention on Day 1 but over
time, it is Network 5 that loses the most users as only 4.8% remain
after 10 days.
Chart 1: Retention Table
Media Source
Network 1100.00%89,095
100.00%34,509
100.00%13,684
100.00%10,595
100.00%9,187
23.62%3,232
31.44%28,012
28.14%9,712
17.92%2,452
11.24%1,191
13.48%1,238
27.64%24,624
24.15%8,335
10.51%1,114
10.55%969
25.19%22,444
21.34%7,364
8.81%933
9.05%831
23.65%21,072
19.53%6,741
11.24%1,538
8.65%916
7.64%702
22.45%20.003
18.60%6,420
9.73%1,332
7.72%818
7.31%672
21.51%19,168
17.12%5,909
8.92%1,221
6.87%728
6.37%585
20.54%18.303
16.16%5,578
7.86%1,076
6.48%687
5.54%509
19.76%17,607
15.30%5,279
7.55%1,033
6.12%648
4.80%441
19.40%17,283
15.05%5,193
7.64%1,045
Network 2
Network 3
Network 4
Network 5
Install Day Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10
Retention Report
11.85%1,621
14.00%1,916
13.15%1,393
18.82%1,729
6.07%643
4.84%445
39.13%34,860
37.78%12,901
39.13%34,860
37.78%12,901
13.15%1.393
18.82%1.729
6.07%643
4.84%445
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45www.appsflyer.com
4.3 Cohort Analysis
A cohort report enables you to group users with common characteristics
and measure specific KPIs over different timeframes.
For example, one cohort can be users who first launched an app any
time during the month of January while another cohort are those
who launched it during February and live in the US. This form of
grouping enables an apples to apples comparison and therefore a
really good indication of change over time. It tells us about the quality
of the average customer and whether its increasing or decreasing
over time.
Lets explore the following example on the next page from AppsFlyers
cohort report. This cohort includes users from Great Britain who
installed the app between January 1 and January 31.
They are then grouped by the media source that acquired them,
which allows us to analyze which network delivered users with the
highest average sessions per user over time.
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46www.appsflyer.com
Chart 2: Cohort Report
Unlike retention, the metric is calculated per different timeframes,
which represent the first X activity days per user, and then accumulated
among all users (thats why the graph points up).
What can we learn from the graph?
Networks 1 and 2 underperform consider removing these
Network 5 growth (purple) is most impressive and constant over
time budget increase can make a lot of sense here
Network 7 (pink) line loses its curve from day 14 meaning
engagement is dropping. Perhaps a retargeting campaign before
day 14 can help maintain the curve in the long run
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
3.00
2.00
1.003 5 7 14
Days301
Se
sio
ns
Pe
r U
ser
Average Sessions per User
Network 1Network 2Network 3
Network 4Network 5Network 6Network 7
3.791.4511.49
10.3212.3210.5710.79
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47www.appsflyer.com
4.4 One Formula to Rule Them All
If we had to sum up a single formula of success in mobile advertising,
this would be it:
The bottom line is that, if your users generate greater value over time
(spend or engagement), than what you invested to acquire them,
youre doing something right!
This formula factors in a lot of what weve discussed: acquiring the
best users in the first place and then maximizing the value of the
existing ones.
LTV > CPIattention
marketers
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4.5 Access to Data With so much good data to use, where and how can you actually
view the data and analyze its patterns? Overall, there are 3 formats:
Analytics Dashboard. View aggregated data or export performance,
aggregated, and user level data to Excel.
Pull API. The advertiser pulls a bulk of data (hourly or daily).
Push API. The provider pushes the data to the advertiser in real
time with information on a specific event.
Lets explore the following marketer personas to better
understand when to use each method:
I use the I use the
I use theI use the
DASHBOARD PULL API
EXPORT FROM THE DASHBOARD PUSH API
I'm a small/medium sized app, with no internal BI system and no need for user level data. I want daily monitoring that provides a quick overview of media source performance.
I'm a medium/large sized app with a high volume of data. I don't have an internal BI system and I don't need organic data.
I'm a small/medium sized app, with no internal BI system. I'm an Excel wizard who likes to go deep by slicing & dicing.
I'm a large sized app withmassive volumes of data.I have an advanced Blsystem and I need organic & non-organic user level data.
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4.6 Mobile Ad Fraud No longer the Elephant in the Room
Wherever there is money, there are bad guys looking for a piece of the
pie. And its a growing piece of the pie with an estimated loss of $1.3
billion annually to mobile fraud (compared to $3.2 billion in desktop
fraud), according to the IAB.
The good news is that there are plenty of ways to fight back. It wont
eliminate fraud entirely but it can definitely minimize it. Also, the subject
is now increasingly raised across the mobile ecosystem, leading to
more collaborations, which is an important step forward in the battle
against fraudsters.
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http://www.iab.com/news/digital-ad-industry-will-gain-8-2-billion-by-eliminating-fraud-and-flaws-in-internet-supply-chain-iab-ey-study-shows/http://www.iab.com/news/digital-ad-industry-will-gain-8-2-billion-by-eliminating-fraud-and-flaws-in-internet-supply-chain-iab-ey-study-shows/
Common Attack Methods
IMPRESSION FRAUD
The shady tactic by which publishers stack multiple display
ads on the same piece of real estate.
CLICK FRAUD
This black-hat technique uses an automated script or a
computer program (aka bots) to imitate a legitimate user that
clicks on ads.
INSTALL FRAUD
With CPI being the most common pricing model in app
marketing, install fraud is also most prevalent. It happens when
Install bots mimic human behavior to automate an install.
IN-APP (EVENT) FRAUD
An attempt to impersonate in-app activity: using an app, playing
a game, or making fake in-app purchases (through a transfer
of virtual goods where no real money is being exchanged).
Generally speaking, the deeper the funnel stage, the harder it is for
the bad guys to succeed in their attacks (see chart below). But since
the financial reward associated with each pricing model is highest at
the bottom of the funnel (CPI and CPA), fraudsters also try harder,
which leads to an increase in fraudulent activity.
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51www.appsflyer.com
Counter attack
Only run with networks you trust: Thankfully, the ecosystem has
hundreds of reputable and established sources of ad inventory.
Demand transparency: Make sure the networks you work with are
transparent about their sources and sub-sources.
Use direct publishers: When you work with direct publishers or with
networks that have relationships with direct publishers, you know
where your ads are running.
Prevent fraud from reaching your dashboard: Automatic IP filtering
based on machine learning algorithms can detect data anomalies in
real time and reject them.
Dive deep into raw data reports: On-going monitoring and in-depth
pattern analysis can detect fraud and block fraudulent sources.
Use in-app receipt validation: With receipt validation, youll know
that real money was spent and that the revenue figure you see in the
dashboard is accurate.
Want to know about mobile ad fraud? Download our in-depth guide
MOBILE AD FRAUDWHAT YOU NEED TO KNOW
DOWNLOAD THE EBOOK
Getting Started with Mobile Attribution & Marketing Analytics
52www.appsflyer.com
http://get.appsflyer.com/mobile-fraud/
THE 10 COMMANDMENTS OF MOBILE ADVERTISING ANALYTICS
CHAPTER 5
Run with an unbiased attribution
provider that you can trust
Tie user acquisition attribution to post- install events that are closely related to your business
goals
Increase ROI by tracking Facebook
and Twitter campaigns
Measure the success of your
retargeting campaigns
Improve user loyalty with
cohort analysis
Use a single attribution deeplink to measure all your campaigns across
all platform
Measure a campaign's impact
on web, cross- device and offline
purchases and engagements
Run campaigns on your attribution
provider's integrated networks to achieve the best
optimization
Integrate in-app events you care
about in the SDK from day 1
Remember that LTV > CPI is the new formula for app success
1
4
7
10
8 9
5 6
2 3
Getting Started with Mobile Attribution & Marketing Analytics
CONCLUSION
CHAPTER 6
The mobile revolution is in full swing. That means the app stores are
packed with numerous competitors of your own app sometimes
even in the hundreds. Making it in this ultra, shark-infested competitive
landscape, beyond having a superior app experience, requires
investment in paid campaigns whether acquisition or retargeting.
Relying on app store discoverability to drive organic installs will not
do the trick. What is required is a mix of organic and non-organic
installs to drive not only a volume of new users but more importantly
a volume of new loyal users who will actually engage with your app.
Also, it is recommended to run retargeting campaigns to better
communicate with existing users and keep them happy and coming
back for more.
An advanced attribution analytics platform can help you reach these
goals by simplifying the fragmented mobile ecosystem and properly
identifying users, while tying attribution to post-install events to
achieve the highest possible ROI on your advertising campaigns.
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54www.appsflyer.com
ABOUT APPSFLYER
AppsFlyer is the leading mobile attribution and marketing analytics
platform, measuring more than $4 billion in mobile ad spend annually.
Over 10,000 app marketers, agencies and brands use our proprietary
solutions to measure and optimize their performance. With over 1,900
integrated ad networks, and as an official measurement partner of
Facebook, Google, and Twitter, AppsFlyer provides marketers with
unbiased attribution, smart deeplinking, mobile campaign analytics,
in-app tracking, lifetime value analysis, ROI and retargeting attribution
for over 800 million installs each month. Clients include Playtika, IHG,
Alibaba, Baidu, Trivago, Macys, Samsung, DeNA, and HBO.
ABOUT THE AUTHOR
Shani Rosenfelder is a senior marketing manager
at AppsFlyer. He has over 10 years of experience
in key content and marketing roles across a variety
of leading online companies and startups. You can
follow him on LinkedIn.
WANT TO LEARN MORE ABOUTMOBILE ATTRIBUTION?
VISIT US
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https://www.linkedin.com/in/shanirosenfelderhttps://www.appsflyer.com/request-a-demo/
www.appsflyer.com
VISIT US AT:
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