mobile attribution & marketing analytics

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

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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.


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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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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


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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


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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.


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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.


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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.


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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


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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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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


opened and matches it with theIDFA for the specific app

it has stored in its database

start here

OPEN


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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


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.


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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.

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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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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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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


36www.appsflyer.com

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.


37www.appsflyer.com

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.


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


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


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


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


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


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.


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


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.


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


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


48www.appsflyer.com

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.


49www.appsflyer.com

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.


50www.appsflyer.com

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.


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


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


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.


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


55www.appsflyer.com

https://www.linkedin.com/in/shanirosenfelderhttps://www.appsflyer.com/request-a-demo/

www.appsflyer.com

VISIT US AT:

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