the complete guide to measuring mobile marketing ltv and roi

MEASURING MOBILE MARKETING LTV & ROI

The Complete Guide to

September 2016

Chapter

INTRO: THE GOLDEN FORMULA ..................................3

USER LTV .............................................................................5

Monetization ........................................................................................................5

Retention ........................................................................................................... 12

oRganic / ViRality ............................................................................................ 18

10 tips to optiMize youR ltV ......................................................................... 22

USER COST ........................................................................27

cost MeasuReMent ............................................................................................28

6 Media Buying tips foR app adVeRtiseRs...................................................... 30

FINAL THOUGHT ON ROI ............................................. 37

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2

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PageWhat’s in the Guide

INTRODUCTION

CHAPTER 1

App marketing is a daunting task. There are around two million apps both

in the App Store and Google Play, and the app discovery process is largely

broken. In fact, 99% of developers can no longer rely on organic traffic to

grow their app.

Mathieu Nouzareth of music trivia game SongPop, was recently quoted in

TechCrunch with a precise description:

“There are only a few ways you can have your app discovered:

a) You pray and hope that a miracle happens

b) You are featured by Apple

c) You reach out to journalists

d) You take matters in your own hands and you decide

to invest in marketing campaigns.”

Measuring Mobile Marketing LTV & ROI

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And here’s the challenge -- and the catch: a developer cannot rely on organic

installs, but still needs them to be ROI positive…even if the organic multiplier

is as low as 1:0.3 (when 1 non-organic install generates 0.3 organic installs).

Ultimately, it all comes down to one golden formula:

The good news is that mobile measurement has come of age so app

marketers today can track the effectiveness of their campaigns from

A-to-Z. This guide will show you how each component of the formula can

be properly measured and optimized to reach a positive ROI, and beyond.

* Refers to all marketing costs including UA and retargeting** ROI only includes return on marketing spend

User Lifetime Value (LTV) > User Cost* = Positive ROI**

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LTV

CHAPTER 2

Lifetime value is the cornerstone of your marketing budget. You’ll know your

spend cap if you know how much value to expect from your average user.

There are a few ways to measure LTV but regardless of the exact formula,

there are three factors you need to look at: monetization, retention and

organic (virality).

Monetization

App monetization is tough. In fact, 60% of app developers are “poor”.

To avoid the app poverty line, developers must capitalize on any

available source of revenue: in-app purchases, advertising, paid-for

apps, and subscriptions.

In recent years, we’ve seen a clear trend dictated by a freemium-

dominated model: in-app purchases and in-app advertising are climbing,

whereas paid-for app revenue is dropping.

2011 2012 2013* 2014* 2015* 2016* 2017*

Sh

are

of

Reven

ue

100

75

50

25

0

Advertising In-app purchases Paid-for

Sources:Gartner, TechCrunch Statista 2015


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http://www.fiercewireless.com/developer/vision-mobile-app-poverty-line-represents-60-all-developers

Revenue Measurement

In order to be in a position to properly optimize your revenue streams,

it is highly recommended to track every single revenue event with your

measurement partner, and connect it to attribution data so you know

which source is “responsible” for that revenue. But what constitutes

a revenue event and what types of revenue events can be measured?

Let’s break it down.

Standard in-app purchase event: By adding a few lines of code

from your measurement partner into the SDK within your app, you’ll

know when a user made an in-app purchase and which media source

acquired/re-engaged that user. If you want to run an action-driven

CPA campaign, you’ll need to set this up to get proper reporting on

post-install conversions.

It is also important to ensure that your media and service partners

(i.e. analytics, A/B testing, automation) are integrated with your

measurement provider and that this data is sent to them for the

purpose of optimization as well as the ability to use the data in

advanced audience targeting campaigns (with select partners).

Rich purchase events: Keeping your in-app event tracking at a basic

level will only scratch the surface of what you can do with your

data. So instead of measuring only standard revenue events, which

basically tell you whether a purchase was made, you can enrich the

event by adding multiple parameters that will tell you so much more

about who, when, where, what is being bought, for how much and

how often.


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For example:

TRAVEL BOOKING EVENT

GAMING PURCHASE EVENT

BOOK BOOK

PURCHASE PURCHASE

Basic In-App Event Rich In-App Event

BookingBooking

In-App Purchase In-App Purchase

Purchase type (flight)

Class (Business)

Destination (Madrid)

Revenue (499)

Currency (USD)

Customer user ID (1234)

Date (4/29)

Purchase type (extra moves)

Revenue (0.99)

Registration method (FB)

Currency (EUR)

Customer user ID (5678)

Date (4/15)

Rich In-App EventBasic In-App Event


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Custom revenue events for paid apps and subscriptions: If your app is

right for these models, set up a custom revenue event to measure incoming

revenue and understand how successful your strategy is (or isn’t…).

Although the paid model is in decline and the market is dominated by free

apps, charging for your app can work if your content is unique, if your app

is licensed by a top brand and / or if your app operates in an area where

there is more demand than supply.

When it comes to subscriptions, Apple placed this model in the spotlight in

its recent Worldwide Developers Conference, announcing that developers

of all types of apps will be able to monetize through this model. It also said

developers would get a larger cut after one year (increasing from 70% to

85%).

The subscription model makes LTV modeling much more predictable, and

as a result you’ll be able to increase the capacity to spend money profitably

on paid marketing. However, don’t forget that this model works best with

apps that provide ongoing value to users or offer regularly updated features

or content. Consumers don’t want to pay a monthly fee for simple utility

apps like flashlights, currency converters, etc.

Also, remember to dedupe activations from different devices that go

through the same account (in most cases, charges are per account, not

per device).


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Out-of-app revenue events: If a user acquired by a specific media source

went on to make purchases on your brand’s desktop or mobile website,

or even in the physical store, you’ll want to know about it as it will have a

significant impact on that user’s LTV and the LTV of the acquiring source.

This is done by setting up a server-to-server In-App Events API, which

will allow you to automatically send these events to your measurement

partner. The following illustration explain how this works:

User clicks onapp install ad

User installsapp

Attribution company

sends developer

unique identifier

User registersin-app

Developer matches

attribution ID to its own

identifier entered during

registration (email,

loyalty card number)

Out-of-store revenue is connected to acquiring source foraccurate measurement of its value and the user’s LTV

User buys out of app(uses unique ID like

email or loyalty card)

Developer sends

revenue event

to attribution provider


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Ad monetization measurement

Like in-app purchases, the use of in-app advertising as a revenue channel

is growing as developers seek to monetize their own inventory. This

can be done by working with:

Networks/mediation platforms often fall short when it comes to reporting.

The vast majority of them provide analytics on an aggregated level only,

with breakdown by geo and date. That leaves app developers in the dark

as they don’t have data that’s sliced by acquisition source.

Furthermore, without user level data, an app developer won’t be able to

measure ROI properly. If the app made $10k on its inventory during July,

it won’t know which users contributed to this ad revenue, and what was

their acquisition cost.

y Ad networks: Act as intermediates between advertisers and publishers,

and help package and bundle, and sell ad inventory from multiple

apps or media properties to media buyers.

y Mediation platforms: Connect publishers with multiple ad networks

simultaneously while streamlining the integration process. Mediation

enables sellers to set priority for serving ad networks’ demand

depending on their price, geography and other parameters. This

technology is ideal for big publishers with lots of impressions to sell;

but make sure you understand and have control over how different

ad networks are ranked and prioritized.

y Supply-Side Platforms: Technology platforms that are used by

publishers to programmatically sell ad inventory. SSPs connect to ad

exchanges, and may consolidate different sources of demand. SSPs

can be used to sell inventory at scale and optimize fill rate, and may

go hand in hand with a mediation solution.


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With their mission to offer app developers complete transparency and the

deepest insights in real-time or near real-time, some mobile measurement

companies provide deeper analytics, including breakdown by specific

acquisition source and LTV measurement that incorporates ad monetization

revenue. This enables UA managers to optimize their campaigns based on

accurate ROI figures.

In addition, knowing that specific users acquired from a particular media

source generated ad revenue enables app marketers to be far more precise

in their selection of UA sources, while contributing to the optimization of

their own inventory (for example, by excluding users who never engage with

ads, and rewarding the users who produce significant revenue from ads).

Once you have revenue data properly measured from all sources, you can

decide how you want to calculate the average revenue per user: ARPU;

Average Revenue Per Daily Active User (ARPDAU - for daily usage apps);

Average Revenue Per Monthly Active User (ARPMAU - for non-frequent

usage apps). More importantly, with troves of revenue data at your finger

tips, you can optimize what is at the heart of your business.


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Retention

With more options than they can handle, people are using less and less

apps and when they actually use apps they do it less and less often. This

harsh reality holds true for the vast majority of apps.

Since retention is the basis of monetization, maximizing the value of your

users without measuring it will range from difficult to impossible. Tracking

is done by setting up a standard app open event (some providers offer

this built-in their SDK), which will tell you the circumstances under which

users came back to your app, as well as the media source they originally

came from (by extension, you’ll also be able to determine which users did

not come back).


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https://www.statista.com/chart/3835/top-10-app-usage/

https://www.statista.com/chart/3835/top-10-app-usage/

https://www.appsflyer.com/resources/the-state-of-app-marketing-h2-2015/

To put yourself in a position to maximize your retention rates, you’ll need

to measure usage on a granular level. This starts with setting up the app

open event, after which you should:

y Measure more campaigns, GEOs, cities, media sources, publishers,

sub publishers, ad groups, etc. The more you break down your

retention data, the deeper you’ll be able to dive in order to pinpoint

key insights. For example, if a specific geo is underperforming, break

down to city level, locate the poor performers and remove them.

y Define KPI milestones to understand the relationship between

retention and funnel progression (e.g. in gaming: tutorial completion,

registration, level 5 success, level 10 success, in-app purchase; or

in e-commerce: category, product, add-to-cart, purchase). It is

particularly important to know at which point an active user suddenly

becomes inactive so you can encourage them to continue using

your app by offering a special promotion, a discount, etc. And on an

aggregated level, if you’re seeing a significant drop after a certain

stage in your funnel, you probably need to change something in

the app itself.

Category visits

Product visitsAdd-to-cart visits

Purchases


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Define activity cohorts: Cohort analysis tells us if user engagement

is actually improving over time or only appears to be improving

because of growth metrics (with standard retention, the inactivity

of old users can easily be hidden by growth driven by new users).

There are two types of cohorts: acquisition cohorts and activity

cohorts. Acquisition cohorts divide users by when they first started

using your app so you can understand which users leave and when.

But if you want to reach the next level you need to understand

why. For that purpose, there are activity cohorts. These divide

users by actions performed in-app within a given time period. For

example, Install, app open, uninstall, initiated checkout, transaction,

transaction value, or even custom events like “used feature X” or

“did not use feature Y.”

Once again, the more you measure, the deeper you can go in your

analysis and the more options you have to optimize. So when defining

an activity cohort you should filter by specific GEOs, media sources,

campaigns, cohort size, ad groups, etc.

The following chart shows an activity cohort sliced by media source:

2 3 4 5 6 7 8 9 10 11 12

Average Sessions Per User

Network A

Network B

Network C

Network D

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Days1


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Ensure deeplinks are in place so you can properly measure

communication with existing users through owned channels like

email and push notifications (86% of emails are opened on a mobile

device, while push messages improve retention by 2X). This form

of dialogue is a key element in continued usage and good retention

(or lack of it if you’re over-exposing your users).

Deeplinking and deferred deeplinking will also deliver a streamlined

user experience as it’s what users have come to expect. Any glitch

or friction in the experience, and you risk losing users. For example,

users will get frustrated if they click on your email promotion and

get taken to the app store, even though they already have your

app installed. Or if a user clicks a promotional link and has your app

open in the home screen instead of a specific screen relevant to

the content or promotion, you are not meeting user expectations.

How can we prevent these glitches from happening? This is done

by querying the conversion data from your attribution provider so

you know which page to show the user when the app opens (this

may sound trivial, but it certainly isn’t in apps). Conversion data

opens the door to personalization as you can customize the user

experience based on all the information on the tracking link, so

there’s no need for a promo code.


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http://go.kahuna.com/rs/052-HXZ-275/images/Kahuna_Marketing_Index_Q1_2016.pdf

http://go.kahuna.com/rs/052-HXZ-275/images/Kahuna_Marketing_Index_Q1_2016.pdf

Let’s explore the following example of a tracking link:

y Measure retargeting (re-engagement) attribution and post interaction

engagement: With retention rates dwindling, and the understanding

that holding on to your existing users - especially the valuable ones - is

key, the use of retargeting on mobile has almost doubled from 54% to

82%. Measuring mobile retargeting attribution is done by matching the

attribution provider’s parameters on a deeplink, or through a device ID

if a deeplink isn’t available. Once the attribution is registered, any post

interaction engagement is tracked to measure the value of the retargeting

campaign and the total user LTV beyond the initial re-engagement.

Deeplinking technology and the ability to query conversion data can also

turn a retargeting campaign into a personalized retargeting campaign.

http://___.com/id12345678?pid=applift&c=camp12&sub1=15&sub2=

18&dp=scheme://Path

App ID = 12345678

Media source = AppLift

Campaign 12 = Spring Sale

Sub 1 = % of discount

Sub 2 = Category type

Path = Scheme of landing page within app

Based on the configuration determined by the advertiser, this scenario describes

a user clicking on an app install ad for app “12345678” offering a 15% Spring

Sale discount for [category type] running shoes. The user then installs the app

and, on first launch, the app opens in the running shoes category screen with

a 15% discount.


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https://www.adroll.com/resources/guides-and-reports/2016-state-industry

As such, when a user searches for a flight to London and then later

clicks on an ad with a London flight offer, a screen appears displaying

that relevant content when the app opens, ensuring much higher

conversion rates (check out 6 Hacks to Make the Most of Retargeting

on Mobile for more).

y Track uninstalls: When users uninstall an app, it’s much worse than

simply not using it. This can often signal a serious breach in trust or

a poor experience. Tracking high or low uninstall rates to a media

source offers important marketing insights. In addition, knowing when

and why users uninstall apps is another KPI you need to measure,

so make sure your attribution provider’s SDK includes this ability.


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https://www.appsflyer.com/resources/6-hacks-to-make-the-most-of-retargeting-on-mobile-mobile-story/

https://www.appsflyer.com/resources/6-hacks-to-make-the-most-of-retargeting-on-mobile-mobile-story/

Organic (virality)

In the early days, paid acquisition was primarily used to boost the

number of organic installs through burst campaigns. This was possible

because the number of installs had a significant weight in the app

store’s discovery formula. But in the last couple of years, this factor is

far less important, while quality-driven parameters like ratings, reviews,

usage, and uninstalls are pulling more weight.

As a result, the organic multiplier (the number of organic installs a

developer can expect from a non-organic install) is well below what

it used to be a mere two years ago (0.2-0.4 is not an uncommon

occurrence nowadays). To make matters worse, with 1.5-2 million apps

in Google Play and Apple’s App Stores, app discovery has become

largely broken. As such, non-organic app installs, especially by valuable

users, are now a vital part of the mix on their own, and not simply a

tool to drive organic traffic.

Despite the diminishing prospects of getting organic traffic, you still

need it to be ROI positive. Even if the multiplier is at a low point, you

must optimize towards getting as many organic users as possible. With

CPI rates ranging from $1-$4, it’s extremely unlikely that the LTV of the

average user can surpass that, so you need to get those “free” users to

reduce your Effective CPI (eCPI).


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Here’s what you need to do to properly and accurately measure organic

data so that you can be in a position to maximize its potential:

y Work with Facebook and Twitter Measurement Partners: Without a

Mobile Measurement Partner (MMP), you won’t be able to separate

organic from non-organic traffic on these social networks. Instead,

you’ll see non-organic installs as organic ones. Being able to accurately

identify organic users coming from two of the top media sources

in the ecosystem is an absolute must. Beyond that, an MMP will

allow you to:

y Measure organic traffic from Twitter without a tracking link so if

users share an app store link with their friends, it can be tracked

y Calculate Twitter and Facebook’s organic multiplier so you know

how many organic users a paid social user can generate

y Automatically configure advanced audience campaigns for your

organic users to maximize their potential. For example, Custom

Audiences to keep them engaged, and lookalike targeting to find

more quality users like them

y Utilize rich in-app events to granularly measure what your organic

users are doing in your app


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y Set up deeplinking to properly track user invites: Know which

GEOs, platforms, channels, and types of promotions deliver the

highest number of invites (and which do not).

http://___.com/id88888888?pid=applovin&c=campA1&sub1=987

654321&sub2=US&sub3=SMS//Path

App ID = 88888888

Media source = AppLovin

Campaign = A1

Sub 1 = user ID

Sub 2 = Geo

Sub 3 = Channel

Path = Scheme of landing page within app

Based on the configuration determined by the advertiser, this link

will inform an advertiser that a user was acquired through campaign

A1 (April Friend Connect), that it happened in the US via the SMS

channel, and that the invite was sent by customer “987654321” so

the advertiser can reward him/her with a $10 gift card for their next

purchase.

For example:

y Make sure you gain access to organic raw data reports so you can

slice and dice the numbers at any level to find key insights about

your organic users

y Track keywords that drove organic installs from Google search and

Google Play to gain valuable insights that can inform your keyword

optimization


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y Test multiple variations of your app store page with different

keywords, descriptions, titles, images, videos etc. to find the top

converting version/s

y Define and track organic cohorts to understand what characterizes

organic users and what kind of activities they perform in-app and

under which circumstances


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10 Tips to Optimize Your LTV

1. Go beyond last click attribution: Just because a source did not deliver

the last click does not mean it does not have value! On the contrary,

understanding the impact of previous touchpoints will change your

ROI calculations. After all, for users to convert they have to be driven

down the funnel. So if a media source delivered 125 so-called “install

assists” and 75 installs as the last click, the return from that media

source is obviously higher than the 75 installs.

User is exposed to an ad for the first time on network X

User clicks on an ad but doesn't install the app

User clicks on a similar ad for the same app but still doesn't install it

FIRST ASSIST

SECOND ASSIST

THIRD ASSIST

User searches for the app, clicks on a text ad and completes the installation

LAST TOUCH - GOAL!

2. Set Up An API: Setting up a push API is the best way to make the

most of data at scale. Practically all big players and those who are big

on data rely on a push API that feeds data into their internal BI systems

in real time so they can make rapid changes in their campaigns.


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3. Separate UA from retargeting in LTV calculation: Understanding

the effectiveness of each activity offers important insights.

The following example best illustrates this point:

In this case, credit will be given to the re-engagement campaign after

the user clicks and opens the app. But when it comes to LTV calculation,

there is a clear distinction between UA and re-engagement:

To prevent duplication, make sure any events that occur within the

re-engagement window are marked as ‘Primary’ to enable simple de-

duplication.

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

Campaign Event A (+3 days)Attribution

Event B (+10 days)Attribution

Event C (+32 days)Attribution LTV

UA True True True $43

Re-engagement True True False $22


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4) Ensure best possible fraud protection: Whether your own safeguards,

those of a specialist anti-fraud provider and / or your measurement

company, you’ll need to have the right tools in place to minimize fraud.

Without them, your LTV calculations can be significantly tainted. This

includes prevention mechanisms (such as active IP filtering), detection

tools (centered on access to raw data to track anomalies) and in-app

receipt validation to remove illegitimate purchases as they happen. On

a different note, only run campaigns with media sources you trust, and

demand complete transparency from these partners including a site-

level drill-down. (For more on fraud, check out our Mobile Ad Fraud:

What You Need to Know guide.)

5) Set frequency caps based on user type: Don’t show more than a

couple of ads per day to your paying and other quality users. Instead,

show the non-paying and low LTV users more ads per day. In any case,

track your exposure carefully to find the ideal number of ads to serve

to each segment and never overexpose.

6) Pass metadata such as gender, age, zip code, latitude/longitude,

income and any other data point that your users have clearly agreed

to share. Your app will often be eligible for more campaigns if you pass

available metadata with each ad call. This data will be used to connect

your users with relevant advertising based on the data they choose to

share with you. The greater the relevancy, the higher the eCPM, and

that means more money for you.

Also remember that it’s important to look at eCPM and fill rate. If an

ad network has a really high eCPM but a low fill rate, it may make you

less money overall compared to an ad network with a lower eCPM, but

higher fill rate (assuming impressions are equal).


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http://hub.appsflyer.com/hubfs/Mobile_Fraud_eBook.pdf

http://hub.appsflyer.com/hubfs/Mobile_Fraud_eBook.pdf

7) Minimize discrepancies to ensure accurate LTV calculations:

Since different platforms use different timezones, discrepancies happen

- especially if data is sliced by hour or day. What often happens is that

data tracked by one platform spills over to a previous or following day

or hour in another platform. For example, let’s say platform A records

timestamps based on a GMT timezone, while platform B is set to a

PST timezone. In such a case, data from 12:00am-6:59am GMT will be

marked as 17:00pm-11:59pm in PST – the previous day!

If alignment between different platforms cannot be achieved, it is

recommended to use a wider time frame (but not too wide) so the

hourly difference between timezones becomes negligible – two weeks

can be good (click here for more examples of discrepancies and how

to minimize them).

8) Use a centralized ROI dashboard: With all your ROI data in one

place, you will be able to compare apples to apples and avoid trying

to consolidate data in excels from multiple sources - which only

cause multiple headaches as a best case scenario, and errors in ROI

calculations in the worst case scenario. In addition, when your ROI

data is centralized under the roof of your attribution partner, you’ll

know that cost is only recorded after an install is driven by a network

with the last click (if running on a CPI model). A network’s dashboard

will record cost upon install, even though it may have not delivered

the last click.


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https://www.appsflyer.com/blog/a-recipe-for-discrepancy/

9) Leverage the power of your CRM data to run smarter re-engagement

campaigns targeting your existing or dormant users. Your 1st-party

data can also boost the number of organic installs by telling you exactly

which rewards / messages best resonate with which type of users so

that you can better optimize your user referral program. After all, your

best customers are helping you drive leads and acquire customers.

Knowing who is referring new business to your app, tracking their

referral activities, rewarding them, and forecasting LTV with referral

activity makes your CRM a mighty revenue driver.

10) Measure Properly! At the risk of repeating ourselves, put yourself

in a position to have a trove of granular revenue-related data that you

can analyze and act upon to boost your average user’s LTV (look no

further than this guide’s previous section for much more).


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USER COST

CHAPTER 3

The part where money pours out of your pockets plays to a very different

tune than when money comes into your pocket. So far, we’ve discussed the

latter with revenue measurement and optimization. Now, we’ll focus on an

equally important part of the equation: the measurement of cost and how

to be as efficient as possible in order to boost your ROI.

Banner $1 CPMNative $2.5 CPM

Search ad $2 CPMVideo $3 CPM


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Cost Measurement

There are three main methods to monitor your cost data: manually on

your own excel dashboard, on your media partner’s dashboard, or the

dashboard/API of your mobile measurement partner.

Whatever the method, it is important to properly define the business

model and different rates you agreed on with your media partner, including

breakdown by geo, quality of inventory (incentivized vs. non-incentivized,

premium vs. non-premium). Don’t forget to continuously monitor that

you are billed accordingly.

Other considerations to take into account include:

y Breakdown of cost: Don’t settle for getting cost data per campaign;

instead demand access to cost data by ad group, creative variation,

and also by geo and publisher. The greater the granularity, the more

data you’ll have to work with for optimization.

y Tracking cost in your measurement partner’s dashboard: It is highly

recommended to have all your cost data in a single dashboard. This

will enable a proper “apples to apples” comparison, and ultimately a

proper measurement of your effective cost across different partners,

channels, campaigns, etc. It will also dramatically reduce the risk of

error when trying to consolidate it all on your own.


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y Passage and tracking: Demand your media partner to pass cost

data to your measurement provider. Make sure that the former can

dynamically pass different cost parameters like pricing model, value

and currency on the link (or API if supported), and that the latter

can properly track this information, and record it as money spent if

the media source in question is credited for the install or post-install

conversion (in CPI and CPA campaigns, respectively).

y Cost data from Facebook and Google: The top two networks in

mobile advertising require a unique integration through an API to

pass cost data to their official measurement partners.


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6 Media Buying Tips for App Advertisers

In a crowded marketplace, many mobile media companies have significant

overlap in their offering to the market. While some consolidation has occurred

in recent years, the mobile app ecosystem will still lack standardization,

transparency, and accountability for the foreseeable future. Here are

some tips to help you navigate the jungle of today’s mobile media buying

environment:

1) Understand the mobile ad value chain: It’s not uncommon for providers

to fulfill – or appear to fulfill – various roles both on the demand and

supply side. That creates all sorts of problems, especially since a lack

of standardization in terminology and technology can make it hard

to understand who exactly does what. For example, an app publisher

may offer ad inventory directly through an in-house sales team, and a

supply-side platform may be connected to an entity that sells media to

advertisers. For this reason, it is more important than ever for marketers

to get a thorough understanding of the specific services their potential

user acquisition partners offer, their affiliations with other parties, and

their business model.

Advertiser Agency/Trading Desk

Demand-SidePlatform

Data ManagementPlatform

Ad Exchanges

Supply-SidePlatform

MeditationPlatform

Ad Networks

Publishers

DEMAND SUPPLY


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2) Balance pricing models that reflect your UA goals with scale:

While significant progress has been made in evolving from the traditional

cost-per-mille (CPM) pricing for digital ads, focusing on pricing models

that truly reflect their UA goals can be a challenge for app advertisers. It

is clear that for most advertisers, the install is secondary in importance

to a relevant in-app action – like adding an item to a shopping cart or

creating an account. This means that in most cases, cost-per-action

(CPA) pricing can be seen as the superior model for value-driven UA.

Nonetheless, cost-per-install (CPI) remains the dominant pricing option

for user acquisition campaigns.

There are multiple reasons for this: Most importantly, there are fewer supply

sources that support CPA pricing compared to other models. Because

there are so many takes on what defines a qualifying user action, it’s very

difficult to compare or predict CPA performance.

Even setting this aside, if the action is located deep in the user funnel, it

can seriously hurt the chances of an ad winning an auction, even with a

very high CPA cost as the conversion rates are low. This is especially true

in the programmatic world, where most auctions are based on a CPM or

cost-per-view (CPV) model. In these settings, the conversion into CPI or

CPA-based pricing may be done by demand-side-platforms, which then

assume the risk of not meeting their conversion goals.

For advertisers, this means that they may have to combine multiple pricing

models to achieve significant reach and scale with their campaigns, and

then monitor and compare the quality of the incoming traffic against their

actual conversion goals using their mobile attribution tracking provider.


31www.appsflyer.com

3) Get a realistic outlook on campaign reach and targeting: Working with

as few supply partners as possible makes for a simpler media buy. This is

why many ad networks make audience reach numbers a key component

of their sales pitch. While scale is certainly a factor to consider, you should

be wary of inflated projections for how many relevant users an ad network

can reach for your specific app.

The ubiquity of ad networks and the competition for advertising dollars

has created a situation where publishers usually work with multiple supply

sources to maximize their ad revenue. For advertisers, this means that there

can be a significant overlap in audiences between different ad networks.

It may also mean that, while an ad network is technically integrated with

a specific publisher, it cannot guarantee that it can actually place ads in

their app.

That’s why it’s a good idea to inquire directly with the ad networks about

the performance of past campaigns for apps similar to yours. You should

also try to get a good understanding of what individual apps are included in

the media plan offered to you, and how much traffic each app is delivering

once your campaign is running. This helps you assess whether your ads

are seen in the right apps, and at the scale you expect.

December 2015 Mobile Media Plan


32www.appsflyer.com

4) Allocate resources to monitor your media buys: Weighing the desired

level of control over your mobile user acquisition strategy versus the

associated cost is a major consideration for any app advertiser. While

mobile-first advertisers tend to dedicate in-house resources and build up

a highly specialized UA team, working with a mobile agency to handle

the media buying can be a viable solution.

In some instances, ad networks will have policies in place that limit the

advertiser’s options in choosing between managed and self-serve media

buying. For example, some smaller networks may provide very little

transparency into where specific ads are running, and therefore push

advertisers to always go managed. On the other side of the spectrum,

larger players may impose relatively high spending minima before offering

an advertiser to move from their self-serve platform to a dedicated

account manager.

No matter which setup works best for you, it is imperative that you allocate

in-house resources to cross check KPIs reported by ad networks with

the data from your mobile attribution provider. This way, you can act

on optimization opportunities on the spot – either from your self-serve

tool, or in collaboration with your mobile agency or account manager.


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5) Consider a user-focused approach to media buying: The traditional

form of mobile media buying revolves around a service provided by

ad networks by which advertisers can purchase pre-packaged ad

inventory at scale. It can be based on many criteria, including vertical

(i.e. casual gaming apps), user demographics (i.e. apps used mainly

by young females in the US), and more. In recent years however,

the increasing automation of the media buying process through

programmatic technologies made it possible to execute transactions

on a per-user level.

Rather than targeting the entire audience of a specific media property,

ads can be delivered only to a set of users who are most relevant

to the advertiser. Today, this form of media buying has become the

dominant model in the digital industry, both on mobile and desktop,

and increasingly on TV.

234.3%

118.2%

$4.44

$15.45

$21.22

US Mobile Programmatic Display Ad Spending, 2014-2017

Mobile Programmatic Display Ad Spending% change % of total mobile display ad spending

www.eMarketer.com

billions. % change and & of total mobile display ad spending

Note: mobile display ads transacted via an API, including everything frompublisher-erected APIs to more standardized RTB technology, includesnative ads and ads on social networks like Facebook and Twitter;includes ad spending on tabletsSource: eMarketer, April 2016

2014 2015 2016 2017

60.0%

$9.68

59.6%

69.0%78.0%

37.3%46.0%


34www.appsflyer.com

Advertisers can greatly benefit from a user-focused approach to media buying

if they leverage capabilities to specifically target (and re-target) users that

are most valuable to them, independent of traditional demographics and

verticals. At the same time, a more automated media buy both enables and

requires a truly data-driven approach. For mobile user acquisition campaigns,

attribution providers deliver this data with consistency for every impression, in

real time. By connecting advertisers with their buying tools, data management

platforms, and their media sources, they provide transparency and fuel an

informed media buy.

6) Leverage your buying power on private programmatic marketplaces

Programmatic buying is often equated with an open auction model, and

Real-Time Bidding in particular. While it is true that many programmatic

technologies make use of the ability to create an open marketplace where

multiple sellers and buyers are bidding on inventory in near real-time,

creating a pricing structure shaped by supply and demand, this view is

far from complete.

The desire from advertisers and publishers to differentiate premium from

remnant inventory has led to the emergence of a number of alternate

types of programmatic markets, from Invitation-Only (auction with limited

number of select advertisers who get first-look option) to Automated

Guaranteed (direct selling of inventory at a fixed rate through an automated

system), and Unreserved Fixed Rate (first-look option provided to select

advertisers at a fixed rate with no obligation to buy).


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On these markets, advertisers with high user acquisition spend, or who are

represented by sizable mobile agencies can leverage their buying power

to get better deals, access to quality publishers with premium inventory,

guaranteed performance or exclusivity, and more while benefiting from

a streamlined programmatic buying process.

Type ofInventory(Reserved,Unreserved)

Pricing(Fixed,Auction)

FixedReservedAutomated Guaranteed

Unreerved Fixed rate

Invitation-only Auction

Open Auction

Uneserved

Uneserved

Uneserved

One-One

Programmatic guaranteed

Programmatic premium

Programmatic direct

Programmatic reserved

Private marketplace

Private auction

Closed auction

Private access

Source: Interactive Advertising Bureau

Prefered deals

Private access

First right of refusal

Real-time bidding (RTB)

Open exchange

Open marketplace

Fixed One-One

Auction One-Few

Auction One-All

Participation(One Seller-One Buyer,One Seller-Few Buyers,One Seller-All Buyers)

Other TermsUsed in Market

OtherConsiderations

Prioritization

in the ad server

Deal ID

Data usage

Transparency

to buyer

Price floors


36www.appsflyer.com

FINAL THOUGHT ON ROI

CHAPTER 4

The first mobile app wave was all about user acquisition and getting

as many users as possible to download an app. At present, we’re in the

midst of the second app wave, which is centered on engagement and the

understanding that getting users to download an app is only step one.

More importantly, step two is about getting users to actually use an app,

continuously, among a sea of apps out there.

The third app wave is about monetization and ROI. It’s about maximizing the

value of engaged users and measuring each and every user’s contribution

to an app’s bottom line. However, the vast majority of app developers and

marketers have yet to jump on this wave. But with the understanding that

measuring mobile ROI is completely within reach - which is exactly what

this guide has sought to communicate - it is only a matter of time before

they catch the perfect wave.


37www.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 2,000 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 250 billion mobile

actions each month. Clients include Playtika, IHG, Alibaba, Baidu, Trivago,

Macy’s, Samsung, DeNA, and HBO.

About the Author

Shani Rosenfelder is the Content Marketing Lead 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.

Contributing Writer

Jasper Radeke is the Director of Marketing, North America

at AppsFlyer. With 7+ years of experience in mobile and

digital marketing, his goal is to drive the evolution of

mobile advertising by making it more transparent and

measurable. You can follow him on LinkedIn.

WANT TO LEARN MORE ABOUTMOBILE ATTRIBUTION?

VISIT US


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https://www.linkedin.com/in/shanirosenfelder

https://www.linkedin.com/in/jasperradeke?authType=NAME_SEARCH&authToken=z0we&locale=en_US&srchid=4831983321471862899883&srchindex=1&srchtotal=1&trk=vsrp_people_res_name&trkInfo=VSRPsearchId%3A4831983321471862899883%2CVSRPtargetId%3A24338399%2CVSRPcmpt%3Aprimary%2CVSRPnm%3Atrue%2CauthType%3ANAME_SEARCH

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