Apptopia Correlation Analysis & Case Studies

Quick start guide to finding Alpha with Apptopia Data

Apptopia Data Points Country Dimension Public Company Data Points

How We Approached “Correlation Analysis”

Step 1 Understanding Dimensions

Downloads

IAP (In-App Purchase) Revenue

Daily Active Users

Monthly Active Users

Sessions

Any of the above data points can be broken down by country or summed up at the World-wide level.

Quarterly earnings revenue

Daily stock data (closing price)

Company reported metrics*

*Company reported metrics are data points that we collect directly from companies’ 10Q & 10K reports. For Pandora, these would be things like Listen-er Hours or Paying Subscribers, where as for American Airlines, this will be key industry metrics such as RPMs or ASMs.

Step 2 Types of Analysis

For all of our Correlation Analysis, we are using the Pearson’s Correlation Coefficient (PCC). This is a statistical measure of linear cor-relation between 2 variables resulting in a value of 1 to -1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation. Ultimately, it’s a widely respected statistical measure of “closeness.” We sliced the data into three segments and then ran the PCC algorithm separately on each of the three segments:

1

4

Actuals

2 QoQ

3 YoY

Public stockcorrelation

This means we’ve simply compared raw Apptopia data values to raw company values and graphed these numbers specifically against each other.

In this case, we are comparing Quarter over Quarter trends in Apptopia Data against the same Quarter over Quarter trends in company values. For instance, to measure Q2 growth, we are using following equation = [ (Q2 - Q1) / Q1 ].

In this case, we are comparing Year over Year trends in Apptopia Data against the same Year over Year trends in company values. For instance, to measure Q2 2016 growth, we are using following equation = [ (Q2 2016 - Q2 2015) / Q2 2015 ].

We layered daily stock close prices on top of daily values of Apptopia Performance Data to identify which data points had the strongest long term trend / closeness to granular stock movement.

Step 3 What We Did

Ultimately, we analyzed every combination of Apptopia Data Point, Company Data Point, and Correlation Type. This result-ed in anywhere from 45 to 75 different analyses per company. These were reviewed both programmatically and individually by an analyst on our team to pick the combinations which were most predictive (if any).

Step 4 Best Practices with Mobile App Data

While we do not claim to be experts on financial analysis or investment strategy, we do believe we understand our data--and mobile intelligence data--better than anyone else today. As such, we wanted to share some tips, tricks, and best practices we learned having gone through this exercise.

It’s very important to note that mobile data is way more volatile than normal company reported metrics. A news article, a tweet, a new ad campaign all could cause major spikes in key mobile metrics for a short period of time. As such, it’s very com-mon to see quarter over quarter % gains and losses in Apptopia’s data which is larger (though still very correlative) than the actual gains or losses the company reported. This is because “movements” or changes in mobile are always larger and easier (i.e. the barrier to entry to gaining or losing users on mobile is a matter of seconds).

Typically, we are advising that companies only look at data starting from January 1st, 2016 to present. The reason is there was 56% FEWER apps in the ecosystem on January 1st 2015 than there are today. Ultimately, this means that it was an entirely different store and market at that time, so it’s very difficult to stitch data from this time period into one cohesive story. We also suggest being aware of when certain apps were launched as we’ve found a handful of examples where we saw crazy growth rates for apps due to the fact that brands only started promoting their app in 2016, causing a huge spike in users when this happened. As you can imagine, this was not indicative of company growth but rather just the stabilizing of their mobile user base.

Mobile Data is Volatile

Beware of Very Historical Data

Step 4: Best Practices with Mobile App Data (cont.)

This is an extremely important strategy for finding the strongest correlation. The hypothesis is that sometimes the flagship app (i.e. the breadwinner) for a certain publisher is more indicative of that publisher’s success than their entire portfolio. This is because the “noise” or newly launched apps, which they are plowing ad dollars into, aren’t as tied to the publisher’s success as the cash cow, which pays the bills. Two classic examples of this are:

Mobile data is extremely granular and there are numerous different signals you can pull out of it. This is why our strongest recom-mendation is that when you are doing your own correlation analysis, you try every combination of data points that you get from Apptopia and Bloomberg. You never know when Downloads, MAU, or Sessions is going to have the stronger correlation with the key metric you care about. Different apps have various signals hidden across the data points and the best way to bring these to the surface is to test every dimension and then key in on the combinations that are most consistent over the last 12+ quarters.

Zynga Poker makes up the majority of their core revenue and has a much stronger correlation signal with Zynga’s reported revenue than if you look at the sum of all of Zynga’s apps.

Very similarly is the Kim Kardashian app, which drives the majority of Glu’s revenue. This app’s metrics alone have a stronger correlation with company reported metrics than the sum of all of Glu’s apps.

Flagship Apps Are Important

Test All Combinations

Zynga (ZNGA)

Glu Mobile (GLUU)

Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0,04

0.06

0.08

0.1

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

App downloads convert into paying customers

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Downloads Quarter over Quarter

Dow

nloa

ds -

QoQ

Cha

nge

(%)

Reported Revenue - QoQ

Change (%)

NASDAQ: PZZA

Apptopia Downloads Reported Revenue

Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Mobile check-ins are a clear indicator of ticket sales

Company Reported Metric Apptopia Metric Analysis TypeRevenue Passenger Mile (RPM) App Sessions Quarter over Quarter

QoQ

Cha

nge

(%)

NASDAQ: AAL

Apptopia Sessions Revenue Passenger Mile (RPM)

Roku revenue growth driven by increased mobile app engagement

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Sessions Actuals

Sess

ions

- Q

oQ C

hang

e (%

)

Reported Revenue - QoQ

Change (%)

NASDAQ: ROKU

Q2 2015

Q1 2015

Q3 2015

Q4 2015

Q1 2016

Q2 2016

Q3 2016

Q4 2016

Q1 2017

Q2 2017

Q3 2017

Q4 2017

Q1 2018

Q2 2018

Q3 201870M

90M

110M

130M

150M

170M

190M

210M

70

50

90

110

130

150

170

190

210

230M

250M

270M

290M

310M

Apptopia Sessions Reported Revenue

Q4 Mobile usage is indicative of holiday spike

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Sessions Q4 Spotlight

Sess

ions

Reported Revenue (millions)

NASDAQ: ROKU

Q4 2015 Q4 2016 Q4 2017

0

50M

100M

150M

200M

250M

300M

20

0

40

60

80

100

120

140

160

180

200

Apptopia Sessions Reported Revenue (millions)

App usage before and during cruises has strong correlation with ticket sales

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Sessions Quarter over Quarter

QoQ

Cha

nge

(%)

NYSE: RCL

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1.0

1.2

Q2 2015 Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018

Apptopia Sessions Reported Revenue

High adoption rate of Atlassian’s apps indicative of growth in paying user base

Company Reported Metric Apptopia Metric Analysis TypePaying Customers Monthly Active Users Quarter over Quarter

QoQ

Cha

nge

(%)

NASDAQ: TEAM

Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Monthly Active Users Paying Customers

Paying customers directly correlate with MAU

Company Reported Metric Apptopia Metric Analysis TypePaying Customers Monthly Active Users Year over Year

YoY

Chan

ge (%

)

NASDAQ: TEAM

Q4 2016Q3 2016Q2 2016 Q1 2017 Q2 2017 Q3 2017

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Monthly Active Users Paying Customers

More sessions = more transactions = more revenue

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Sessions Actuals

Sess

ions

Reported Revenue (millions)

NYSE: SQ

Q2 2015

Q1 2015

Q3 2015

Q4 2015

Q1 2016

Q2 2016

Q3 2016

Q4 2016

Q1 2017

Q2 2017

Q3 2017

Q4 2017

Q1 2018

Q2 2018

Q3 2018100M

200M

300M

400M

500M

600M

700M

100

0

200

300

400

500

600

700

800

900

1000

Apptopia Sessions Reported Revenue (millions)

Strong end to 2017 for mobile a leading indicator for 2018 revenue growth

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Sessions Quarter over Quarter

QoQ

Cha

nge

(%)

NYSE: SQ

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018

Apptopia Sessions Reported Revenue

App downloads signify intent to purchase

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Downloads Quarter over Quarter

App

Dow

nloa

ds -

QoQ

Cha

nge

(%) Reported Reveue - Q

oQ Change (%

)

NYSE: WMT

-0.2

-0.3

-0.4 -0.15

0.15

-0.1

0.1

-0.05

0.05

0

-0.1

0

0.1

0.2

0.3

0.4

0.5

Q3 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018

App Downloads Reported Revenue

App downloads signify intent to purchase

Company Reported Metric Apptopia Metric Analysis TypeReported Revenue App Downloads Actuals

NYSE: WMT

App

Dow

nloa

ds

Reported Reveue (millions)

100K

140K

135K

130K

105K

125K

110K

120K

115K

Q3 2015Q2 2015Q1 2015 Q4 2015 Q1 2016 Q2 2016 Q3 2016 Q4 2016 Q1 2017 Q2 2017 Q3 2017 Q4 2017 Q1 2018

App Downloads Reported Revenue

Significant MAU increase indicates a strong quarter

Company Reported Metric Apptopia Metric Analysis TypeClosing stock price Monthly Active Users Public stock correlation

TGT Closing Stock Price

Targ

et M

onth

ly A

ctive

Use

rs

NYSE: TGT

Aug 17

Oct 18

Dec 18

Feb 18

Jul 17

Sep 17

Nov 18

Jan 18

Mar 18

0M $0

$10

$20

$30

$40

$50

$60

$70

$80

$90

1M

2M

3M

4M

5M

6M

7M

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9M

Target Monthly Active Users TGT Closing Stock Price

Fewer users represent fewer paying customers

Company Reported Metric Apptopia Metric Analysis TypeClosing stock price Monthly Active Users Public stock correlation

Mon

thly

Acti

ve U

sers

Reported Reveue (millions)

NASDAQ: DISH

SlingTV Montly Active Users DISH Closing Stock Price

A burst of in-app purchase revenue directly impacts company revenue

Company Reported Metric Apptopia Metric Analysis TypeClosing stock price IAP revenue Public stock correlation

Tind

er In

-app

Pur

chas

e Re

venu

eM

TCH Closing Stock Price

NASDAQ: MTCH

Tinder In-App Purchase Revenue MTCH Closing Stock Price

Engaged mobile audience is a consistent indicator of stock performance

Company Reported Metric Apptopia Metric Analysis TypeClosing stock price Monthly active users Public stock correlation

Mon

thly

Acti

ve U

sers

McD

onalds Closing Stock Price

NASDAQ: MCD

Monthly Active Users McDonalds Closing Stock Price

50

75

100

125

175

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200

0

5M

10M

15M

20M

25M

Q3 2015

Q2 2015

Q1 2015

Q4 2015

Q1 2016

Q2 2016

Q3 2016

Q4 2016

Q1 2017

Q2 2017

Q3 2017

Q4 2017

Q1 2018

Q2 2018

Q3 2018

Q4 2018

www.apptopia.com

sales@apptopia.com

/company/apptopia

@apptopia

/apptopia