Statfords University project. Venture Lab 2012.

1. REVENUE MODEL
for an iOS (iPhone/iPad/iPod Touch)
cooking application

2. The mobile app
ecosystem
According to
Gartner, worldwide mobile app
revenue exceeded $15 billion in
2011.
To monetize free apps, one can
use in-app advertising, in app
purchases, or a freemium version.
 The predictions for the mobile
advertising space are to hit $5.4
billion by 2015 in the U.S .

3. Crucial concepts to understanding mobile
advertising:
 eCPM (Effective cost per mille): The revenue the developer receives per every 1000
impressions.
 CTR (Click-through rate): This is obtained by dividing the number of users who clicked
on an ad by the number of total impressions. The primary contributor to a high CTR is
the relevancy of the ad and the ad placement strategy. In order to increase the
relevancy of ads, the campaign should be of a premium and local nature: You want to
display ads to users in their native language, and the topic of the ads should be relevant
to the type of app in which the ad appears. In terms of the ad placement strategy, the
location and format (text vs banner) of the ad also have a significant effect on the
overall CTR. For this, it is important that you work with a company that will walk you
through the process following the integration in order to maximize the effects of the
ads.
 Global Fill Rate: The percentage of the total ad inventory that is populated by paying
ads. Due to many different factors, in some instances ads will either not appear at all, or
a “house ad,” essentially a self-promotional ad, will appear in its place. If your app has a
100 percent global fill rate, out of which 60 percent are house ads (a common number
in this case), you are essentially leaving that 60 percent of your potential revenue on
the table. In fact, a lower fill rate with all “paid ads” is more effective than a high fill rate
with house ads. So don’t be misled by a high fill rate; the paid ads are the only ones that
count.

4. To estimate the revenue model
we should calculate:
• conversion rate
• partial CLV
• eCPM
• fill rate
• how much time a person who doesn't convert
spends using our app
• and what percentage of the install base is
network connected

5. The average conversion rate for the entire
mobile apps industry is around 10%
• Let´s take 100,000 downloads as the original
amount
• 100,000 * 0.1 = 10,000 converts

6. Now we need to calculate CLV
(customer lifetime value)
• The most simple CLV is how much the person pays
for the premium version of our app (or the average
spend on in-app purchases) plus the value of the
advertising they are served.
• How long is a user considered a customer? At some
point they'll get tired of your app and stop using
it. Let's say they use it for an average of 3
weeks. During those three weeks, they use it for an
average of 15 minutes a day.
• 7 days a week * 3 weeks * 15 minutes/day = 315
minutes.
• So a customer uses your app for 210 minutes while
they're a customer.

7. How many ad impressions did we
serve them?
1) Let's assume a 100% fill rate.
• For an ad display to count as an impression, it
has to be up for 30 seconds, so that's 2
impressions per minute.
• 315 minutes * 2 impressions/minute = 630
impressions.
2) Let's assume $3 eCPM
• 630 impressions / 1000 (CPM is per 1000) * $3
= $1.89

8. How much were we paid for those
impressions?
• Let's assume $3 eCPM
• 630 impressions / 1000 (CPM is per 1000) * $3
= $1,89
• So we could make $1,89 per customer serving
ads.
• There were 10,000 converts, but not every
customer is network connected.

9. How many of those devices are
network attached?
• Let's use 70%.
• 10,000 converts * 0,7 * $1.89 per convert =
$13,230

10. Then…
• Let´s calculate the other 90% of the average
conversion rate  100,000 * .9 = 90,000
• Let's use the same 70% of the network
attached devices  90,000 * 0.7 = 63,000

11. How much time did they spend
evaluating the app?
• If we assume that all of the non-converts just
installed, tested, and deleted the app, let's
say 6 minutes.
• We remind you that for an ad display to count
as an impression, it has to be up for 30
seconds, so that's 2 impressions per minute.
• 63,000 * 6 = 378,000 minutes * 2
impressions/minute = 756,000 impressions.

12. If we get 100% fill rate for ads and our
eCPM is $3. We've served 756,000 ads.
So...
• 756,000 / 1000 (CPM is per 1000) = 756 * $3 =
$2,268 (or about $0.09 per download).
• So from the original 100,000, those 90,000
that downloaded, tried, didn't like, and
deleted the app you made $2,268.

13. And then converts + non-converts:
• $13,230 + $2,268 = $15,498
• So from 100,000 downloads we would make
$15,498 in ad revenue in this scenario (about
$0.15 per download).