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REVENUE MODEL




for an iOS (iPhone/iPad/iPod Touch)
         cooking application
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 .
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.
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
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
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.
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
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.
How many of those devices are
        network attached?
• Let's use 70%.
• 10,000 converts * 0,7 * $1.89 per convert =
  $13,230
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
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.
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.
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).

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Revenue model for cooking app

  • 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).