Swrve Monetization Report 0114

7/25/2019 Swrve Monetization Report 0114

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THE SWRVEMONETIZATION

REPORTJanuary 2014


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The Swrve Monetization Report -January 2014 2

INTRODUCTIONThe mobile games industry has undergone a revolution in the past fewyears.

From a situation that many on the outside looking in would view as normal - namely consumerspaying for a game up front - we have migrated to the freemium model in which developerseffectively give the product away and then rely on in-game purchases.

That model has been a success, but theresstill surprisingly little data relating to exactlyhow consumers interact with the freemiummodel. This report intends to change all that.

Swrve is the worlds leading marketing engine

for freemium games. With access of tens ofmillions of players, we have aggregated datafrom across our system to finally lift the lid

on what goes on after install - and where themoney is made.

Enjoy!

Brief Notes on Our

Methodology

The report is restricted to

freemium games and examines

only revenue delivered via in-

app purchases

Using Swrves Fraud Filter

technology, only genuine

revenue events are reported

The report is restricted to a

single month of data - providing

a snapshot of how mobile

gamers monetize; it is not

intended to show lifetime values

In total 10s of millions ofusers, and their associated

monetization events, have been

analyzed and aggregated to

produce this data


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HOW MANY PAY, AND HOW MUCH?Its a given that the vast majority of freemium games players dontactually spend money at all. In fact, in January 2014 only 1.5 % of active

players surveyed made an in-app purchase at all.

That means that excluding any revenuefrom advertising (which does not fall under

the remit of this report), the vast majority ofplayers in any given month deliver no revenue,and confirms again that great care should betaken when acquiring users to ensure theyare, as much as possible, in the subset ofspenders.

If we look at how MUCH is spent per month

(and from now on all averages and percentilesrelate solely to paying players, unless stated

otherwise), we see a monthly mean averagetotal spend of $15.27. This is in effect a typical(or target) MRPPU - monthly revenue perpaying user.

That total comprises of an average numberof purchases per month of 2.57, of an average

value of $5.94. The rest of this report will takethe form of a detailed breakdown of thesenumbers.

1.5% of playerspurchased

in January 2014

Average monthlyspend is $15.27


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A full 49% of payers only make a singlepurchase, although note that this relates tothe month of January, rather than a lifetime

figure.

At the other extreme, 13% of paying playersmake 5 or more purchases. A repeatedtheme is the dependence of the freemiumgames model on a small percentage of payingplayers (who are, if we recall, themselves only1.5% of the total player base).

NUMBER OF PURCHASESThe illustration below indicates the percentage of payers making theabsolute number of purchases - from 1 to 5 or more.

49% of all payers

make only a singlepurchase per calendar

month


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This illustration shows in-app purchases in value buckets (as individual purchases) and, plotsboth the % of purchases that fall into that bucket, and the total % of revenue accrued from thatbucket.

VALUE OF PURCHASESWe saw above that the average value of an in-app purchase is $5.01.How that figure breaks down sheds interesting light on the relative

contributions of different purchase types, or value levels, to the bottomline of a freemium game business.

$ 1-5

66.8%

$ 6-10 $ 11-20$ 21-50$ 51+

27.4%

22.4%

27.0%

8.2%

22.4%

1.9%

14.0%

0.7%

9.1%

Purchases by SKU Value Revenue by SKU Value


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Most notably, we see that while purchasesbetween $1 and $5 represent a majority (67%)of all purchases, they contribute only 27% oftotal revenues.

Similarly, high value purchases (those over$50) unsurprisingly account for a mere 0.7%

of the total number, but a still significant asizable 9% of total revenue. It is worth notingthat very few SKUs in fact fall into this range.Those titles that currently do not offer SKUsat prices greater than $50 should probablyconsider introducing them.

In-App purchases ofover $50 contribute

9% of all revenues


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Below we chart every paying player in themonth in deciles by total spend. In plain

English, that means we show the bottom 10%- ie the 10% of users who spent the least intotal - on the left, and move in 10% steps tothe top 10% on the right.

0.7%

0-10%1 0-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100%

1.4% 2.2%2.8%

4.0%4.8%

6.9%

10.2%

16.3%

50.8%

(#1.2, $0.90)(#1.2, $1.70) (#1.2, $2.80)

(#1.6, $2.60)

(#1.8, $3.30)(#1.8, $4.00)

(#2.6, $4.10)

(#3.2, $4.90)

(#4.0, $6.20)

(#7.0, $11.10)

Percentage of Total Revenue by Player Spend Category

HOW PLAYERS BREAK DOWNMany industries are characterized by a small number of customersdelivering a significant percentage of total revenues. How true is this of

the freemium games market?

We show for each of those deciles the % ofrevenue derived from that group, the average

number of purchases each member of thegroup made, and the average purchase pricethey made them at.


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A couple of key data points immediatelystand out. A full 50% of revenue derives fromthe top 10% of payers. To add further context,if we express that group as a % of total playersthey represent a mere 0.15% of that group.That number bears repeating: 0.15% of allmonthly players contribute 50% of total

revenue.

Taking an even more granular approach, 13%of revenue comes solely from the top 1% ofpayers (not graphed).

The implications of those numbers bearextended consideration, most of which wedont have space to cover.

The top 10% ofpayers (by revenue)

contribute 50% oftotal revenues in the

month

However at the very least they suggest anyfreemium business should ask themselvesthe following three questions:

Have we identified these customerswithin our own business?

Are we investing appropriate time and

resources on retaining this specificgroup of players?

Do we understand where they comefrom, and have we amended ouracquisition strategy accordingly?

Note in addition, from the same graph, thatconversely a full 50% of all paying playerscontribute little over 11% of all revenues.

It is clear from the data that it is both thegreater value of individual purchases AND thegreater frequency of purchases that is thedriver behind the large value derived from the

top 10%.

This group has an average number of

purchases of 7, only around 700% greaterthan the bottom 10%( average purchases 1.2),but and the average value of those purchasesis $11.1 - an 681% uplift.


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Shown on the following page is time to firstpurchase, shown in buckets of 24 hours fromfirst session start.

From this chart it is clear that a significantamount of spend activity takes place relatively

soon after the game is first played. That isborn out by the fact that the average time tofirst purchase is a mere 23.97 hours.

NEW PLAYER SPENDING PATTERNSApproximately 53% of our player base were new in the month - ieplaying the game for the first time in the calendar month of January.

As we wished to give these payers sufficient time to develop a spendprofile, they are taken from the first 17 days of the month only, allowing

us to compare the first 14 days activity for all.

Average time to firstpurchase is 23.97

hours

01 00 200

Hours

3000

10000

20000

30000

Min [ hrs: 0]

25th Percentile [hrs: 1.17 ]

Median [ hrs: 23.97 ]


Max [ hrs: 335.98 ]


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Note this analysis is (deliberately) limited tothe first 14 days of life. However, it is fair to saythat the shape of the long tail is apparent evenat this point in a payers lifecycle.

Next we looked at how many new payers wenton to make a second purchase within the 14

day period, and how long the typical delaybetween the 1st and 2nd purchase was.

As can be seen below, 53% of payers go on tomake a repeat purchase, whilst 47% do not,and 13.7% of new players accrue more than 4purchases in their first 14 days.

They are probably on their way tomembership of our top 10%.

46.9%MAKE ONE PURCHASE

21.8%MAKE TWO PURCHASES

11.2%MAKE THREE PURCHASES

6.4%MAKE FOUR PURCHASES

13.7%MAKE FIVE+ PURCHASES


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Interestingly, the typical time lapse betweenthe first and second purchase is only 1 Hour40 Mins. The chart below shows typical timelapse to second purchase amongst thosenew players who made at least 2 purchases inthe month.

00

1002 00

Hours

300

10000

20000

30000

Min [ hrs: 0]


Median [ hrs: 1.47 ]


Max [ hrs: 335.96 ]

As the previous chart related to average timeto first purchase, this chart shows 14 daysbinned into 24 hour periods. Whilst secondpurchases can occur after a significantamount of time has passed, they tend tofollow hard on initial buy-ins.


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Further detail of the revenue spread for newusers can be seen below, in which we chartthe total revenue (for the first 14 days oflife) that accrues on day one, day two andso on - to a final bucket of beyond day 5.That information reveals that fully 72% of allrevenue is accrued by the third (change as

applicable) day of life, with only 15% comingafter day 5.

Whilst there is almost certainly a long tail interms of revenue beyond the first 14 days(again, not shown here, and not reported)the consistency in the pattern in terms ofrevenue share suggests long term value canbe predicted with some accuracy after as

little as 3 days.

60.8%ACCRUEDONDAY1

10.9%ACCRUEDONDAY2

6.0%ACCRUEDONDAY3

4.1%ACCRUEDONDAY4

4.1%ACCRUEDONDAY4

3.2%ACCRUEDONDAY5

15.0%ACCRUEDONDAYS 6-14

The typical time lapsebetween the first and

second purchase is

only 1 Hour 40 Mins

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Swrve Monetization Report 0114