The economics of mobile game publishing

Our latest guest post brings a new face to the GameAnalytics blog. Eric Seufert is a quantitative marketer with a specific interest in freemium analytics and product design. He is currently writing a book called Freemium Economics, which will be published by Morgan Kaufmann in late 2013. He also blogs frequently at Ufert.se, so feel free to pay him a visit.  Supercell’s recent $100mm secondary financing round raises some interesting questions about how mobile game developers capitalize on a hit game. A title in the Top 10 grossing chart for the US can, as evidenced by a number of recent high-profile examples, generate upwards of $500,000 per day, but this capacity for revenue generation does not directly scale with enterprise value for the firm because these revenue streams are not themselves business units. Rather, in many cases, hit games are evaluated as short-term windfalls that 1) cannot be expected to persist permanently into the future and 2) are assumed to be non-repeatable (ie. a huge hit game is seen as an aberration, not the result of a product strategy that can be re-implemented in another game to the same degree of success). In order to build sustainable streams of revenue out of massive hit games, some studios have begun engaging in publishing activities. The reasoning is clear:

  1. In most cases, Customer Equity (the aggregate total of all current and future Lifetime Customer Values) is the sole component of enterprise value for a mobile gaming studio. Brand Equity represents no monetary value; a mobile gaming studio (unless they have diversified into other revenue streams, as has Rovio) gains nothing in the present from a hit game in the past if those users have churned out of the ecosystem.
  2. Mobile game development is a long and resource-intensive process. And the success of a mobile game in development is difficult to assess, especially given the rapidly escalating competitive landscape of the mobile gaming marketplace and the brisk pace at which mobile technologies evolve. The present value of future cash streams attributed to a future release decreases drastically as the timeline for projection exceeds one year.
  3. Increased marketing costs have priced many small studios out of the user acquisition market. Publishing is the only viable launch option for studios without large cash reserves, absent incredibly virality.

The publishing model therefore sits at the confluence of the needs of studios sitting on large hits but with long-term product pipelines (ie. their next release date is more than nine months away, when the user base of the current hit is expected to have decayed substantially) and small studios sitting on games with great potential but without the marketing budget to seed their game with a large user base at launch. The terms of a publishing agreement generally involve a revenue sharing component and a user acquisition component. The developing studio agrees to split revenues with the publisher (generally at a rate of 50% of net revenues) in exchange for a pre-determined acquisition budget to be spent by the publisher on the game (sometimes the revenue split will compensate for the acquisition spending, eg. 80% of the revenue goes to the publisher until acquisition spending has been recouped). An agreement structured this way is no-risk for the developer because the game is guaranteed to receive users without any upfront costs (except for development costs, which are sunk). The structure of the agreement involves some risk on the part of the publisher, although the publisher would have thoroughly vetted the game in terms of its potential to generate revenue. In all, it is a good solution for a publisher starved for a destination for its current users (lest they churn out, requiring re-acquisition in the future) and for a developer starved for a marketing budget with which to launch a title. The question that arises from these agreements is: on what criteria should a developer make the decision to go with a publishing agreement or not? What factors determine the prudence of the decision? And can this decision be modeled?

The Decision Model

I have attempted to model the decision; the Excel spreadsheet can be downloaded here. I used as primary determinants of the decision six assumptions:

Adjusting the values of the assumptions illustrates clearly that revenue is driven almost entirely by virality, the acquisition budget, and the rate of re-investment; when k-factor is high, revenue growth increases at a compounding rate given constant ARPDAU, negating the need for any initial acquisition budget but justifying continued re-investment of net revenue through future paid acquisition. When virality is low, only a very high re-investment percentage allows the user base to grow (assuming organic installs don’t surge as a result of some external event, such as platform featuring). The starting assumptions about the game used in the model reflect a mediocre commercial performer; an ARPDAU of $.10 is respectable but not exceptional, and a K-factor of 15% is strong but not overly viral. On the other hand, the terms of the publishing deal illustrated in the model are not favorable but not altogether unrealistic. It should be noted that the only expenses considered in this model are those related to acquisition, not capital expenditures, salaries, travel and entertainment, etc. This is not a full financial model but rather an operational model describing a specific decision point. The following six scenarios are examined:

1. $0 initial monthly acquisition budget with 25% net revenue reinvestment, no publishing deal

$0 initial monthly acquisition budget with 25% net revenue reinvestment, no publishing deal

 2. $0 initial monthly acquisition budget with 25% net revenue reinvestment, publishing deal(monthly spend=$20,000, revenue split after acquisition recoup=50%, revenue split before acquisition recoup=80%)

• 52-week total net revenue: $288,168

• Monthly revenue curve:

$0 initial monthly acquisition budget with 25% net revenue reinvestment, publishing deal(monthly spend=$20,000, revenue split after acquisition recoup=50%, revenue split before acquisition recoup=80%)

3. $5,000 initial monthly acquisition budget with 25% net revenue reinvestment, no publishing deal

• 52-week total net revenue: $251,915

• Monthly revenue curve:

$5,000 initial monthly acquisition budget with 25% net revenue reinvestment, no publishing deal

4. $5,000 initial monthly acquisition budget with 25% net revenue reinvestment, publishing deal (monthly spend=$20,000, revenue split after acquisition recoup=50%, revenue split before acquisition recoup=80%)

• 52-week total net revenue: $318,642

• Monthly revenue curve:

$5,000 initial monthly acquisition budget with 25% net revenue reinvestment, publishing deal (monthly spend=$20,000, revenue split after acquisition recoup=50%, revenue split before acquisition recoup=80%)

5. $15,000 initial monthly acquisition budget with 25% net revenue reinvestment, no publishing deal

• 52-week total net revenue: $389,805

• Monthly revenue curve:

$15,000 initial monthly acquisition budget with 25% net revenue reinvestment, no publishing deal

6. $15,000 initial monthly acquisition budget with 25% net revenue reinvestment, publishing deal (monthly spend=$20,000, revenue split after acquisition recoup=50%, revenue split before acquisition recoup=80%)

• 52-week total net revenue: $379,592

• Monthly revenue curve:

$15,000 initial monthly acquisition budget with 25% net revenue reinvestment, publishing deal (monthly spend=$20,000, revenue split after acquisition recoup=50%, revenue split before acquisition recoup=80%)

Conclusion

Examining different scenarios with the model under different game and publishing agreement assumptions reveals a few realities of the economics of game publishing:

Publishing in some cases is a Pareto optimal choice for both publisher and developer, but those cases conform to specific parameters. The degree to which a developer will benefit from a publishing agreement relates directly to that developer’s ability to generate viral installs and its tolerance for setting revenue aside for marketing expenditure.

About the author

Eric Seufert

Eric Seufert is a quantitative marketing and mobile user acquisition specialist. He has a specific interest in freemium / free-to-play economics, programmatic statistical methods, and "big data" analysis for building predictive revenue and retention models. Estimating lifetime customer value, user segmentation, and eCPA optimization are also on the list.

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