U.S. patent application number 12/082445 was filed with the patent office on 2008-10-16 for forecasting.
Invention is credited to Justin Holmes, Peter Sispoidis, Jeffrey Thibeault.
Application Number | 20080255927 12/082445 |
Document ID | / |
Family ID | 39854232 |
Filed Date | 2008-10-16 |
United States Patent
Application |
20080255927 |
Kind Code |
A1 |
Sispoidis; Peter ; et
al. |
October 16, 2008 |
Forecasting
Abstract
A method for forecasting a performance characteristic of a game
title is provided and includes selecting base game-play data for
the game title, wherein the base-game play data is at least
partially responsive to the game-play pattern of a user, generating
base sales data for the game title responsive to initial sales data
and generating forecast data for the game title responsive to the
base sales data and the base game-play data.
Inventors: |
Sispoidis; Peter; (Guilford,
CT) ; Holmes; Justin; (Guilford, CT) ;
Thibeault; Jeffrey; (Branford, CT) |
Correspondence
Address: |
THE LAW OFFICES OF STEVEN MCHUGH, LLC
46 WASHINGTON STREET
MIDDLETOWN
CT
06457
US
|
Family ID: |
39854232 |
Appl. No.: |
12/082445 |
Filed: |
April 11, 2008 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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60923264 |
Apr 12, 2007 |
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60923344 |
Apr 12, 2007 |
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60923345 |
Apr 12, 2007 |
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60923346 |
Apr 12, 2007 |
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60923351 |
Apr 12, 2007 |
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60923352 |
Apr 12, 2007 |
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60923353 |
Apr 12, 2007 |
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G07F 17/3227 20130101;
G06Q 30/02 20130101; G07F 17/32 20130101; G06Q 30/0202
20130101 |
Class at
Publication: |
705/10 |
International
Class: |
G06Q 50/00 20060101
G06Q050/00 |
Claims
1. A method for forecasting a performance characteristic of a game
title, the method comprising: selecting base game-play data for the
game title, wherein the base-game play data is at least partially
responsive to the game-play pattern of a user; generating base
sales data for the game title responsive to initial sales data; and
generating forecast data for the game title responsive to the base
sales data and the base game-play data.
2. The method of claim 1, wherein said initial sales data includes
at least one of said sales forecast data, sales data from previous
versions of the game title and sales data from similar game
titles.
3. The method of claim 2, wherein generating base sales data
includes combining said at least one of said sales forecast data,
sales data from previous versions of the game title and sales data
from similar game titles.
4. The method of claim 1, wherein generating forecast data includes
generating final sales data responsive at least in part to the base
sales data.
5. The method of claim 4, wherein generating final sales data
includes combining the base sales data with sales modifier data,
wherein said sales modifier data includes at least one of seasonal
sales modifier data, marketing modifier data, piracy modifier data,
pricing modifier data and buzz modifier data.
6. The method of claim 4, further comprising combining the final
sales data and the base game-play data to generate life-cycle
data.
7. The method of claim 6, further comprising combining the
life-cycle data with a life-cycle modifier data to generate the
forecast data.
8. The method of claim 7, wherein life-cycle modifier data includes
data that relates the number of hours the game is played to the
time frame that the game is played.
9. The method of claim 5, wherein sales modifier data includes data
that relates the number of units sold to the amount of time the
game is played.
10. A system for implementing a method for forecasting the
performance of a game title, the system comprising: a means for
selecting base game-play data for the game title, wherein the
base-game play data is at least partially responsive to the
game-play pattern of a user; a means for generating base sales data
for the game title responsive to initial sales data; and a means
for generating forecast data for the game title responsive to the
base sales data and the base game-play data.
11. The system of claim 10, wherein said initial sales data
includes at least one of said sales forecast data, sales data from
previous versions of the game title and sales data from similar
game titles.
12. The system of claim 11, wherein generating base sales data
includes combining said at least one of said sales forecast data,
sales data from previous versions of the game title and sales data
from similar game titles.
13. The system of claim 10, wherein generating forecast data
includes generating final sales data responsive at least in part to
the base sales data.
14. The system of claim 13, wherein generating final sales data
includes combining the base sales data with sales modifier data,
wherein said sales modifier data includes at least one of seasonal
sales modifier data, marketing modifier data, piracy modifier data,
pricing modifier data and buzz modifier data.
15. The system of claim 13, further comprising combining the final
sales data and the base game-play data to generate life-cycle
data.
16. The system of claim 15, further comprising combining the
life-cycle data with a life-cycle modifier data to generate the
forecast data.
17. The system of claim 16, wherein life-cycle modifier data
includes data that relates the number of hours the game is played
to the time frame that the game is played.
18. The method of claim 14, wherein sales modifier data includes
data that relates the number of units sold to the amount of time
the game is played.
19. A computer readable storage medium having computer executable
instructions for implementing a method for forecasting the
performance of a game title, the method comprising: selecting base
game-play data for the game title, wherein the base-game play data
is at least partially responsive to the game-play pattern of a
user; generating base sales data for the game title responsive to
initial sales data; and generating forecast data for the game title
responsive to the base sales data and the base game-play data.
20. The computer readable storage medium of claim 19, wherein
generating forecast data includes, combining the base sales data
with sales modifier data to generate final sales data; processing
the final sales data with the base game-play data to generate
life-cycle data; and combining the life-cycle data with life-cycle
modifier data to generate the forecast data.
Description
RELATED APPLICATIONS
[0001] This application relates to U.S. Provisional Patent
Application Ser. No. 60/923,264 (Atty. Docket No. IGA-0001-P),
filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No.
60/923,344 (Atty. Docket No. IGA-0002-P), filed Apr. 12, 2007, U.S.
Provisional Patent Application Ser. No. 60/923,345 (Atty. Docket
No. IGA-0003-P), filed Apr. 12, 2007, U.S. Provisional Patent
Application Ser. No. 60/923,346 (Atty. Docket No. IGA-0004-P),
filed Apr. 12, 2007, U.S. Provisional Patent Application Ser. No.
60/923,351 (Atty. Docket No. IGA-0005-P), filed Apr. 12, 2007, U.S.
Provisional Patent Application Ser. No. 60/923,352 (Atty. Docket
No. IGA-0006-P), filed Apr. 12, 2007, U.S. Provisional Patent
Application Ser. No. 60/923,353 (Atty. Docket No. IGA-0007-P),
filed Apr. 12, 2007, all of which are incorporated by reference
herein in their entireties.
FIELD OF THE INVENTION
[0002] This disclosure relates generally to in-game advertising and
more particularly to a method for estimating desired parameters
relevant to in-game advertising.
BACKGROUND OF THE INVENTION
[0003] As the placement of realistic advertisements in video games
becomes more popular and acceptable in the gaming community, more
and more video games are beginning to utilize video game
advertisements as a viable source of revenue. Currently, most video
games that employ realistic advertisements typically utilize a
static advertising technique that involves placing each
advertisement in one site throughout game play. As such, the
location of the advertisement cannot change or move and other
advertisements cannot take its place. Thus, although there may be
multiple advertisements in one game, each advertisement can only
occupy a single location throughout the entire game. This is
undesirable because it lacks the ability to maximize the effect of
the advertisement on the gamer.
[0004] One way to increase the effectiveness of the advertisement
on the gamer is to utilize real-time dynamic advertising techniques
which allow for the targeting of advertisements to specific gamers
or groups of gamers. These dynamic advertising techniques allow
multiple advertisements from different advertisers to be rotated
through the same site during game play. Moreover, these dynamic
advertising techniques allow for different content types, such as
Billboard, Logo, Video, Audio and Beacons, to be used to display
advertisements to the gamer. Each of these content types is capable
of receiving and displaying multiple advertisements throughout the
game for display to the gamer. For example, a racing game may have
a billboard display advertising one product as the racing car goes
around the curve and passes the billboard. However, subsequent
times the race car goes around the curve and passes the billboard,
entirely different advertisements may be displayed. Thus, dynamic
advertising not only enhances the reality of the game's content, it
maximizes the revenue generating capability of the software product
by generating multiple revenue streams, as opposed to one revenue
stream generated using static advertising techniques.
[0005] Unfortunately however, some problems currently exist with
current approaches to in-game advertising. For example, because it
is very difficult to determine the number of available users (i.e.
gamers) and/or impressions that a game title is capable of
delivering, it is very difficult to efficiently and effectively
target advertisements to a specific audience.
SUMMARY OF THE INVENTION
[0006] A method for forecasting a performance characteristic of a
game title is provided and includes selecting base game-play data
for the game title, wherein the base-game play data is at least
partially responsive to the game-play pattern of a user, generating
base sales data for the game title responsive to initial sales data
and generating forecast data for the game title responsive to the
base sales data and the base game-play data.
[0007] A system for implementing a method for forecasting the
performance of a game title is provided, where the includes a means
for selecting base game-play data for the game title, wherein the
base-game play data is at least partially responsive to the
game-play pattern of a user, a means for generating base sales data
for the game title responsive to initial sales data and a means for
generating forecast data for the game title responsive to the base
sales data and the base game-play data.
[0008] A computer readable storage medium having computer
executable instructions for implementing a method for forecasting
the performance of a game title is provided, where the method
includes selecting base game-play data for the game title, wherein
the base-game play data is at least partially responsive to the
game-play pattern of a user, generating base sales data for the
game title responsive to initial sales data and generating forecast
data for the game title responsive to the base sales data and the
base game-play data.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] The foregoing and other features and advantages of the
present invention will be more fully understood from the following
detailed description of illustrative embodiments, taken in
conjunction with the accompanying figures in which like elements
are numbered alike:
[0010] FIG. 1 is a high level schematic block diagram illustrating
one embodiment of a gaming system, in accordance with the present
invention.
[0011] FIG. 2 is a lower level schematic block diagram illustrating
the integration server of the embodiment of the gaming system of
FIG. 1, in accordance with the present invention.
[0012] FIG. 3 is a high level block diagram illustrating one
embodiment of a method for forecasting a performance characteristic
of a game title, in accordance with the present invention.
[0013] FIG. 4 is a lower level block diagram illustrating the
method of FIG. 3, in accordance with the present invention.
[0014] FIG. 5 is a graph illustrating one embodiment of a
"game-play curve," in accordance with the present invention;
[0015] FIG. 6 is a graph illustrating another embodiment of a
"game-play curve," in accordance with the present invention;
[0016] FIG. 7 is a graph illustrating still another embodiment of a
"game-play curve," in accordance with the present invention;
[0017] FIG. 8 is a graph illustrating one embodiment of a "sales
forecast curve," in accordance with the present invention;
[0018] FIG. 9 is a graph illustrating one embodiment of a "seasonal
sales modifier curve," in accordance with the present
invention;
[0019] FIG. 10 is a graph illustrating one embodiment of a "day of
week modifier curve," in accordance with the present invention;
[0020] FIG. 11 is a graph illustrating one embodiment of a "hours
played/day curve," in accordance with the present invention;
[0021] FIG. 12 is a graph illustrating one embodiment of "monthly
sales information," in accordance with the present invention;
[0022] FIG. 13 is a graph illustrating one embodiment of a
"seasonal sales modifier," in accordance with the present
invention;
[0023] FIG. 14 is a graph illustrating one embodiment of a "sale
forecast," in accordance with the present invention;
[0024] FIG. 15 is a graph illustrating one embodiment of a "final
sales curve," in accordance with the present invention;
[0025] FIG. 16 is a graph illustrating one embodiment of a
"life-cycle curve," in accordance with the present invention;
[0026] FIG. 17 is a graph illustrating one embodiment of a
"life-cycle curve," with iterations, in accordance with the present
invention
[0027] FIG. 18 is a graph illustrating one embodiment of a "base
life-cycle curve," in accordance with the present invention;
[0028] FIG. 19 is a graph illustrating one embodiment of a
"day-of-week modifier curve," in accordance with the present
invention;
[0029] FIG. 20 is a graph illustrating one embodiment of a
"life-cycle curve," in accordance with the present invention;
and
[0030] FIG. 21 is a graph illustrating one embodiment of an
"impression forecast curve," in accordance with the present
invention.
DETAILED DESCRIPTION
[0031] The present invention allows for the accurate determination,
or forecasting, of an audience for a game title over a given time
period, such as for example the game title "GameX" for the month of
April of a specific year. Thus, forecasting provides an accurate
estimate as to the number of available users and impressions that a
specific game title is capable of delivering. It should be
appreciated that as referred to herein, advertising content means
any type of advertising content, including but not limited to
3-Dimensional and/or holographic content.
[0032] In accordance with the present invention, one way
forecasting may be accomplished is via algorithms that determine
with a great deal of precision the audience for a title over a
desired time period. This information may then be used, along with
weighting information, to increase the effectiveness of an
advertising campaign by delivering a requested number of
impressions to a specifically targeted audience. It should be noted
that the impression numbers are typically dependent upon the users
making the forecasting accuracies only one component to success in
in-game advertising. Accordingly, a sophisticated analytic engine
is provided herein that is capable of accurately predicting out
months (and/or years) at a time how many users and impressions a
title is capable of delivering. This analytic engine helps
advertisers plan an advertising campaign with an accurate degree of
certainty, wherein the analytic engine may include a data warehouse
that uses an analytical approach, such as an OLAP, to provide data,
such as a comprehensive matrix of data, for a specific purpose,
such as reporting and/or forecasting purposes.
[0033] The algorithm(s) used in forecasting may take into account
seasonal game-play variations (i.e. variations in game-play
activity due to seasons (summer, winter, fall, spring) and holidays
(people tend to play more during holidays)) as well as day-of-week
variations (day-of-week variations tend to be similar, but people
tend to play more on weekends), using actual and/or predicted
performance data as a feedback loop to modify forecasts on an
ongoing dynamic basis, using actual and/or predicted data from
similar games to generate forecast data and/or using pre-sales
and/or post-sales data to determine the expected number of users
that may be playing the game. One embodiment of the feedback loop
works as follows: generate a forecast of the number of users, get
actual user numbers and plug both of these values into the
algorithm (and/or equation) to help generate more accurate data.
This more accurate data can then be used to forecast similar games
of similar genres, say for example two types of first-person
shooter games.
[0034] It should be appreciated that the present invention is
capable of determining inventory availability more precisely than
current methods of random guessing. Additionally, the invention can
also take into account marketing budgets, "buzz," title genre,
setting and/or game-play attributes, and/or uses one of a set of
base game-play curves that describes the game-play pattern of an
average user of that type of game. This base game-play curve can
then be extrapolated to generate a life-cycle curve for the game,
where the life-cycle curve is a larger curve created by combining
sales forecasts (that are themselves determined by above factors).
This life-cycle curve indicates the number of users expected to be
playing a game for any date desired, such as a date within the
game's life-time. An impression curve can also be generated based
on testing of the game title. This impression curve is used to
determine the number of impressions delivered within a typical
session of game play. The impression curve can be combined with the
life-cycle curve to provide the expected impressions for any date
or time frame desired, such as that within the game's life-time. As
actual game-play data is generated and/or recorded, this
information can be fed back into the system (via a feedback loop)
and used to adjust the forecast and/or delivery of content. It
should be appreciated that theoretically there is a point where the
forecast data and actual data converge to approximately (or
exactly) the same values. This information may then be used to plan
effective advertising campaigns.
[0035] In accordance with the present invention, although the
concepts as discussed herein are discussed with regards to a gaming
environment as follows, any type of gaming environment or
configuration may be used. Referring to FIG. 1, one embodiment of a
gaming system 10 for implementing the method of the invention
showing the connectivity between the elements is shown and includes
a user gaming device 20 having gaming software 30 and application
software (SDK) 40, a gaming server 50 (optional) and an integration
server 60 which includes advertiser information 70. In accordance
with the present invention, a gaming server is optional and the
game may be wholly or partially implemented via one or more
computer(s) and/or gaming device(s) as desired. During gameplay,
the gaming software 30 communicates with the gaming server 50
(optional) to facilitate the gameplay and the SDK 40 communicates
with the integration server 60 to facilitate the integration of
advertising content. Referring to FIG. 2, a lower level block
diagram illustrating the elements of the integration server 60. As
shown, the interaction within the integration server 60 is
illustrated by a first set of arrows 75 which represents the flow
of impressions through the integration server 60, a second set of
arrows 80 which represents the flow of advertising content through
the integration server 60 and a third set of arrows 85 which
represents the flow of control messages (i.e. figuring out a user
location, start session message, etc.) through the integration
server 60.
[0036] In accordance with the present invention, one embodiment of
a method 300 for forecasting the number of available users and/or
impressions that a specific game title is capable of delivering is
discussed hereinafter with regards to the performance of a specific
game title in relation to the number of available users and
impressions over the lifetime of the game (i.e. the title's
forecast) and is illustrated as shown in FIG. 3 and FIG. 4. The
method 300 includes selecting a base "game-play curve" for the game
title from a set of pre-generated "game-play curves," (see FIG. 5,
FIG. 6, and FIG. 7 for examples) as shown in operational block 302,
where the "game-play curve" is representative of how many hours a
single average user would most likely play the game per day from
the time the game was purchased/received until the end of the life
of the game (i.e. no longer played).
[0037] The pre-generated "game-play curves" may be provided by the
game publisher or generated based on test data, historical data,
estimated data and/or predicted data as desired, such as for
example a game title, genre and/or age group. The "game-play curve"
may be selected from the set of pre-generated "game-play curves"
based on one or more desired parameters, such as common
characteristics between a specific pre-generated "game-play curve"
and the game title being forecasted. For example, the pre-generated
"game-play curves" may include a curve that is representative of an
action game genre which involves a fantasy science fiction theme
and that is targeted to the 15-18 year old age group. If the game
title being forecasted is for an action game genre that is targeted
to the 15-18 year old age group, then the aforementioned game-play
curve may be selected. Additionally, if the pre-generated
"game-play curves" also include a curve that is representative of
an action game genre which involves a non-fantasy science fiction
theme and that is targeted to the 15-18 year old age group, this
game-play curve may be selected.
[0038] It is also contemplated that selected curves in the
pre-generated set of "game-play curves" may be combined and/or used
together to generate forecast data. Accordingly, the selected
"game-play curve" may be selected based on various attributes of
the title, including (but not limited to): Genre (i.e. Action,
Driving, Shooter, Role-Playing), Distribution Type (i.e. Retail,
Budget, Demo), Game-play (i.e. Single Player, Multiplayer), and
Setting (i.e. Fantasy, Historic, Sci-Fi). This is possible because
each attribute (or combination thereof) typically lends itself to
different playing habits, which may ultimately be used to determine
how many hours and at what frequency the game is played.
[0039] A base "sales curve" is also created, as shown in
operational block 304, wherein the base "sales curve" is
representative of how many units are expected and/or estimated to
be sold during the life-time of the game and may be broken down
into specific time periods, such as individual days. This helps to
determine how many units of the game title are available and is
usually directly related to the number of users available
(typically a one-to-one relationship although the ratio may be
different), as well as how long the game will be available.
Creation of the base "sales curve" may be accomplished by taking
sales forecast data (which may be furnished by the game publisher
or obtained via other methods) (see FIG. 8 for example), actual
sales data from previous versions of the game (if the game is a
sequel), and/or actual sales data from similar games (i.e. games
that may have the same or similar attributes used in determination
of the "game-play curve"). Actual sales information may be
generated by the user, provided by some entity that tracks such
sales and/or provided by the publishers themselves. All or some of
this data may be combined to form the "base sales curve," where a
weighted average may or may not be used.
[0040] The "base sales curve" may be modified to produce a "final
sales curve," as shown in operational block 306, wherein the
modifier may be based on any number of desired characteristics,
such as factors which affect the number of units "sold" over a
period of time. For example, since the release date of the game is
typically known in advance, the "sales curve" can be fixed to a
specific period of time where Day 0 of the curve indicates the
release date of the game. Accordingly, one modification to the
"sales curve" may include adjustments to account for seasonal sales
trends. One way this may be accomplished is by using a "seasonal
sales modifier curve" (see FIG. 9 for example), where the "seasonal
sales modifier curve" is a set of data (fixed or variable) that
indicates representative for each day of the year (e.g. 1 through
365) of whether the game sales will be higher or lower than
average, with a value of 1.0 typically indicating average. This
modifier curve may be shifted (and repeated) so that the days of
the year match those of the "fixed sales curve" and the values of
the two curves can then be combined, for example multiplied
together. Furthermore, it is contemplated that other modifier
curves may be used to adjust the "base sales curve" in the same or
similar manner, including but not limited to: a "marketing modifier
curve" that indicates how the amount spent and methods of marketing
the game title will affect the sales of the game over time, a "buzz
modifier curve" that indicates how media attention to the game
title will affect the sales of the game over time, a "pricing
modifier curve" that indicates how adjustments in the pricing of
the game title will affect the sales over time and a "piracy
modifier curve" that indicates how rates of software piracy will
typically affect the number of units of the game being played over
time.
[0041] At this point, the "final sales curve" and the selected
"base game-play curve" are combined to create a "final life-cycle
curve," as shown in operational block 308, which provides an
indication of how many hours per day the game is expected to be
played throughout its lifetime. The "final life-cycle curve" can be
generated by iterating over a specific time period in the "final
sales curve," for example each day, taking the number of units
expected to be "sold" on that day and multiplying the selected
"game-play curve" by that number of units and plotting the results
of the "final life-cycle curve" starting at the day of iteration.
Next, the "forecast data" is generated, as shown in operational
block 310, and may be accomplished by multiplying one or more
additional modifiers in the same way that the "seasonal sales
modifier curve" is used to modify the "base sales curve." These
modifiers however relate the number of hours played to time
(instead of units "sold" to time), and may be generated and
formatted similarly to the sales modifier curves. One such modifier
may be the "day of week modifier curve" (see FIG. 10 for example)
which indicates how the day of week on which the game is played
will affect the number of hours that a user will play the game. It
is contemplated that at this stage if no modifiers are desired,
then the "final life-cycle curve" can be interpreted as the
forecast data. The resultant forecast data is indicative of the
number of available users that the game title will most likely
generate.
[0042] It should be appreciated that before a game title is
released, it may undergo a testing period that determines how many
impressions per hour on average the title will be expected to
generate. This may be accomplished in any number of ways, such as
by playing the game as a normal user would and counting the number
of impressions generated during each hour period, or through some
other acceptable method. The "final life-cycle curve" is then
multiplied by the impressions per hour value, to produce the
forecast data for the number of available impressions that the game
title will typically generate on a daily basis. Once the game title
is released, the actual performance of the game title may not match
that of the forecast values, so the forecast for future days may be
modified to take this discrepancy into account. Actual performance
data may include data sent to the system by the title and may
include counts of impressions and/or users and may be stored in a
database that allows easy access and/or search capabilities. This
performance data can then be combined with the original forecast
data for available users and impressions, by using a weighted
average of the forecast and/or actual data, where the weight of the
forecast data may initially be much higher than that of the actual
data, but will typically decrease over time as the weight of the
actual data increases over time as more actual data is accrued
(i.e. the longer the game title is out, the more actual data is
obtained). This process of adjusting the forecast can be repeated
continuously (or in a predetermined fashion) as data is received
for the game title. Typically, the forecast data and actual data
will eventually converge within a small margin of error, such that
the forecast data may be very close to what the game title will
actually deliver.
[0043] In accordance with the invention, the method 300 for
forecasting is illustrated with regards to the following example
which assumes that the game title for which the forecast is being
generated is directed to a single-player, shooter game set in the
present day that will be distributed through retail channels
starting at a predetermined time. Based on these attributes, a
particular "game-play curve" is chosen as described hereinbefore.
Typically performed by an analyst familiar with game-play styles,
this selection may be based on only one characteristic of the game
title. For this example, a curve as shown in FIG. 11 is chosen.
This curve may be selected from a set of "standard" and/or
pre-generated "game-play curves," each of which provides for
different types, genres and patterns of play. The curve that was
selected is one in which game-play starts out at a constant level,
and falls gradually over time. For this example, it is assumed that
an average player will play the game for 33 days, with a maximum
daily game-play of 5 hours and as such, the selected curve has been
scaled to conform to these values.
[0044] The next (or preceding or concurrent) step in forecasting
data for this game title is to combine data collected from various
sources to generate a predictive "base sales curve." It is
contemplated that if no combination of data is desired then the
original data could be used for the "base sales curve" or that no
data is available, then the "base sales curve" could be generated
with data already obtained at this point. Referring to FIG. 12,
monthly sales information used to generate the sales forecast
(shown as a thicker line) is illustrated and may combine
information on sales of a previous version of the game, sales of
similar games and/or sales predictions provided/generated by the
publisher or other entity. In this example, these three sets of
data are combined using a weighted average to generate the sales
forecast. The data has been weighted such that the sales forecast
is approximately 50% of the sales of the previous game, 30% of the
sales of similar games, and 20% of the publisher sales predictions.
As shown in FIG. 12, for this example in month 0 the sales for the
previous game version was 25,000 units, the sales for similar game
titles were 30,000 units and the publisher prediction was 30,000
units. Given these values the forecast for month 0 is equal to
27,500 units (i.e. (25,000*0.50)+(30,000*0.30)+(30,000*0.20)=27,500
units). It is contemplated that other methods for generating weight
values may also be used.
[0045] At this point, the "base sales curve" is modified to
generate a "final sales curve." It should be appreciated that
although for this example only one modifier was used to show the
process, any number of modifiers (include zero) may be applied in
the same manner. For this example a "seasonal sales modifier," as
illustrated by the graph shown in FIG. 13, was used, where the
"seasonal sales modifier" indicates how sales are affected by the
time of year. However, the sales forecast may be fixed to a
specific period of time. This is possible because the date the game
will be released is typically known and may correspond to the
beginning of month 0 of the sales forecast as shown in FIG. 14.
[0046] The modifier curve may be applied to the sales forecast, for
example one way may include aligning the time periods on both
curves, and replicating the modifier curve to span the entire
period of the forecast. Referring to FIG. 15, the "final sales
curve" (i.e. the thicker line) is generated by multiplying the
"base sales curve" by the "modifier curve." At this point, the
life-cycle curve is created by combining the game-play curve and
the final sales curve. This process may be better understood and
illustrated by describing the process of combining the curves,
rather than by showing it in graphical form, where the process is
an iteration over every day in the "final sales curve" (e.g. the
time period in this example). For each day, the "game-play curve"
may be multiplied by the number of units to be sold that day. The
monthly values of the sales curve may then be extrapolated to daily
values by spreading them evenly throughout the month (i.e. 30,000
units in the month of April would amount to 1,000 units sold each
day on average). It is contemplated that this may be accomplished
via any mathematical method to more accurately match the slope of
the sales curve. In this example, although the monthly values are
distributed evenly throughout the month to simplify the process, it
is contemplated that these may be distributed any way suitable to
the desired end purpose.
[0047] For example, if sales for the month of January for a given
year are predicted to be 33,000 units, it would be expected that
about 1,065 units per day would be sold that month (i.e.
33,000/31). The first day of the set over which we are iterating is
January 1.sup.st, so the "game-play curve" is multiplied by that
number of units to be sold on January 1.sup.st (i.e. 1,065) which
will produce a curve as shown in FIG. 16. This resultant curve may
be viewed as the start of our life-cycle curve. The second day of
the iteration is January 2.sup.nd, with another 1,065 units
expected to be sold. Again, the "game-play curve" is multiplied by
that value and the resultant value is added to our life-cycle curve
with day 0 indicating January 2.sup.nd, as shown in FIG. 17. This
iteration is continued until the "final life-cycle curve" is
created, as shown in FIG. 18.
[0048] Next, modifiers to the "final life-cycle curve" may be
applied in the same manner as was done with the "final sales curve"
to generate the forecast data. Referring to FIG. 19, for this
example a "day of week modifier" is applied to the "final
life-cycle curve." It should be appreciated that the overall
resultant final curve (forecast data) is difficult to illustrate
since the modifier causes the curve to have peaks and valleys on a
weekly cycle. However, for illustrative purposes a section of the
resultant final curve is shown in FIG. 20 for the first month of
the life-cycle, in this case January. At this point in the
forecasting process the number of hours expected to be played may
be converted to a value which indicates the number of impressions
expected. For this example, it is assumed that the game title being
forecast has been tested and generates an average of seven (7)
impressions for each hour that it is played. To perform the
conversion for this example, the "final life-cycle curve" is
multiplied by this value (i.e. "7"). The resulting "impression
forecast curve" is shown in FIG. 21 and typically indicates the
number of impressions expected per day.
[0049] It should be appreciated that once the game title is
released, the actual impression and/or user data that is collected
may be compared to the predicted impressions and/or user values
(i.e. the "final sales curve," "impression forecast curve," etc.).
This may be done by taking representative samples of users playing
the game, generating an "actual game-play curve" and/or generating
an "actual sales curve." These curves may then undergo the same
procedure as described hereinbefore, with the possible exception of
the modifiers since the modifiers may be implicit in the actual
data, to create an "actual impression curve." However, it is
contemplated that modifiers may or may not be used as desired. The
resulting curve may then be combined with the "impression forecast
curve" using a weighted average (in the same or similar manner as
for creating the "base sales curve"). As discussed hereinbefore,
the weighting values for this average may start out highly in favor
of the forecast curve and decrease as time goes on (e.g. 95% to 5%)
since very little actual data is immediately available. Since this
process occurs on a regular basis (perhaps at least once a day),
the amount of actual data will typically increase, as will it's
reliability. Over time, as more actual data is obtained, the
weighting values for the forecast curve may be reduced while the
weighting value for the actual curve may be increased until they
each reach approximately 50% each. The result from this averaging
may then become the new forecast curve.
[0050] It should be appreciated that the method of the present
invention may be embodied, in whole or in part, via software,
firmware and/or hardware, and that that any type of application
software may be used to practice the present invention. Moreover,
the invention may be implemented via any type or configuration of
software suitable to the desired end purpose, such as a generic SDK
and/or an application specific SDK. Furthermore, the software
application may or may not be embedded, in whole or in part.
Additionally, it should also be appreciated that the method of the
present invention may or may not be embodied, in whole or in part,
via instruction using training manuals (i.e. text based materials),
seminars, classes, and/or any other media suitable to the desired
end purpose. Moreover, it should be appreciated that although the
method of the present invention may be implemented, in whole or in
part, via software, hardware, firmware and/or any combination
thereof, it is also contemplated that the method of the present
invention may also be implemented, in whole or in part, without the
use of software, hardware, firmware and/or any combination thereof.
For example, without the full or partial use of any software,
hardware and/or firmware and/or with any combination thereof, but
rather via instruction using PC based software and/or classroom
instruction with text materials (i.e. books, pamphlets, handouts,
tapes, optical media, etc.).
[0051] Moreover, it should be appreciated that each of the elements
of the present invention may be implemented in part, or in whole,
in any order suitable to the desired end purpose. In accordance
with an exemplary embodiment, the processing required to practice
the method of the present invention, either in whole or in part,
may be implemented, wholly or partially, by a controller operating
in response to a machine-readable computer program. In order to
perform the prescribed functions and desired processing, as well as
the computations therefore (e.g. execution control algorithm(s),
the control processes prescribed herein, and the like), the
controller may include, but not be limited to, a processor(s),
computer(s), memory, storage, register(s), timing, interrupt(s),
communication interface(s), and input/output signal interface(s),
as well as combination comprising at least one of the foregoing. It
should also be appreciated that the embodiments disclosed herein
are for illustrative purposes only and include only some of the
possible embodiments contemplated by the present invention.
[0052] Furthermore, the invention may be wholly or partially
embodied in the form of a computer or controller implemented
processes. It should be appreciated that any type of computer
system (as is well known in the art) and/or gaming system may be
used and that the invention may be implemented via any type of
network setup, including but not limited to a LAN and/or a WAN
(wired or wireless). The invention may also be embodied in the form
of computer program code containing instructions embodied in
tangible media, such as floppy diskettes, CD-ROMs, hard drives,
and/or any other computer-readable medium, wherein when the
computer program code is loaded into and executed by a computer or
controller, the computer or controller becomes an apparatus for
practicing the invention. The invention can also be embodied in the
form of computer program code, for example, whether stored in a
storage medium, loaded into and/or executed by a computer or
controller, or transmitted over some transmission medium, such as
over electrical wiring or cabling, through fiber optics, or via
electromagnetic radiation, wherein when the computer program code
is loaded into and executed by a computer or a controller, the
computer or controller becomes an apparatus for practicing the
invention. When implemented on a general-purpose microprocessor the
computer program code segments may configure the microprocessor to
create specific logic circuits.
[0053] While the invention has been described with reference to an
exemplary embodiment, it should be understood by those skilled in
the art that various changes may be made and equivalents may be
substituted for elements thereof without departing from the scope
of the invention. In addition, many modifications may be made to
adapt a particular situation or material to the teachings of the
invention without departing from the scope thereof. Therefore, it
is intended that the invention not be limited to the particular
embodiment disclosed as the best mode contemplated for carrying out
this invention, but that the invention will include all embodiments
falling within the scope of the appended claims. Moreover, unless
specifically stated any use of the terms first, second, etc. do not
denote any order or importance, but rather the terms first, second,
etc. are used to distinguish one element from another.
* * * * *