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 Features
From
Casual to Core: A Statistical Mechanism for Studying Gamer Dedication
Weightings
Clearly, some of the factors mentioned above are more important than others.
For example, a factor such as "play over long sessions frequently"
is more important, than say, "prefer games that have depth and complexity."
The latter is merely an individual preference with regard to the design
of the game, and not the degree to which gaming is pursued as a hobby.
In order to take into account this varying degree of importance, weightings
should be attached to each factor during classification (where higher
weights indicate greater relative importance). Moreover, in applying the
classification procedure, weightings can be determined arbitrarily according
to the importance the marketer perceives them to hold for his own purposes.
Table 2 below shows the 15 Factors and their corresponding weightings
according to our interpretation of their degree of importance.
| Factor
|
Weighting
|
| 1.
Play games over many long sessions |
10
|
| 2.
Discuss games with friends/bulletin boards |
10
|
| 3.
Comparative knowledge of the industry |
10
|
| 4.
Much more tolerant of frustration |
9
|
| 5.
Indications of early adoption behaviour |
9
|
| 6.
Desire to modify or extend games in a creative way |
8
|
| 7.
Technologically savvy |
7
|
| 8.
Have the latest high-end computers/consoles |
7
|
| 9.
Play for the exhilaration of defeating (or completing) the game |
7
|
| 10.
Hunger for gaming-related information |
6
|
| 11.
Engaged in competition with himself, the game, and other players |
6
|
| 12.
Willingness to pay |
5
|
| 13.
Prefer games that have depth and complexity |
3
|
| 14.
Time started playing games relative to the age of the industry |
2
|
| 15.
Prefer violent/action games |
1
|
Table
2: The 15 Factors of Classification and associated weightings
(ranked according to weight)
Calculating
Gamer Dedication
Application of the 15 factors requires the use of measurement scales with
which to record the responses given by the subjects. There are many varieties
of measurement scales, each with their own unique advantages and disadvantages,
and varying levels of complexity. For this example, we have decided to
assume that the raw data for each factor will be normalized to a scale
of 1 to 5. This corresponds to the familiar Likert scale, widely used
in questionnaire and survey experiments, in which subjects are asked whether
they "strongly disagree," "disagree," "neither
disagree or agree," "agree," or "strongly agree"
with a series of statements.
Suppose
that gamer X provided the following normalized data for the 15 factors:
| Factor
|
Weighting
|
Normalized
Data
|
| 1.
Play games over many long sessions |
10
|
3
|
| 2.
Discuss games with friends/bulletin boards |
10
|
4
|
| 3.
Comparative knowledge of the industry |
10
|
4
|
| 4.
Much more tolerant of frustration |
9
|
5
|
| 5.
Indications of early adoption behaviour |
9
|
4
|
| 6.
Desire to modify or extend games in a creative way |
8
|
5
|
| 7.
Technologically savvy |
7
|
3
|
| 8.
Have the latest high-end computers/consoles |
7
|
4
|
| 9.
Play for the exhilaration of defeating (or completing) the game |
7
|
2
|
| 10.
Hunger for gaming-related information |
6
|
3
|
| 11.
Engaged in competition with himself, the game, and other players |
6
|
2
|
| 12.
Willingness to pay |
5
|
3
|
| 13.
Prefer games that have depth and complexity |
3
|
2
|
| 14.
Time started playing games relative to the age of the industry |
2
|
2
|
| 15.
Prefer violent/action games |
1
|
1
|
Table
3: Data obtained from gamer X
From the
above information, the overall gamer-dedication score (GD) can be calculated
as:
Where n=15;
s = self-ranked score; and w = weight
GD for gamer
X is therefore:
Interpretation
As mentioned earlier, the weighting attached to each factor is arbitrary
depending on the person using them. However, further research (such as
an extensive survey or poll to obtain the views of industry experts or
gamers themselves) could be conducted in order to establish standardised
weightings. From these, one can easily obtain measures from individuals
regarding their overall attitude towards gaming. It is reasonable to assume
that the higher the overall score, the stronger the evidence for classifying
a person as a hardcore gamer and vice versa. However, the most teasing
concern remains to be one of delicate and accurate interpretation of overall
scores, particularly those which are neither leaning towards the casual
or hardcore segment. In the absence of actual data, we hypothesize the
existence of five possible categories that a person can potentially fit
into based on their score. Diagram 1 below illustrates this.
Ultra
casual or non-gamers. A person obtaining a low score from the 15 Factors
of Classification could be a casual gamer or even a non-gamer. "Ultra
casual" gamers have great potential for further exploitation; they
have clearly demonstrated at least some interest in gaming. However, much
needs to be done to determine the factors that turn the "ultra casual"
into the "casual". Gamers included in this category, could,
in theory, incorporate any section of the demographic ranging from 8 year-olds
to old-age pensioners. As long as the person has some kind of experience
with games or interest in them, he or she is likely to be a potential
customer. Non-gamers may or may not be potential customers; it depends
on whether the reason that they don't play at the moment is a total disinterest
and rejection of games, or a lack of information and opportunities to
play. Further survey questions could distinguish between these groups,
and those subjects who do not and will not ever play should be eliminated
from the dataset.
Casual.
With a higher score than the above category, but lower than that of the
preceding ones, casual gamers show a mild response to the 15 Factors.
Casual gamers are not ignorant or indifferent about games, but simply
show a reserved level of interest.
Transitional/moderate.
Not to be confused with other categories, the "transitional/moderate"
segment is used to describe those who obtained a relatively neutral score.
There are potentially two types of gamer who reside in this category.
"Moderate" gamers would generally have greater knowledge and
experience of games than the preceding categories, but don't necessarily
have the latest games or keep up with news about the game industry itself.
The term "transitional" is used here to describe gamers who
obtained a relatively neutral score because their habits are in transition
from the "casual" to the "hardcore" segment, or vice
versa.
Hardcore.
Those with consistent scores for all 15 factors show a strong indication
of being a hardcore gamer. These gamers are likely to possess greater
gaming-related knowledge and experience, as well as spending considerably
more time and resources on games than the preceding categories.
Ultra
hardcore (obsessive). Few gamers fall into this category. However,
those who do are likely to take the hobby of gaming very seriously, and
devote significant resources to it - even more so than "hardcore"
gamers.
The proportions
of each of the gamer-segments shown in Figure 1 are merely estimates.
The real significance of the diagram is that it demonstrates that gamer
dedication is a continuum, not a dichotomy. We think it likely that there
are five useful categories of gamer, though an actual poll may reveal
more or fewer in the form of peaks in the graph. It seems probable that
the categories of "ultra casual" and "casual" comprise
the largest population of gamers, as compared to "transitional/moderate",
"hardcore" and "ultra hardcore" gamers.
Clearly,
more work is required in several areas:
- A proper
survey must be designed with suitable questions that yield unambiguous
quantitative results.
- If gamer
dedication is to become a standardized measure for marketing purposes,
then standard weightings for each of the 15 factors must be agreed upon
by the game marketing community.
- The results
of an actual poll must be studied in order to determine how many useful
categories of gamers there really are, and what their scoring intervals
are.
- Once
a useful and group of gamer categories has been identified, the next
step is to determine how important each category is to the industry.
Once this is achieved, effective strategies can thus be developed to
attract the appropriate customer segments.
Once these
steps have been taken, much research needs to be done in order to establish
the probability of gamers migrating from one segment to another, and the
causal factors leading to such behaviour. Whether or not the five consumer-segments
we have proposed really exist, we are confident that certain patterns
will emerge from a proper study - ultimately exemplifying the variation
in consumer tastes and attitudes towards interactive entertainment.
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