The Gamer Motivation Profile: Model and Findings

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Nick Yee (Co-founder & Analytics Lead at Quantic Foundry - Game Analytics Consulting Practice) presents the results of a global survey that help inform game developers about the different motivations of players, showing the different types of potential customer that exist in the video game market.

Source:
The Gamer Motivation Profile: Model and Findings
(no direct link to watch/listen)
(direct link to watch/listen)
Type:
Presentation
Publication date:
February 9, 2017
Added to the Vault on:
April 22, 2021
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💎 #
1

The gamer motivation model has 12 motivations that group into 6 pairs (within which motivations are more correlated). See here.

04:39
💎 #
2

At the a high level, there are 3 motivation clusters (cf. Motivation Map here):
1. Action-Social
2. Mastery-Achievement
3. Immersion-Creativity

06:48
💎 #
3

The Discovery and Power motivations do not really fit in any cluster and seem to be “bridges”:
- Discovery is a bridge between Immersion-Creativity and Mastery-Achievement
- Power is a bridge between Action-Social and Mastery-Achievement

07:40
💎 #
4

This structure/pattern of 3 clusters is stable across all the regions Quantic Foundry has data for.

07:57
💎 #
5

These motivation clusters relate to existing traits in psychology i.e. the “big five” personality factors:
1. Openness to Experience
2. Conscientiousness
3. Extraversion
4. Agreeableness
5. Neuroticism

08:30
💎 #
6

People who are outgoing and extraverted tend to be more cheerful, more optimistic, more excited and thrill-seeking and tend to be more assertive in group situations. This explains why the Action and Social motivations are tied: they are an expression of personality in the gaming context

10:00
💎 #
7

Conscientiousness is the personality trait for how organized and self-disciplined you are. It maps to the long-term-thinking-oriented motivations and therefore the Mastery-Achievement cluster.

10:24
💎 #
8

Openness is how imaginative, how curious and how unorthodox you are. It maps to the Immersion-Creativity cluster.

10:37
💎 #
9

Games are more of an identity management tool rather than an escape. People play games that align with their personality traits.

12:25
💎 #
10

The most common primary motivations for women are Fantasy, Design and Completion. For men they are Destruction, Competition, Strategy and Challenge.

13:21
💎 #
11

However a lot of the gender differences are dwarfed by age differences. Examples: past the age of 35, there isn’t really a difference in appeal for competition between men and women. Age also explains twice the variance that gender does.

13:50
💎 #
12

We should think more about what games for different age groups should look like (rather than the differences between men and women).

15:26
💎 #
13

The Action-Social group is the one that varies the most dramatically with age:
- For men all the motivations of the cluster (Competition, Excitement, Challenge, Community) fall dramatically with age
- For women, younger women are a lot more interested in story/narrative than older women are

15:58
💎 #
14

There is a cognitive threshold above which there is too much conflict between Excitement and Strategy, and you need to think about the tradeoff between strategic complexity and reaction time. Example: if it’s a highly strategic game like Europa Universalis, you need the time to think about the decisions which lowers excitement. 

20:06
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💎 #
1

The gamer motivation model has 12 motivations that group into 6 pairs (within which motivations are more correlated). See here.

04:39
💎 #
2

At the a high level, there are 3 motivation clusters (cf. Motivation Map here):
1. Action-Social
2. Mastery-Achievement
3. Immersion-Creativity

06:48
💎 #
3

The Discovery and Power motivations do not really fit in any cluster and seem to be “bridges”:
- Discovery is a bridge between Immersion-Creativity and Mastery-Achievement
- Power is a bridge between Action-Social and Mastery-Achievement

07:40
💎 #
4

This structure/pattern of 3 clusters is stable across all the regions Quantic Foundry has data for.

07:57
💎 #
5

These motivation clusters relate to existing traits in psychology i.e. the “big five” personality factors:
1. Openness to Experience
2. Conscientiousness
3. Extraversion
4. Agreeableness
5. Neuroticism

08:30
💎 #
6

People who are outgoing and extraverted tend to be more cheerful, more optimistic, more excited and thrill-seeking and tend to be more assertive in group situations. This explains why the Action and Social motivations are tied: they are an expression of personality in the gaming context

10:00
💎 #
7

Conscientiousness is the personality trait for how organized and self-disciplined you are. It maps to the long-term-thinking-oriented motivations and therefore the Mastery-Achievement cluster.

10:24
💎 #
8

Openness is how imaginative, how curious and how unorthodox you are. It maps to the Immersion-Creativity cluster.

10:37
💎 #
9

Games are more of an identity management tool rather than an escape. People play games that align with their personality traits.

12:25
💎 #
10

The most common primary motivations for women are Fantasy, Design and Completion. For men they are Destruction, Competition, Strategy and Challenge.

13:21
💎 #
11

However a lot of the gender differences are dwarfed by age differences. Examples: past the age of 35, there isn’t really a difference in appeal for competition between men and women. Age also explains twice the variance that gender does.

13:50
💎 #
12

We should think more about what games for different age groups should look like (rather than the differences between men and women).

15:26
💎 #
13

The Action-Social group is the one that varies the most dramatically with age:
- For men all the motivations of the cluster (Competition, Excitement, Challenge, Community) fall dramatically with age
- For women, younger women are a lot more interested in story/narrative than older women are

15:58
💎 #
14

There is a cognitive threshold above which there is too much conflict between Excitement and Strategy, and you need to think about the tradeoff between strategic complexity and reaction time. Example: if it’s a highly strategic game like Europa Universalis, you need the time to think about the decisions which lowers excitement. 

20:06
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💎 #
1

The gamer motivation model has 12 motivations that group into 6 pairs (within which motivations are more correlated). See here.

04:39
💎 #
2

At the a high level, there are 3 motivation clusters (cf. Motivation Map here):
1. Action-Social
2. Mastery-Achievement
3. Immersion-Creativity

06:48
💎 #
3

The Discovery and Power motivations do not really fit in any cluster and seem to be “bridges”:
- Discovery is a bridge between Immersion-Creativity and Mastery-Achievement
- Power is a bridge between Action-Social and Mastery-Achievement

07:40
💎 #
4

This structure/pattern of 3 clusters is stable across all the regions Quantic Foundry has data for.

07:57
💎 #
5

These motivation clusters relate to existing traits in psychology i.e. the “big five” personality factors:
1. Openness to Experience
2. Conscientiousness
3. Extraversion
4. Agreeableness
5. Neuroticism

08:30
💎 #
6

People who are outgoing and extraverted tend to be more cheerful, more optimistic, more excited and thrill-seeking and tend to be more assertive in group situations. This explains why the Action and Social motivations are tied: they are an expression of personality in the gaming context

10:00
💎 #
7

Conscientiousness is the personality trait for how organized and self-disciplined you are. It maps to the long-term-thinking-oriented motivations and therefore the Mastery-Achievement cluster.

10:24
💎 #
8

Openness is how imaginative, how curious and how unorthodox you are. It maps to the Immersion-Creativity cluster.

10:37
💎 #
9

Games are more of an identity management tool rather than an escape. People play games that align with their personality traits.

12:25
💎 #
10

The most common primary motivations for women are Fantasy, Design and Completion. For men they are Destruction, Competition, Strategy and Challenge.

13:21
💎 #
11

However a lot of the gender differences are dwarfed by age differences. Examples: past the age of 35, there isn’t really a difference in appeal for competition between men and women. Age also explains twice the variance that gender does.

13:50
💎 #
12

We should think more about what games for different age groups should look like (rather than the differences between men and women).

15:26
💎 #
13

The Action-Social group is the one that varies the most dramatically with age:
- For men all the motivations of the cluster (Competition, Excitement, Challenge, Community) fall dramatically with age
- For women, younger women are a lot more interested in story/narrative than older women are

15:58
💎 #
14

There is a cognitive threshold above which there is too much conflict between Excitement and Strategy, and you need to think about the tradeoff between strategic complexity and reaction time. Example: if it’s a highly strategic game like Europa Universalis, you need the time to think about the decisions which lowers excitement. 

20:06
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How the model was created

A factor analysis helped create clusters to be able to form the different gamer motivation profiles.

Then they reviewed the literature on the topic to extract the motivations shared from there.


They then did a first survey with 600 people to create the early model of motivations, before creating an online app. Here is an initial sample report:


What the gamer motivation model looks like now

[💎@04:39] The gamer motivation model has 12 motivations that group into 6 pairs (within which motivations are more correlated. See below.


[💎@06:48] At the a high level, there are 3 motivation clusters (cf. Motivation Map below):

  1. Action-Social
  2. Mastery-Achievement
  3. Immersion-Creativity



[💎@07:40] The Discovery and Power motivations do not really fit in any cluster and seem to be “bridges”:

  • Discovery is a bridge between Immersion-Creativity and Mastery-Achievement
  • Power is a bridge between Action-Social and Mastery-Achievement

[💎@07:57] This structure/pattern of 3 clusters is stable across all the regions Quantic Foundry has data for.


Motivations and personality traits

[💎@8:30] These motivation clusters relate to existing traits in psychology i.e. the “big five” personality factors:

  1. Openness to Experience
  2. Conscientiousness
  3. Extraversion
  4. Agreeableness
  5. Neuroticism

[💎@10:00] People who are outgoing and extraverted tend to be more cheerful, more optimistic, more excited and thrill-seeking and tend to be more assertive in group situations. This explains why the Action and Social motivations are tied: they are an expression of personality in the gaming context

[💎@10:24] Conscientiousness is the personality trait for how organized and self-disciplined you are. It maps to the long-term thinking oriented motivations and therefore the Mastery-Achievement cluster.

[💎@10:37] Openness is how imaginative, how curious and how unorthodox you are. It maps to the Immersion-Creativity cluster.


Quantic Foundry also surveyed some gamers on both personality traits and motivations to find correlations. However the correlations were fairly low, even though there are some connections.


[💎@12:25] Games are more of an identity management tool rather than an escape. People play games that align with their personality traits.

The data by demo


[💎@13:21] The most common primary motivations for women are Fantasy, Design and Completion. For men they are Destruction, Competition, Strategy and Challenge.



[💎@13:50] However a lot of the gender differences are dwarfed by age differences. Examples: past the age of 35, there isn’t really a difference in appeal for competition between men and women. Age also explains twice the variance that gender does.




[💎@15:26] We should think more about what games for different age groups should look like (rather than the differences between men and women).


[💎@15:58] The Action-Social group is the one that varies the most dramatically with age:

  • For men all the motivations of the cluster (Competition, Excitement, Challenge, Community) fall dramatically with age
  • For women, younger women are a lot more interested in story/narrative than older women are



They also had a question about the gamer's 3 favorite games, and then tried to see if they could build a game recommendations model and eventually a venn diagram.


Game profiles and coding

They also used the data to calculate the motivation profile for 1000 games.



[💎@20:06] There is a cognitive threshold above which there is too much conflict between Excitement and Strategy, and you need to think about the tradeoff between strategic complexity and reaction time. Example: if it’s a highly strategic game like Europa Universalis, you need the time to think about the decisions which lowers excitement. 

However games that are at the edge of that cognitive threshold can be great esports candidates.


You can look at a specific game and do a cluster analysis to surface the distinct personas (and their distribution) and the combinations of motivations come up the most.


Q&A

Relative share of each cluster?

They’ve done it by motivation but not by cluster.


Using the data possible?
This dataset is proprietary but they have older datasets available.


Any data below 13yo?

No, because it’s hard to do surveys. Other companies may have done it.


Mapping brain chemicals and neurobiology back to this?

This would take a lot of research and QF hasn’t gone into this.


How much data has biases because people know what’s happening

They skew towards a core gamer demographic which tends to bring more men and slightly younger database. But it's hard to identify the social media bias.


Any studies that are not self-administered?

They’re working with clients to target their gamers so they can check if what gamers say correlates with what they do. If they know the actual distribution of retention per cohort, etc. they can sample more accurately.


Younger women more interested in story/narrative than older women: is it just Western Europe?

They haven’t looked at that yet. It can/may vary by region.






The notes from this resource are only available to premium members.
↘ At this point, you know what to do ↙
Upgrade Your Plan

How the model was created

A factor analysis helped create clusters to be able to form the different gamer motivation profiles.

Then they reviewed the literature on the topic to extract the motivations shared from there.


They then did a first survey with 600 people to create the early model of motivations, before creating an online app. Here is an initial sample report:


What the gamer motivation model looks like now

[💎@04:39] The gamer motivation model has 12 motivations that group into 6 pairs (within which motivations are more correlated. See below.


[💎@06:48] At the a high level, there are 3 motivation clusters (cf. Motivation Map below):

  1. Action-Social
  2. Mastery-Achievement
  3. Immersion-Creativity



[💎@07:40] The Discovery and Power motivations do not really fit in any cluster and seem to be “bridges”:

  • Discovery is a bridge between Immersion-Creativity and Mastery-Achievement
  • Power is a bridge between Action-Social and Mastery-Achievement

[💎@07:57] This structure/pattern of 3 clusters is stable across all the regions Quantic Foundry has data for.


Motivations and personality traits

[💎@8:30] These motivation clusters relate to existing traits in psychology i.e. the “big five” personality factors:

  1. Openness to Experience
  2. Conscientiousness
  3. Extraversion
  4. Agreeableness
  5. Neuroticism

[💎@10:00] People who are outgoing and extraverted tend to be more cheerful, more optimistic, more excited and thrill-seeking and tend to be more assertive in group situations. This explains why the Action and Social motivations are tied: they are an expression of personality in the gaming context

[💎@10:24] Conscientiousness is the personality trait for how organized and self-disciplined you are. It maps to the long-term thinking oriented motivations and therefore the Mastery-Achievement cluster.

[💎@10:37] Openness is how imaginative, how curious and how unorthodox you are. It maps to the Immersion-Creativity cluster.


Quantic Foundry also surveyed some gamers on both personality traits and motivations to find correlations. However the correlations were fairly low, even though there are some connections.


[💎@12:25] Games are more of an identity management tool rather than an escape. People play games that align with their personality traits.

The data by demo


[💎@13:21] The most common primary motivations for women are Fantasy, Design and Completion. For men they are Destruction, Competition, Strategy and Challenge.



[💎@13:50] However a lot of the gender differences are dwarfed by age differences. Examples: past the age of 35, there isn’t really a difference in appeal for competition between men and women. Age also explains twice the variance that gender does.




[💎@15:26] We should think more about what games for different age groups should look like (rather than the differences between men and women).


[💎@15:58] The Action-Social group is the one that varies the most dramatically with age:

  • For men all the motivations of the cluster (Competition, Excitement, Challenge, Community) fall dramatically with age
  • For women, younger women are a lot more interested in story/narrative than older women are



They also had a question about the gamer's 3 favorite games, and then tried to see if they could build a game recommendations model and eventually a venn diagram.


Game profiles and coding

They also used the data to calculate the motivation profile for 1000 games.



[💎@20:06] There is a cognitive threshold above which there is too much conflict between Excitement and Strategy, and you need to think about the tradeoff between strategic complexity and reaction time. Example: if it’s a highly strategic game like Europa Universalis, you need the time to think about the decisions which lowers excitement. 

However games that are at the edge of that cognitive threshold can be great esports candidates.


You can look at a specific game and do a cluster analysis to surface the distinct personas (and their distribution) and the combinations of motivations come up the most.


Q&A

Relative share of each cluster?

They’ve done it by motivation but not by cluster.


Using the data possible?
This dataset is proprietary but they have older datasets available.


Any data below 13yo?

No, because it’s hard to do surveys. Other companies may have done it.


Mapping brain chemicals and neurobiology back to this?

This would take a lot of research and QF hasn’t gone into this.


How much data has biases because people know what’s happening

They skew towards a core gamer demographic which tends to bring more men and slightly younger database. But it's hard to identify the social media bias.


Any studies that are not self-administered?

They’re working with clients to target their gamers so they can check if what gamers say correlates with what they do. If they know the actual distribution of retention per cohort, etc. they can sample more accurately.


Younger women more interested in story/narrative than older women: is it just Western Europe?

They haven’t looked at that yet. It can/may vary by region.






The notes from this resource are only available to premium members.

The detailed notes taken for a resource are an easy way to see the gems in context to get a better understanding. They also include any relevant visuals from the source.
↘ At this point, you know what to do ↙
GET Access

How the model was created

A factor analysis helped create clusters to be able to form the different gamer motivation profiles.

Then they reviewed the literature on the topic to extract the motivations shared from there.


They then did a first survey with 600 people to create the early model of motivations, before creating an online app. Here is an initial sample report:


What the gamer motivation model looks like now

[💎@04:39] The gamer motivation model has 12 motivations that group into 6 pairs (within which motivations are more correlated. See below.


[💎@06:48] At the a high level, there are 3 motivation clusters (cf. Motivation Map below):

  1. Action-Social
  2. Mastery-Achievement
  3. Immersion-Creativity



[💎@07:40] The Discovery and Power motivations do not really fit in any cluster and seem to be “bridges”:

  • Discovery is a bridge between Immersion-Creativity and Mastery-Achievement
  • Power is a bridge between Action-Social and Mastery-Achievement

[💎@07:57] This structure/pattern of 3 clusters is stable across all the regions Quantic Foundry has data for.


Motivations and personality traits

[💎@8:30] These motivation clusters relate to existing traits in psychology i.e. the “big five” personality factors:

  1. Openness to Experience
  2. Conscientiousness
  3. Extraversion
  4. Agreeableness
  5. Neuroticism

[💎@10:00] People who are outgoing and extraverted tend to be more cheerful, more optimistic, more excited and thrill-seeking and tend to be more assertive in group situations. This explains why the Action and Social motivations are tied: they are an expression of personality in the gaming context

[💎@10:24] Conscientiousness is the personality trait for how organized and self-disciplined you are. It maps to the long-term thinking oriented motivations and therefore the Mastery-Achievement cluster.

[💎@10:37] Openness is how imaginative, how curious and how unorthodox you are. It maps to the Immersion-Creativity cluster.


Quantic Foundry also surveyed some gamers on both personality traits and motivations to find correlations. However the correlations were fairly low, even though there are some connections.


[💎@12:25] Games are more of an identity management tool rather than an escape. People play games that align with their personality traits.

The data by demo


[💎@13:21] The most common primary motivations for women are Fantasy, Design and Completion. For men they are Destruction, Competition, Strategy and Challenge.



[💎@13:50] However a lot of the gender differences are dwarfed by age differences. Examples: past the age of 35, there isn’t really a difference in appeal for competition between men and women. Age also explains twice the variance that gender does.




[💎@15:26] We should think more about what games for different age groups should look like (rather than the differences between men and women).


[💎@15:58] The Action-Social group is the one that varies the most dramatically with age:

  • For men all the motivations of the cluster (Competition, Excitement, Challenge, Community) fall dramatically with age
  • For women, younger women are a lot more interested in story/narrative than older women are



They also had a question about the gamer's 3 favorite games, and then tried to see if they could build a game recommendations model and eventually a venn diagram.


Game profiles and coding

They also used the data to calculate the motivation profile for 1000 games.



[💎@20:06] There is a cognitive threshold above which there is too much conflict between Excitement and Strategy, and you need to think about the tradeoff between strategic complexity and reaction time. Example: if it’s a highly strategic game like Europa Universalis, you need the time to think about the decisions which lowers excitement. 

However games that are at the edge of that cognitive threshold can be great esports candidates.


You can look at a specific game and do a cluster analysis to surface the distinct personas (and their distribution) and the combinations of motivations come up the most.


Q&A

Relative share of each cluster?

They’ve done it by motivation but not by cluster.


Using the data possible?
This dataset is proprietary but they have older datasets available.


Any data below 13yo?

No, because it’s hard to do surveys. Other companies may have done it.


Mapping brain chemicals and neurobiology back to this?

This would take a lot of research and QF hasn’t gone into this.


How much data has biases because people know what’s happening

They skew towards a core gamer demographic which tends to bring more men and slightly younger database. But it's hard to identify the social media bias.


Any studies that are not self-administered?

They’re working with clients to target their gamers so they can check if what gamers say correlates with what they do. If they know the actual distribution of retention per cohort, etc. they can sample more accurately.


Younger women more interested in story/narrative than older women: is it just Western Europe?

They haven’t looked at that yet. It can/may vary by region.