JIAYANG LI-Esports Fans Analysis
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Transcript of JIAYANG LI-Esports Fans Analysis
ESPORTS FANS ANALYSISJIAYANG LIOctober 25, 2015
LEAGUE OF LEGENDS
AGENDA
PREDICTION SEGMENTATIONBACKGROUND
APPENDIXGENDER
BACKGROUND
SUMMARY
Objective:Analyze general gamers and esports fans to make recommendation on esports marketing strategy and resource allocation.
Analysis:1. Hypothesis Test & Prediction Analysis2. Segmentation Analysis.3. Explore Gender Difference
SUMMARY
VARIABLES
Gamer ID Gender Weekly Online Duration 2015
Average Annual RP PurchasingIP Earned 2015
Registration Time
Rank Proportion
History Best Ranked TierAverage KDA
Clicks Video Clicks Article
TotalClicks
Variable explanations are in the Appendix
VARRIABLES
PREDICTION ANALYSIS
Define esports fans by his total clicks on esports videos and articles.
Methods:Statistic, T-Test, Chi-Square Test
Esports Fans: total clicks > average
General Gamers:total clicks < average
Test the difference between esports fans and general gamers
INDICATOR
Results:Esports fans are performing differently from general gamers on 4 indicators: “rank proportion”, “weekly online duration”, “average RP purchasing” and “registration time”.
RESULTS
21%
Long Time Registration Time > 4 years
Moderate Time2 years < Registration Time < 4 years
Short TimeRegistration Time < 2 years
Esports Fans General Gamer
Two groups are performing differently on “Registration Time”
“Registration Time”
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0
24%
55%
20%
37%
43%
P-value < 0.05 Indicator
EXAMPLE
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0
4%
15%17%
25%
Challenger Diamond Platinum
Esports Fans General Gamer
“History Best Tier”
Two groups are performing similarly on “History Best Tier”
13%
26%
2%
19%
15%
30%
8%
26%
Gold
Silver Bronze
EXAMPLE
P-value > 0.05 Indicator
Build Predictive Model
MODELING 1) Which game behaviors are significantly correlated to esports behaviors.
2) How these game behaviors influence gamers’ clicks on esports material.
To Find:
Methods:Regression ModelingModel Validation
Rank Proportion
Tota
l Clic
ks
Optimal Model:Total Clicks = 111.18 R + 0.29 D
*R: rank proportion*D: weekly online duration 2015
MODELING
The more frequently a gamer plays rank, the more he pays attention to esports
1.
The more time a gamer spends on LOL weekly, the more he pays attention to esports
INSIGHTS
2.
Average RP Purchasing is not correlated to whether he is an esports fan 3.
4. Most non-esports fans’ average RP purchasing are low
The x-axis is gamer ID and the y-axis is average annual RP purchasing
The color represents whether he is an esports fan (blue = Yes, orange = No)
INSIGHTS
Registration time not correlated to whether he is an esports fan 5.
6. Most esports fans are new gamers
The x-axis is gamer ID and the y-axis is registration time
The color represents whether he is an esports fan (blue = Yes, orange = No)
INSIGHTS
Pick gamers who play rank frequently or play LOL for longer than weekly average time as survey samples. Without further analysis, they are most likely to be esports fans.
Sampling Method:
Research on insight 6. If research shows long-time gamers are tired of LCK dominating, we should hold more cross-league tournaments to improve communication in tactics and market more on non-league genres like All Star.
Engagement:
Insight 4 may indicate a causal relationship between esports and RP purchase. Conduct A/B testing to evaluate our contribution on ARPU and find out the reason behind to take advantage of it.
ARPU:
RECOMMENDATION
Prediction Analysis could be applied to various aspects.
such as:Predict channel performance among Game Log-in interface, Youtube and TwitchTV. Allocate budget and resources accordingly.
In reality, we need to consider more factors than this projects, such as: How long they spend on each platform after the click (bounce rate) Their behaviors after watching videos (share to social media)
EXTENSION
SEGMENTATION ANALYSIS
Group A
Group B
Group C
6%
7%
28%
Divide esports fans into groups according to their similarities on behaviors. (process in Apendix)
>
There are 3 groups in esports fans, takes a total 41% of sample gamers
>
Method:K-Mean Clustering, Cluster Plot
SEGMENTATION
Population Proportion
GROUP A
GROUP B
GROUP C
GROUP A:
Most Valuable Segment
GROUP B: GROUP C:
PROFILES
They are still learning about the game and they are curious about esports. They watch lots of videos to improve skills and spend much money in game too.
Losing Segment
They are master gamers. They no longer need to spend money in game. They still watch videos but they are losing passion to esports.
Bottleneck Segment
They play LOL a lot but lack of tactics. They are familiar with esports but didn’t get much fun yet
GROUP A:
Most Valuable Segment
GROUP B: GROUP C:
STRATEGY
Made special videos on pro gamers and teams to deepen their interest.
Cooperate with popular commentators online to market esports via entry-level unofficial channels
Losing Segment
Stimulate their intersts with new changes.(ex. encourage pros to deepen champion pools in game)
Help weak leagues to grow to make the pro games more exciting
Bottleneck Segment
Hold special “Recap Show” after Live, with detailed champion & tactics commentary to help them understand the fun part of esports
Segmentation could be applied to various aspects.
Such as:Segmentation on Riot social media followers on different platformsSegmentation on esports merchandise buyers
Use Segmentation before Prediction Analysis to get a more accurate prediction model.
EXTENSION
GENDER
GENDERTest whether male gamers and female gamers have different taste on choosing esports materials: videos or articles
Methods:
T-Test
Article Clicks
Female GamerMale Gamer
Video ClicksThere is no significant difference between male gamers and female gamers on the taste of choosing videos or articles
RESULT
Gender Test could also test other taste differences
Such as differences on:
Favorite teams/pros, commonly used social media platformtaste on esports merchandise, trigger to become esports fans
There are many demographic groups we can analyze too, such as: different leagues, countries, age groups.
EXTENSION
APPENDIX
Data Analytics All the data and analysis are processed by R
Code Script & Output:
http://rpubs.com/jli101/120496
Variables
Gamer ID Gamer Account Name (unique)
Gender Gamer Gender
Weekly Online_Duration_2015Each gamer’s average weekly log-in duration in 2015. A good indicator for whether he is an active user in 2015.
Average_Annual_RP_Purchasing
Average annual dollar spend on purchasing RP. A good indicator for purchasing power and ARPU. Also minimize the aggregate effect due to registration duration
IP_Earned_2015Total IP earned in 2015. A good indicator for whether he is an active user in 2015 and game skills.
Registration_TimeFor how many years he has been registrated as a summoner in League of Legend
Rank_Proportion The proportion of playing rank out of all the games he plays
Hitory_Best_Ranked_Tier The best ranked tier he has ever get. A good indicator for game skills
Average KDA Average KDA. A good indicator for game skills
Clicks_VideoHow many times he clicks on the esports videos on LOL welcome interface during the "Worlds"
Clicks_ArticleHow many times he clicks on the esports articles of LOL welcome interface during the "Worlds"
Total_ClicksSum of clicks_video and clicks_article. A good indicator for whether he is a heavy esports fans
Clustering Plot
THANK YOUFor Your Watching and Everything