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Alisa Chumachenko, GOSU

wnconf
March 26, 2019

Alisa Chumachenko, GOSU

Gaming Data Magic

(White Nights Conference Berlin 2019)
The official conference website — http://wnconf.com

wnconf

March 26, 2019
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Transcript

  1. FUTURE OF
    PERSONALIZATION
    IN GAMES
    Alisa Chumachenko for White Nights 2019

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  2. Alisa
    • 17 years in gaming
    • Game Insight founder and ex-CEO
    (2009-2015)
    • GOSU Data Lab Founder 

    (2017-2019)
    • In Top 30 women changed IT by
    Techcrunch
    • 50+ titles launched

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  3. LEAGUE OF LEGEND
    CHAMPIONS

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  4. Mold of unique
    gamers behaviors
    • Identification
    • Esports (Online Tournaments)
    • Matchmaking
    • Personal features
    • SmartBots
    GAMER
    FINGERPRINT

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  5. Machine learning approach
    More skilled bots: model plays millions of
    games, each game outcome is taken into
    account in the learning process to choose
    the most effective actions

    More human bots: model predicts which
    action person would have made
    SMART BOTS/NPC
    C A S E S T U DY
    ALPHA GO
    STARCRAFT

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  6. Traditional Analytics approach
    • Balanced game — good game
    • Rating (MMR)
    MATCHMAKING
    Machine learning approach
    • The selection of the relevant group based
    on the user experience and game
    preferences;
    • Multi-factor analysis of player`s skills and
    involvement in the gameplay
    Beyond Skill Rating: Advanced Matchmaking in
    Ghost Recon Online” Olivier Delalleau et. al.
    C A S E S T U DY

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  7. GOSU PARTY
    FINDER
    C A S E S T U DY
    On the basis of millions analyzed
    matches, AI suggests the most relevant
    potential party members for each
    GOSU.AI user

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  8. Traditional analytics approach
    • Send special activation offer of award on a
    Nth day after player stops playing
    SMART CHURN PREVENTION
    Machine learning approach
    • Train machine learning model that predicts
    probability of user not to do any action in next
    week based on previous data.

    • Send activation offer when predicted probability
    falls below the threshold, not after fixed period
    of time
    C A S E S T U DY

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  9. Traditional analytics approach
    • Users segmentation by clusters;
    • Selection of triggers /conditions for each cluster.
    PERSONAL OFFER/

    PRICING
    Machine learning approach
    • Data collection: random sentences on a small target group;
    • Model training;
    • Decision making: model using for each player individually
    • Send offer at the time when the user is ready enough for
    accepting the offer (smart trigger / when probability of
    acceptance is high enough).
    C A S E S T U DY

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  10. Machine learning approach:
    • Predict user preferences using pre-trained
    machine learning model (...preference
    examples)

    • Change gameplay according to predicted
    preferences, i.e. more pvp quests for “brutal"
    man, more crafting quests and beautiful skins
    for a tender girl
    DYNAMIC GAMEPLAY
    (PERSONALIZATION
    AND PERSONAL
    RECOMMENDATIONS)
    C A S E S T U DY

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