gdc 2014: analytics 101 wavemaker: social...
TRANSCRIPT
![Page 1: GDC 2014: Analytics 101 WaveMaker: Social Valuetwvideo01.ubm-us.net/o1/vault/GDC2014/Presentations/Williams... · Title: Overview, 2012 Author: Jim Sink Subject: Board Observer Created](https://reader031.vdocument.in/reader031/viewer/2022030509/5ab943827f8b9aa6018da0e9/html5/thumbnails/1.jpg)
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Dmitri Williams, CEO
(424) 634-1791
WaveMaker: Social Value
Ninja Metrics confidential information. Copyright 2013
Dmitri Williams, CEO
www.ninjametrics.com & www.ninjametrics.com.blog
GDC 2014: Analytics 101
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vwobservatory.org
ninjametrics.com/blog
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Talk Overview
• Setting up: Why are you doing this? What do you need up front?
• Definitions, acronyms, jargon
• Understanding a distribution
• Segmentation, cohort analysis, funnels
• Statistics & modeling
• Advanced stuff (time permitting) predictive analytics, machine learning, social networks analysis
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Before you do anything else
• You want to be “data driven.” Great. Really?
• What do we want to know?
• How will we act on it?
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What do we want to know?
• How much money have I made this (day/week/month/period)?
• How much money am I likely to make in the coming (day/week/month/period)?
• How stable is my player base?
• What is my churn rate?
• Which players are likely to leave in the next period?
• How much are each of my players worth?
• Which promotions are generating positive results?
• Do my players generate value among each other, or are they relatively isolated?
• Which marketing sources are leading to conversions? What is the ROI on each?
• What mechanics drive player churn?
• What mechanics drive player conversion?
• What are the effects of the changes I’ve made?
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How will we act on it?
• Benchmark reporting?
• Actionable? OK, who’s acting?
• What tools? Can you reach players (in-app, email,
push)? Can you have systems, e.g. queuing,
matchmaking, call those results tables?
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Setting Up
• Instrumentation
• Review the SDK
• Make security decisions, Enterprise vs. SaaS
• Understand “real time”
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Definitions, Acronyms, Jargon
• Acronym Soup
• DAU
• Concurrency
• MAU
• DAU/MAU
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Definitions, Acronyms, Jargon
• ARPU/ARPPU
• Retention
• Churn
• Conversion
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Definitions, Acronyms, Jargon
• Session metrics: Avg. Length, Avg. # of Sessions
• Virality
• K-Factor vs. other approaches
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Definitions, Acronyms, Jargon
• CaC: Cost of Customer Acquisition
e.g. PAX: $20, Google Ad $1.50, Ad
platform $1
• LTV: Lifetime Value
PAX: $35, Google Ad $2.50, Ad
platform $1.25.
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Understanding a distribution
• Why an average is sometimes not a good metric, and
why a SD and a Gini coefficient are super useful.
Average = $17
SD = $29
Gini = .52
Min = $5
Max = $100
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Segmentation
By:
• Demographic group
• CRM event
• Region
• Platform
• AB Group
• Start Date
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Cohort Analysis
Age, Period & Cohort
Amy Jo Kim’s stages:
Visitor, Novice,
Regular, Leader, Elder
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Funnels
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Modeling
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No, data modeling
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Statistics in 5 min
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Machine learning and predictive models:
power vs. understandability
• A->B->C->D 45/50 times. Now A->B->C->?
• If a player in EQII has 18,324 NPC kills or less, has completed 234 or
more quests, has 1889 or more total power, has 194 or less total agility
points, and has 2395 or more total health points, 68% will be partnered in
real life (n= 954, 954/306, Accuracy = 68%, Coverage = 10%). JRip .59
• So, do you need to understand “Why?”
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Social Network Analysis
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Ninja Metrics confidential information. Copyright 2013
Dmitri Williams, CEO
(424) 634-1791
WaveMaker: Social Value
Ninja Metrics confidential information. Copyright 2013
Dmitri Williams, CEO
www.ninjametrics.com
www.ninjametrics.com/blog
![Page 22: GDC 2014: Analytics 101 WaveMaker: Social Valuetwvideo01.ubm-us.net/o1/vault/GDC2014/Presentations/Williams... · Title: Overview, 2012 Author: Jim Sink Subject: Board Observer Created](https://reader031.vdocument.in/reader031/viewer/2022030509/5ab943827f8b9aa6018da0e9/html5/thumbnails/22.jpg)
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Models and Predictive Analytics