1. Kontagent Facebook Developer Garage
San Francisco, March 25, 2009
What App and Game Devs can
Learn from Amazon.com
Andreas Weigend
www.weigend.com
Andreas S. Weigend, Ph.D. 韦思岸教授
© people & data | www.weigend.com

2. Outline
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PHAME

Problem
Hypothesis
Action
Metrics
Experiments
Examples

User value: Value of user for firm, for network vs value for user
© people & data | www.weigend.com
Acquisition (viral) vs retention (engagement) ?
More info: weigend.com and SocialDataRevolution.com

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3. Problems
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Decisions, decisions…

How to adjust difficulty of your game?
How much to charge for a virtual gift?
Who to send invites to?
Where to place stuff on the screen?
…
…
Q: What problems do you face with your game or app?

© people & data | www.weigend.com
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6. Result: Right vs Left
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对比结果：左还是右
Metrics

衡量标准
Conversion rate: Percentage of visits placing an order
转化率：下订单的浏览者所占的比例
Order size: Number of additional (from the second page) items put into cart
订单大小：（从第二页起）新购商品数量
Result

结果
“Your Shopping Cart” on right is about 1% better than on left
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“Your Shopping Cart”置于右侧比置于左侧的效果提高1%
All customers Existing customers
现有消费者
所有消费者
Cart-adds from 2nd page: Cart-adds from 2nd page:
+0.6% +0.8%
从第二页起新购商品数量： 从第二页起新购商品数量：
Wishlist-adds: DVD Cart-adds:
+1.4% +0.8%
选择礼物清单： 新购DVD：
DVD (USD): +1.0% 6
DVD (USD): +1.1%

7. Why Analytics and Data Mining?
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Stanford Stats252 “Data Mining and Electronic Business”
Mondays 2-5, Gates B01 (first class Spring 2009 is April 6, 2009)

weigend.com/teaching

Data mining  Actionable Insights?

The Past: Someone gives you data, and you do your best
Worst: Reporting

Slightly better: Regression analysis

Better: Predictions on new data

© people & data | www.weigend.com
Eternal hope: Actions

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8. 1/3 of sessions only one click! Distribution of visit length:
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访问时间分布图
How many clicks per visit?
每次访问有多少点击数？
无法辨认的未购
买行为 Gold
可以识别的未购买行为 Box
Web-
可以识别的购买行为
crawlers
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内部的未购买行为
内部的购买行为
点击数量
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9. | +1 650 906-5906 | +49 174 906-5906 | +86 138 1818 3800
How long ago did a customer first look at the
detail page of an item eventually purchased?
(Conditioned on purchase)
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10. | +1 650 906-5906 | +49 174 906-5906 | +86 138 1818 3800
© people & data | www.weigend.com
$5-10
$20-25
1.2% of all orders are below $5
How much does a customer
spend on an order?
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11. Picking good visualizations is key to seeing patterns
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选择正确的形象是识别特征的关键
Traffic by day Heat map
 
按天计流量 热图
Shows traffic colored from green to yellow
Easy to see weekends
to red
容易识别周末
用颜色(从绿色到黄色到红色)显示流量
Difficult to see other patterns
Utilizes cyclical nature of the week
很难区分其他的特征
利用一周的周期性特点
Ronny Kohavi, Microsoft
•
Note 9/3 (Labor Day) and 9/11
注意：9/3（劳动节）和9/11
Weekends
周末
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12. Actions: Social recommendations
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90% of people believe information Social recommendations work
passed to them by friends and
well because context, content,
family
and targeted individual
80% of all consumer decisions are
(recipient) are chosen by a
influenced by social
friend
recommendations
89% of consumers recommend
products or services that they like
to others.
Context
Social recommendations are…
© people & data | www.weigend.com

… 9 times as effective as
advertising in converting
unfavorable or neutral pre-
dispositions into positive attitudes
Content Recipient
… 4 times as effective in
influencing consumer to switch
brands
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Tom Gieselmann, BV Capital
•

13. Leverage the social graph
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Example: New communications service
US phone company with deep experience with targeted marketing

Sophisticated segmentation models based on experience, intuition, and data

e.g., demographic, geographic, loyalty data
Hill, S., F. Provost., and C. Volinsky.
•
Network-based Marketing: Identifying likely adopters via consumer networks.
Statistical Science 21 (2) 256–276, 2006
.
•
4.82
(1.35%)
2.96
© people & data | www.weigend.com
(0.83%)
1
0.4
(0.28%) (0.11%)
Non-NN 1-21 NN 1-21 NN 22 NN not
targeted
Response increases by a factor of 4.82 by marketing to nearest neighbors (NN)

From 0.28% based on segmentation, to 1.35% based on social graph
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14. Metrics: beyond unique users, clicks…
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Scarce vs abundant?

What are the real costs?
Time vs money?

Social capital and social cost?

Cost of interrupt

Short-term vs long-term?

Should you make user aware of similar games or apps?
Amazon.com: Helping people make decisions they don’t regret
© people & data | www.weigend.com
Local vs global?

MrTweet

Notifications
Computational issues
Hard-wired vs fluid?

Where can the behavior of people change, where not?
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15. How real people make real decisions
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Will removing an option nobody chooses have any effect?

Dan Ariely, “Predictably Irrational”
•
All 3 Only 2
16% 68%
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0% n/a
84% 32%
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16. Recap: the PHAME framework
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 Problem
 Hypotheses
Ask people, and compare responses to what they do
Dating site

 Action
© people & data | www.weigend.com
 Metrics
Actionable
Accessible
Auditable
 Experiment
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17. Outlook
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User value

Value of user for your company

Value your app / game has for user

Value user has for other users (Network effects)

Acquisition (viral) vs retention (engagement) ?

Transaction economics  Relationship economics

Optimize for the product: Acquisition * retention
Want to know more?

Economics of messaging

© people & data | www.weigend.com
Gifting

Virtual item pricing

See weigend.com
Join “Social Data Revolution” on Facebook
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