Social Interactions and Commerce (in Second Life)

April 4, 2011

Last week: How does the social network affect economic decisions?

• Observing other decisions• Coordination decisions (external costs)• Simple models and how to exploit models (viral marketing)

Today: How does the social network affect economic decisions?

• Observation study• A “real world”-ish setting– Large observable social network– Diverse types of economic activity– Real money involved

• But:– Production costs are zero– Transportation costs are zero– No “mass market” advertising – instead events and

groups

Second Life - Background

Second Life - Background

Second Life - Background

Second Life - Background• Most content is user-created• Linden Labs provides development tools (programming, modeling) for landscapes and objects• Linden Labs also sells “real estate” (cycles) • Users can charge for services and exchange objects for “Linden Dollars”

Second Life - Background

Second Life - background “This has given rise to a social and economic system that resembles the brick-and-mortar world. Individuals can specialize in the production of basic building components, and work together to assemble increasingly complex products and businesses. Entrepreneurs and speculators can build marketplaces and other infrastructure (e.g., shopping malls) to support sellers. Sellers can distinguish themselves by diversifying products, using advertising outlets, and providing immersive shopping experiences (Ondrejka 2008). Groups, clubs and events can serve as additional avenues to support business activity, and are used to sell goods or services andfeature new products.”

Dataset 1: 2008-2009

• Friend lists (4.2M users, 43M relations)– Clustering coefficient, etc look like real social nets

• Groups (520K groups, 23M affiliations)– Some O(10,000) but median size 7

• Chat volume between users• Transactions– Time, type, amount transferred– Avg revenue for sellers is 100k/m (L$) ~=400 US$

Dataset 2: April 2009

• Similar data for– The 100 largest sellers in SL (accounting for > 40%

of all SL commerce)!– 100 randomly-sampled “boutique” sellers that are

near the average revenue for all sellers

~=400 US$

An example SL seller

• Sells female clothing and bodies• April 2009:– 773 transactions with 327 customers– Made $70k L$– 356 zero-cost transactions (gifts) to 172 users– Sent ~= 16k messages to ~= 2900 users

• Avg message 551 chars• 107 chat parters are customers• 238 friends, 15 of which are customers• Belongs to 32 groups (mostly commercial & design)

– Personal contact not needed for a transaction

“Doris”

Visualizing Doris’s Store

Ordered by time of first transaction with Doris

x=date of transaction, color=amount

Repeat customers

New customers

Another store

More repeat customers and smaller transactions

New customers show shallower slope

Yet another store

Ordered by time of first transaction

x=date of transaction, color=amount

In social contact necessary?

• Correlation between user-user chatting and user-user paid transaction: – Spearman rank correlation 0.27

• Correlation between user-user chatting and user-user exchange of gifts: – Spearman rank correlation 0.42

What about indirect social contact?

Most customers are not friends of the seller, but many belong to common groups

41% of first purchased from S by U are preceded by one of U’s friends purchasing from S (social adoptions)

Niche markets? Users interacting with a seller are likely to share a group

What contributes to revenue?

signif

not signif

Associative sorting? Partnerships?

Diversity of your “niche”, in groups

Social network of 100 boutique sellers

Green links are transaction networks; blue links are chat networks; red links are friend networks. Node size represents total revenue made in April 2009. Edge weight is number of transactions and chat frequency.

Social network of 100 largest sellers

Green links are transaction networks; blue links are chat networks; red links are friend networks. Node size represents total revenue made in April 2009. Edge weight is number of transactions and chat frequency.

Where they are…

What contributes to revenue?

signif

not signif

Associative sorting? Partnerships?

Diversity of your “niche”, in groups

What contributes to repeat business?

signif

not signif

Diversity of your “niche”, in groups

%customers in a group with you

Conclusions

• Goal: empirical foundation for understanding the relationship between social behavior, communication, and economic activity

• Social ties and group membership are significant factors in SL trade

• Social ties are more associated with free goods than trade