The role of social networks in online shopping: Information passing, price of trust, and consumer choice. ACM EC 2011 San Jose, CA, USA.

Stephen Guo, Mengqiu Wang & Jure Leskovec

Stanford University

Fredrick Awuor

2 Outline

Introduction

Paper Objectives

Case Study: Taobao Networks

Information Passing

Price of Trust

Consumer Choice Prediction

Open Research Issues

31-Mar-15

3

Introduction

We speak beforehand to the shopkeeper about suitable products we want to purchase.

We consult our friends and family before buying something unfamiliar

We have an urge to tell friends about a popular new product we just bought

E-commerce websites use recommendation and product comparison to help customers discover new products.

Can we substitute this (anonymous opinions) with personalized recommendations one receives from a friend or relative?

31-Mar-15

4

Paper Objective

How do friends influence consumer purchasing decisions and product adoption?

What factors influence the success of word of-mouth product recommendations?

How does social influence and reputation affect commercial activity?

Contribution: Establish relationship between social and e-commercial network

Insight: Information passing, price of trust, consumer choice prediction

Information passing: an individual purchases a product, then messages a friend, what is the likelihood that the friend will then purchase the product? Where will he purchase it from?

31-Mar-15

5 Case Study: Taobao Networks

31-Mar-15

Chinese consumer marketplace, World largest e-commerce website, (> 380M users 2010).

6

Case Study: Taobao Networks

Why Taobao? It integrates instant messaging tool which buyers can use to ask sellers about products or ask other buyers for advice

Supports B2C, B2B, and C2C

Dataset describing behavior of 1M users and transactions across over 10,000 products

User information: transactions with other users (product ID, price, quantity, timestamp), contact lists, timestamp of messages exchanged (not the content)

31-Mar-15

7

Taobao Networks

Dyadic buyer-seller relationships:

Is message activity correlated with trading activity?

Positive increasing relationship between message volume and trade volume

31-Mar-15

8 Taobao Networks “Do buyers talk to sellers more about expensive products?”

Expectedly YES … but how much more are they talked about?

Count the number of messages sent from buyer to seller on transaction date, assuming that at least one message is exchanged

31-Mar-15

• The number of messages sent is relatively constant for products of price below 100 CNY, then increases logarithmically for products of higher price.

• Why? Buyers minimize transaction risks by messaging

9Taobao Networks

31-Mar-15

To minimize transaction risks buyers speak with sellers often before transaction to inquire about product details.

How often do buyers speak to sellers before and after trades?

Most messages occur on the day of transaction, likely being product negotiation.

Post-trade messages are significantly more common than pre-trade messages

10

Information Passing

In the Taobao system, buyers have an option of using an escrow service, where the seller first ships the product, and payment is exchanged after the buyer examines the product.

The observed post-trade messages are likely discussion regarding product satisfaction and payment confirmation.

 Messaging activity is correlated with trading activity across pairs of users

31-Mar-15

11

Presence of Information Passing

Consider: a buyer notices a deal offered at an electronic store, makes a purchase, then messages his friend about the deal.

Will the friend also make a purchase from the same store?

How large is the influence of the buyer?

31-Mar-15

12Presence of Information Passing

The more mutual contacts a pair of users has, the greater the likelihood that they engaged in a commercial transaction (Standard)

For users who have exchanged at least one message (Msg Req), for a given number of mutual contacts, the transaction probability is higher than standard.

31-Mar-15

13Presence of Information Passing

Inference: Trades are more likely to be embedded in the dense sub-graphs of communication networks.

Implication: Social proximity and trade likelihood are correlated

Information passing and product recommendation present in the Taobao network

Social proximity: Measured by the number of mutual contacts between the pair

Conclusion: Messaging increases purchasing behavior in the Taobao network.

31-Mar-15

14 Presence of Information Passing

31-Mar-15

Information passing success rate decreases with product price for the price range from 1 CNY to 15 CNY, then increases slightly for products priced above 15 CNY.

The large closure probability at a price of 1 CNY is due to the popularity and virality of virtual goods, such as game credits.

15 Price of Trust

Spread of product recommendations, through word-of-mouth, inherently relies upon a notion of buyer-buyer trust.

In the context of electronic marketplaces, buyers are unsure about seller trustworthiness, so buyers put their trust into seller ratings and reviews, and are willing to pay a premium to sellers with good reputations

How much extra will a buyer pay for transaction with a highly rated seller?

31-Mar-15

16 Price of Trust

31-Mar-15

Use seller rating as indicator for reputations and trustworthiness

Use rating information to compare sellers of the same product and determine how their sale prices differ.

Higher rating is associated with a seller selling his products at a premium compared to most of his peers.

17 Price of Trust

31-Mar-15

Observation: seller rating of 97.1% corresponds to transaction at the median cluster price.

Also, buyers are willing to pay more to highly rated sellers to minimize transaction risk,

sellers who maintain good reputations are financially rewarded.

18 Consumer Choice Prediction

31-Mar-15

“How does an online consumer decide upon a seller to purchase from when there are many sellers offering the same product?”

Model consumer choice as a machine learning task (utilizing primarily social networking features)

Reason: Social graph is a far better determinant of consumer behavior than metadata features such as seller reputation or product price.

19 Consumer Choice Prediction

When faced with a selection of substitute goods offered by different sellers, buyers will not just choose their preferred seller through simple heuristics regarding price or rating.

Buyers utilize many sources of information (seller history, advice of friends, seller’s messages), and each buyer processes the information in their own way in order to make a personal purchasing decision

Contribution:

Develop a machine learning model to predict consumer choice, and

Show that the social network is the most important feature in predicting how consumers choose their transaction partners.

31-Mar-15

20 Consumer Choice Prediction

58,812 sets of training examples, (75% train and 25% test sets). The SVMs trained with linear kernels.

Evaluation metrics:

Precision at Top 1 (P@1)- Fraction of times that the top ranked seller is actually the true seller. (Higher is better)

Mean Rank (MR)- Average Rank of the true seller. (Lower is better)

Mean Reciprocal Rank (MRR) - Average Reciprocal Rank of the true seller. (Higher is better)

Three rule-based baselines: Random, MinPrice, MostMsg

31-Mar-15

21 Consumer Choice Prediction

31-Mar-15

22 Open Research Issues Analysis of user browsing data to develop refined consumer choice models

for social commerce,

Study of information passing while factoring in both buyer-buyer and buyer-seller trust relationships, and

Viral marketing to influence consumer choice in social commerce.

Cold start problem – New sellers/products with no ratings

31-Mar-15

23

Comments

31-Mar-15