Comparison of Seeding Strategies (Hinz/Skiera/Barrot/Becker, 2011, Journal of Marketing)

Social Contagion – An Empirical Comparison

of Seeding Strategies for Viral Marketing

Hinz, Oliver / Skiera, Bernd / Barrot, Christian / Becker, Jan U. (2011), "An Empirical Comparison of Seeding Strategies for Viral Marketing",

Journal of Marketing, 75 (November), 55-71

Oliver Hinz Bernd SkieraTU Darmstadt University of Frankfurt

Christian Barrot & Jan U. BeckerKühne Logistics University, Hamburg

2Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71

A changing environment…

…requires new marketing instruments

• Companies discover innovative new methods to proactively stimulate and channel Word-of-mouth Viral Marketing as savior

• Viral marketing: consumers mutually share and spread information, initially sent out deliberately by marketers to stimulate and capitalize on word-of-mouth (WOM)

TRADITIONAL MARKETING INSTRUMENTS ARE FACING SHRINKING EFFECTIVENESS IN THE FACE OF NEW SOCIAL MEDIA

• Information over-flow: Traditional advertising instruments such as print ads or TV commercials struggle to reach an audience growing tired of ever more ads

• Rise of social media: Communications is shifting towards digital social media, such as facebook, twitter, email or SMS.

• Credibility: Studies have shown the higher effectiveness of customer-initiated communication (e.g., word-of mouth) compared to advertising

• Effective customer acquisition: Marketing managers have discovered social interactions between existing and potential customers as new sources for customer acquisition


VIRAL MARKETING RAPIDLY GAINS GROUND

Global spending for viral marketing campaigns

→ Shift from traditional marketing budgets towards viral

2001 2006 2013 eSeries1

e Forecast

Stephen, Andrew (2010): Viral Marketing: Tell a Woman, Working Paper, INSEAD, Fontainebleau.

$ 76 Mio.

$ 980 Mio.

$ 3.000 Mio.


VIRAL MARKETING UTILIZES THE ADVANTAGES OF PERSONAL COMMUNICATIONS IN SOCIAL NETWORKS

Viral marketing

• Advantages

- familiar senders have a higher credibility for the recipient

- familiar senders are not blocked by spam filters (higher reception and open rates)

- low to very low cost (e.g., for distribution via SMS or email)

• Key success factors

- Content (e.g., funny, entertaining, surprising, motivating)

- Willingness-to-share, often stimulated by incentives (e.g., coupons, competitions, financial rewards)

- Social Network Structure (e.g., connectedness)

- Seeding (selection of starting points to maximize campaign impact)


Strategy 1: High-degree seeding

High-Degree(hub)

Hypothesis: Seeding of individuals with a very high number of personal contacts (High- Degree) maximizes the reach of a viral marketing campaign

→ Supported by, for example, Katz/Lazarsfeld 1955; Rogers 1962; Coleman et al. 1966; Rosen 2000; Weidlich 2000; Hanaki et al. 2007; van den Bulte/Joshi 2007

THREE POTENTIAL SEEDING STRATEGIES BASED ON SOCIOMETRIC MEASURES ARE DISCUSSED IN LITERATURE (1 / 3)


Strategy 2: High-betweenness seeding

Hypothesis: Seeding of individuals acting as “bridges“ or intermediaries between sub- networks (High-Betweenness) maximizes the reach of a viral marketing campaign

→ Supported by, for example, Granovetter 1973; Kemper 1980; Rayport 1996; Watts 2004

High-Betweennessbridge



Strategy 3: Low-degree seeding

Low-Degree(fringe)

Hypothesis: Seeding of individuals with a small number of personal contacts (Low-Degree) maximizes the reach of a viral marketing campaign

→ Supported by, for example, Simmel 1950; Becker 1970; Sundararajan 2006; Galeotti/Goyal 2007; Watts/Dodds 2007; Porter/Donthu 2008



PREVIOUS RESEARCH

Social Position has Positive Influence on … Recom-mendation for

Optimal Seeding Strategy

Empirically Tested

Seeding StrategyStudiesParticipation

Prob. Pi

Used Reachni

Expected # Referrals Ri

Conversion Rate wi

Expected # Successful

Referrals SRi

Coleman, Katz, and Menzel (1966) Hub Hub Hub

Becker (1970) HubFringe Hub

Fringe HubFringe

Simmel (1950); Porter and Donthu (2008) Fringe Fringe

Watts and Dodds (2007) Fringe Hub Fringe Fringe Fringe

Leskovec, Adamic, and Huberman (2007) Hub Hub Hub Fringe

Anderson and May (1991); Kemper (1980) Hub Hub Hub Hub

Granovetter (1973); Rayport (1996) Bridge Bridge Bridge

Iyengar, Van den Bulte, and Valente (2011) Hub Hub Hub Hub

Study 1 Controlled Hub, Fringe, Bridge, Random

Study 2 Hub, Fringe, Bridge, Random

Study 3 Hub, Fringe, Random

Notes: i = focal individual. Expected number of referrals: Ri = Pi∙ni; Successful number of referrals: SRi = wi*Ri.


Overview

Study 1: "Controlled" setup

(120 nodes, 270 edges)

Study 2: Realistic setup

(1,380 nodes, 4,052 edges)

• Field experiment with within-subject design

• 120 students recruited from leading digital social network

• Participation awareness "controls" for activity level

• Varying extrinsic motivation to share secret tokens

• Field experiment with between-subject design

• Participants were business students

• Intrinsic motivation to share interesting content (video about their university)

Study 3: Real world referral program(208,829 nodes, 7,786,019 edges)

• Ex-Post analysis of transaction data

• Identification of factors driving social contagion process

• Extrinsic motivation by monetary referral reward

USING THREE COMPLEMENTARY STUDIES FOR EMPIRICAL TESTING


Study 1: Design

INITIAL TEST OF SEEDING STRATEGIES IN SMALL, CONTROLLED EXPERIMENT

Recruit participants

• 120 students recruited• Precondition: Students have account on social network platform StudiVZ

Track social network data

Model social network and

calculate metrics

Seed secret tokens

Track logins and feedback entered

on website

• Collect data of mutual friendship relations from online platform

• 120 nodes with ~270 edges • Degree and betweenness centrality calculated per node

• 4 seeding strategies: high / low degree, betweenness centrality, random• 2 seeding levels: 10%/20% of network• 2 incentive levels: high/low• 4x2x2 factorial design = 16 secret tokens seeded

• Students spread the secret tokens (no groups, no forums allowed)• Responses have been entered on a website using individual login

information• Duration: 2 weeks per experiment


Study 1: Individual probability to respond

HIGH-DEGREE SEEDING STRATEGY MAXIMIZES RESPONSE

• Random Effects Logit Model• High degree seeding maximizes

responses• Decreasing marginal effect of

seeding• Most responses for high degree

seeding (activity)


HIGH-DEGREE AND HIGH-BETWEENNESS STRATEGIES CLEARLY OUTPERFORM RANDOM AND LOW-DEGREE STRATEGIES

Study 1: Conditional odds ratios of seeding strategies


Study 2: Design

Track social network data

Model social network and

calculate metrics

Seed link to video

Track website visits and video

downloads

• Collect data of mutual friendship relations from social network platform

• Information obtained for all 1,380 students with business-related subjects at University

• 1,380 nodes with 4,052 edges • Degree and betweenness centrality

calculated per node

• Information seeded: link to funny Video about University• 4 seeding strategies: high / low degree, betweenness centrality, random

(links to different websites, seeding at same day, HB/HD overlap removed)• No additional incentives

• Four different (seeding strategy) website visit statistics recorded• Experiment duration: 2 weeks

TESTING SEEDING STRATEGIES IN REALISTIC EXPERIMENTAL SETTING


Study 2: Number of visits per day

• Random Effects Model• High-Degree / High-Betweenness best seeding strategies• Clearly outperforming random and Low Degree seeding at every point in time• (Re-)seeding day dummy doubles R²

STUDY 2 CONFIRMS THE SUPERIORITY OF HIGH-DEGREE AND HIGH-BETWEENNESS SEEDING STRATEGIES (1 / 2)


Study 2: Number of visits per day

STUDY 2 CONFIRMS THE SUPERIORITY OF HIGH-DEGREE AND HIGH-BETWEENNESS SEEDING STRATEGIES (2 / 2)


Study 3: Design

REAL-LIFE APPLICATION OF VIRAL MARKETING CAMPAIGN USING THE CUSTOMER BASE OF A MOBILE PHONE SERVICE PROVIDER

• SMS mailing to 208,829 customers of a low cost mobile phone service promoting a special „refer-a-friend“ campaign

• As special promotion, the referral reward was increased by 50% (15€ instead of 10€)

SMS campaign aimed at

customer base

Conversion tracking

Establishing the social network

Adding covariates

• All referrals tracked through the website / call center of the service provider

• 4.549 customers participated • 6.392 successful referrals

• Calculation of Degree Centrality on the basis of individual-level call data (more than 100 million calls)

• Included are only calls / SMS between customers and non-customers („external degree“), as existing customers are no potential referral targets

• Additional set of covariates to explain the referral likelihood such as:- Socio-demographics (age, gender)- Contract details (length of customer relationship, tariff plan, payment

method etc.)- Service usage (monthly volume of voice minutes / SMS)


Study 3: Poisson-logit hurdle regression model (PLHR)

TWO-STAGE MODEL REVEALS DIFFERENT EFFECTS OF DEGREE CENTRALITY FOR THE SELECTION AND REGRESSION COMPONENT

• Hubs are more likely to participate in viral campaign

• Hubs are more likely to be successful referrers

• Higher degreeleads to more referrals

• Higher degreehas no influenceon the number of successful referrals


Study 3: Determinants of conversion rates for active referrers

HUBS ARE NOT MORE PERSUASIVE THAN AVERAGE CUSTOMERS IN VIRAL MARKETING

• Within the group of active campaign participants, degree centrality is no significant effect on conversion rate

• Viral marketing works at awareness stage through simple information transfer

• Hubs are no “better” referrers – they just have a higher reach


RESULTS CONFIRM THE POSITIVE CORRELATION BETWEEN DEGREE CENTRALITY AND THE SUCCESS OF VIRAL MARKETING

Study 3: Relationship of conversion rates and degree centrality


Study 3: Influence domain of referral campaign participant

• 20.8% of all first-generationreferrals became active referrers themselves

• 5.8% did so multiple times

• Viral referral chains with maximum length of 29 generations and on average .48 additional referrals

• Fringe actors have access to new parts of network

Initial CampaignStimulus

Customer X(Origin)

Customer Y

1

2

3

4

5

6

7

123

ReferralGenerations

2

33

33 3 4

4

6

7

7

REAL-LIFE APPLICATION OF VIRAL MARKETING CAMPAIGN USING THE CUSTOMER BASE OF A MOBILE PHONE SERVICE PROVIDER


Study 3: Determinants of influence domain (PLHR)

CONDITIONAL ON SUCCESSFUL PARTICIPATION DEGREE CENTRALITY HAS A NEGATIVE EFFECT ON INFLUENCE DOMAIN


Study 3: Determinants of unconditional influence domain

• Hubs are more important for viral success

• Results hold for both first-generation referrals as well as influence domains

• Results hold for all combinations of covariates (incl. usage, demographics etc.)

• Results hold for both simple OLS as well different count model formulations

POSITIVE EFFECT OF DEGREE CENTRALITY DOMINATES IN THE UNCONDITIONAL MODEL


Study 3: Relationship of conversion rates and degree centrality

• Hubs seem to participate, refer and successfully refer more often than average

• The average degree of the best and worst customer cohort is ca. 4:1

• A high-degree strategy would outperform a random selection by ca. 100%

• A high-degree strategy leads to conversion rates of nearly 10 times of the comparable low-degree strategy

HIGH-DEGREE STRATEGY CLEARLY OUTPERFORMS RANDOM AND LOW-DEGREE STRATEGIES


Summary

HIGH-DEGREE AND HIGH-BETWEENNESS STRATEGIES WORK BEST FOR VIRAL MARKETING CAMPAIGNS – AT LEAST ON AWARENESS STAGE

• High-Degree and High-Betweenness seeding is comparable and outperforms random

seeding +39-52% (study 1), +60% (study 2), +100% (study 3)

• High-Degree and High-Betweenness outperforms Low-Degree by factor 7-8 (study 1),

factor 3 (study 2) and factor 8-9 (study 3)

• Influence of socio-metric measures beyond and above loyalty and revenue measures

• Hubs more likely to participate, do not fully use their reach potential, are not more

persuasive (due to social contagion working at awareness stage)

• Social networks possess valuable data that has not been used for targeting purposes


COMPARISON OF STUDY RESULTS

Business

Comparison of Seeding Strategies (Hinz/Skiera/Barrot/Becker, 2011, Journal of Marketing)