Your SlideShare is downloading. ×
Comparison of Seeding Strategies (Hinz/Skiera/Barrot/Becker, 2011, Journal of Marketing)
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

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

17,870
views

Published on

These slides that describe the content of the paper: Hinz, Oliver / Skiera, Bernd / Barrot, Christian / Becker, Jan (2011), "An Empirical Comparison of Seeding Strategies for Viral Marketing", Journal …

These slides that describe the content of the paper: Hinz, Oliver / Skiera, Bernd / Barrot, Christian / Becker, Jan (2011), "An Empirical Comparison of Seeding Strategies for Viral Marketing", Journal of Marketing, 75 (November), 55-71.

Published in: Business, News & Politics

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
17,870
On Slideshare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
35
Comments
0
Likes
1
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. 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 Skiera TU Darmstadt University of Frankfurt Christian Barrot & Jan U. Becker Kühne Logistics University, Hamburg
  • 2. TRADITIONAL MARKETING INSTRUMENTS ARE FACING SHRINKING EFFECTIVENESS IN THE FACE OF NEW SOCIAL MEDIA A changing environment… • 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 …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)Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 1
  • 3. VIRAL MARKETING RAPIDLY GAINS GROUND Global spending for viral marketing campaigns $ 3.000 Mio. $ 980 Mio. $ 76 Mio. 2001 2006 2013 e → Shift from traditional marketing budgets towards viral e Forecast Stephen, Andrew (2010): Viral Marketing: Tell a Woman, Working Paper, INSEAD, Fontainebleau.Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 2
  • 4. 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)Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 3
  • 5. THREE POTENTIAL SEEDING STRATEGIES BASED ON SOCIOMETRIC MEASURES ARE DISCUSSED IN LITERATURE (1 / 3) 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 2007Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 4
  • 6. THREE POTENTIAL SEEDING STRATEGIES BASED ON SOCIOMETRIC MEASURES ARE DISCUSSED IN LITERATURE (2 / 3) Strategy 2: High-betweenness seeding High-Betweenness bridge 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 2004Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 5
  • 7. THREE POTENTIAL SEEDING STRATEGIES BASED ON SOCIOMETRIC MEASURES ARE DISCUSSED IN LITERATURE (3 / 3) 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 2008Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 6
  • 8. PREVIOUS RESEARCH Social Position has Positive Influence on … Recom- Empirically Expected # mendation for Tested Participation Used Reach Expected # Conversion Optimal Seeding SeedingStudies Successful Prob. Pi ni Referrals Ri Rate wi Strategy Strategy Referrals SRiColeman, Katz, and Hub Hub HubMenzel (1966) Hub Hub HubBecker (1970) Fringe Fringe FringeSimmel (1950); Porter and Fringe FringeDonthu (2008)Watts and Dodds (2007) Fringe Hub Fringe Fringe FringeLeskovec, Adamic, and Hub Hub Hub FringeHuberman (2007)Anderson and May (1991); Hub Hub Hub HubKemper (1980)Granovetter (1973); Bridge Bridge BridgeRayport (1996)Iyengar, Van den Bulte, Hub Hub Hub Huband Valente (2011) Hub, Fringe,Study 1 Controlled  Bridge, Random Hub, Fringe,Study 2  Bridge, Random Hub, Fringe,Study 3      Random Notes: i = focal individual. Expected number of referrals: Ri = Pi∙ni; Successful number of referrals: SRi = wi*Ri.Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 7
  • 9. USING THREE COMPLEMENTARY STUDIES FOR EMPIRICAL TESTING Overview Study 1: "Controlled" Study 2: Realistic setup Study 3: Real world setup referral program (120 nodes, 270 edges) (1,380 nodes, 4,052 edges) (208,829 nodes, 7,786,019 edges) • Field experiment with within- • Field experiment with • Ex-Post analysis of subject design between-subject design transaction data • 120 students recruited from • Participants were business • Identification of factors leading digital social network students driving social contagion • Participation awareness • Intrinsic motivation to share process "controls" for activity level interesting content (video • Extrinsic motivation by • Varying extrinsic motivation to about their university) monetary referral reward share secret tokensHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 8
  • 10. INITIAL TEST OF SEEDING STRATEGIES IN SMALL, CONTROLLED EXPERIMENT Study 1: Design Recruit • 120 students recruited participants • Precondition: Students have account on social network platform StudiVZ Track social network data • Collect data of mutual friendship relations from online platform Model social • 120 nodes with ~270 edges network and calculate metrics • Degree and betweenness centrality calculated per node • 4 seeding strategies: high / low degree, betweenness centrality, random Seed secret • 2 seeding levels: 10%/20% of network tokens • 2 incentive levels: high/low • 4x2x2 factorial design = 16 secret tokens seeded • Students spread the secret tokens (no groups, no forums allowed) Track logins and • Responses have been entered on a website using individual login feedback entered on website information • Duration: 2 weeks per experimentHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 9
  • 11. HIGH-DEGREE SEEDING STRATEGY MAXIMIZES RESPONSE Study 1: Individual probability to respond • Random Effects Logit Model • High degree seeding maximizes responses • Decreasing marginal effect of seeding • Most responses for high degree seeding (activity)Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 10
  • 12. HIGH-DEGREE AND HIGH-BETWEENNESS STRATEGIES CLEARLY OUTPERFORM RANDOM AND LOW-DEGREE STRATEGIES Study 1: Conditional odds ratios of seeding strategiesHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 11
  • 13. TESTING SEEDING STRATEGIES IN REALISTIC EXPERIMENTAL SETTING Study 2: Design Track social • Collect data of mutual friendship relations network data from social network platform • Information obtained for all 1,380 students with business-related subjects at University • 1,380 nodes with 4,052 edges Model social • Degree and betweenness centrality network and calculate metrics calculated per node • Information seeded: link to funny Video about University Seed link to • 4 seeding strategies: high / low degree, betweenness centrality, random video (links to different websites, seeding at same day, HB/HD overlap removed) • No additional incentives Track website • Four different (seeding strategy) website visit statistics recorded visits and video • Experiment duration: 2 weeks downloadsHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 12
  • 14. STUDY 2 CONFIRMS THE SUPERIORITY OF HIGH-DEGREE AND HIGH-BETWEENNESS SEEDING STRATEGIES (1 / 2) 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 R²Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 13
  • 15. STUDY 2 CONFIRMS THE SUPERIORITY OF HIGH-DEGREE AND HIGH-BETWEENNESS SEEDING STRATEGIES (2 / 2) Study 2: Number of visits per dayHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 14
  • 16. REAL-LIFE APPLICATION OF VIRAL MARKETING CAMPAIGN USING THE CUSTOMER BASE OF A MOBILE PHONE SERVICE PROVIDER Study 3: Design • SMS mailing to 208,829 customers of a low cost mobile phone service SMS campaign aimed at promoting a special „refer-a-friend“ campaign customer base • As special promotion, the referral reward was increased by 50% (15€ instead of 10€) • All referrals tracked through the website / call center of the service Conversion provider tracking • 4.549 customers participated • 6.392 successful referrals • Calculation of Degree Centrality on the basis of individual-level call Establishing data (more than 100 million calls) the social network • 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) Adding covariates Contract details (length of customer relationship, tariff plan, payment method etc.) Service usage (monthly volume of voice minutes / SMS)Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 15
  • 17. TWO-STAGE MODEL REVEALS DIFFERENT EFFECTS OF DEGREE CENTRALITY FOR THE SELECTION AND REGRESSION COMPONENT Study 3: Poisson-logit hurdle regression model (PLHR) • Hubs are more likely to participate in viral campaign • Hubs are more likely to be successful referrers • Higher degree leads to more referrals • Higher degree has no influence on the number of successful referralsHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 16
  • 18. HUBS ARE NOT MORE PERSUASIVE THAN AVERAGE CUSTOMERS IN VIRAL MARKETING Study 3: Determinants of conversion rates for active referrers • 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 reachHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 17
  • 19. RESULTS CONFIRM THE POSITIVE CORRELATION BETWEEN DEGREE CENTRALITY AND THE SUCCESS OF VIRAL MARKETING Study 3: Relationship of conversion rates and degree centralityHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 18
  • 20. REAL-LIFE APPLICATION OF VIRAL MARKETING CAMPAIGN USING THE CUSTOMER BASE OF A MOBILE PHONE SERVICE PROVIDER Study 3: Influence domain of referral campaign participant 7 • 20.8% of all first-generation 7 referrals became active 6 referrers themselves • 5.8% did so multiple times 5 6 7 • Viral referral chains with 4 Referral maximum length of 29 3 3 Generations generations and on average 3 2 .48 additional referrals 2 3 3 3 4 • Fringe actors have access 1 to new parts of network 3 2 1 4 Customer Y Customer X Initial Campaign (Origin) StimulusHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 19
  • 21. CONDITIONAL ON SUCCESSFUL PARTICIPATION DEGREE CENTRALITY HAS A NEGATIVE EFFECT ON INFLUENCE DOMAIN Study 3: Determinants of influence domain (PLHR)Hinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 20
  • 22. POSITIVE EFFECT OF DEGREE CENTRALITY DOMINATES IN THE UNCONDITIONAL MODEL 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 formulationsHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 21
  • 23. HIGH-DEGREE STRATEGY CLEARLY OUTPERFORMS RANDOM AND LOW-DEGREE STRATEGIES 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 strategyHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 22
  • 24. HIGH-DEGREE AND HIGH-BETWEENNESS STRATEGIES WORK BEST FOR VIRAL MARKETING CAMPAIGNS – AT LEAST ON AWARENESS STAGE Summary • 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 purposesHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 23
  • 25. COMPARISON OF STUDY RESULTSHinz, Skiera, Barrot & Becker Seeding Strategies for Viral Marketing | Journal of Marketing, Vol. 75 (November), 55-71 24