The Dynamics of Viral Marketing Jure Leskovec Lada Adamic Bernardo A. Huberman Stanford University University of MichiganHP Labs Presented by Leman Akoglu March 2010

Targeted marketing Personalized recommendations Cross-selling “people who bought x also bought y” Collaborative filtering “based on ratings of users like you…” Viral marketing We are more influenced by our friends than strangers. 68% of consumers consult friends and family before purchasing home electronics (Burke 2003) Our friends know about our needs/tastes better. Why need Viral Marketing? October 12, 20152

The paper in a nutshell Analysis of a person-to-person recommendation network (June 2001 to May 2003) – 4 million people – 0.5 million products – 16 million recommendations Contributions: Data statistics Propagation, cascade sizes Network effects Effectiveness of viral marketing on product and pricing categories October 12, 20153

productscustomersrecommenda- tions edgesbuy + get discount buy + no discount Book103,1612,863,9775,741,6112,097,80965,34417,769 DVD19,829805,2858,180,393962,341 17,232 58,189 Music393,598794,1481,443,847585,7387,8372,739 Video26,131239,583280,270160, Full542,7193,943,08415,646,1213,153,67691,32279,164 people recommendations I.Music CDs and DVDs have the most/least number of items, respectively. II.Still, DVDs account for than half of all recommendations. III.Number of unique edges for Books, Music and Videos is less than number of customers –suggests many disconnected components October 12,

1.Largest connected component at the end contains ~2.5% of the nodes. 2.Total number of nodes grow linearly over time.  The service itself was not spreading epidemically. October 12,

productscustomersrecommenda- tions edgesbuy + get discount buy + no discount Book103,1612,863,9775,741,6112,097,80965,34417,769 DVD19,829805,2858,180,393962,341 17,232 58,189 Music393,598794,1481,443,847585,7387,8372,739 Video26,131239,583280,270160, Full542,7193,943,08415,646,1213,153,67691,32279,164 people recommendations IV.Influence: 1) Books (1/69) 2) DVDs (1/108) 3) Music (1/136) 4) Video (1/203) buy+get discount … buy+no discount V.People tend to buy books when they can get a discount whereas for DVDs discount does not matter much. October 12,

7 Lag between time of recommendation and time of purchase Book DVD 40% of those who buy buy within a day but > 15% wait more than a week daily periodicity October 12, 2015

Contributions of the paper: Data statistics Propagation, cascade sizes Network effects Effectiveness of viral marketing on product and pricing categories October 12,

Identifying cascades … t t t t+t’ t+t’’ t’’’ > t’’ > t’ Cascade size: 6 t+t’’’ steep drop-off very few large cascades shallow drop off DVD cascades can grow large October 12,

10 Propagation model (produces power-law cascade-size distribution) Each individual will have p t successful recommendations. – p t :[0,1] At time t+1, the total number of people in the cascade, N t+1 = N t * (1+p t ) October 12, 2015

11 Summing over long time periods – The right hand side is a sum of random variables and hence normally distributed. (Central Limit Theorem) Integrating both sides, N is log-normally distributed if  large resembles power-law Propagation model (produces power-law cascade-size distribution) October 12, 2015

Contributions of the paper: Data statistics Propagation, cascade sizes Network effects Effectiveness of viral marketing on product and pricing categories October 12,

13 Question: Does receiving more recommendations increase the likelihood of buying? (receiver’s perspective) BOOKS DVDs  Book recommendations are rarely followed.  A peak at 2, and then a slow drop (!)  For DVDs, saturation is reached at 10 –diminishing returns October 12, 2015

14 Question: Does sending more recommendations yield more purchases? (sender’s perspective) BOOKS DVDs To too few –changes of success is low versus to everyone –spam effect  For Books, the number of purchases soon saturates.  For DVDs, the number of purchases increases throughout. October 12, 2015

15 Question: Do multiple recommendations between two individuals weaken the impact of the bond on purchases? BOOKS DVDs YES! --Less is more… October 12, 2015

Contributions of the paper: Data statistics Propagation, cascade sizes Network effects Effectiveness of viral marketing on product and pricing categories October 12,

17 Recommendation success by book category Success rate: # of purchases following a recommendation / # recommenders Books overall have a 3% success rate Lower than average success rate – Fiction romance (1.78), horror (1.81) teen (1.94), children’s books (2.06) comics (2.30), sci-fi (2.34), mystery and thrillers (2.40) – Nonfiction (personal & leisure) sports (2.26) home & garden (2.26) travel (2.39) Higher than average success rate – professional & technical medicine (5.68) professional & technical (4.54) engineering (4.10), science (3.90), computers & internet (3.61) law (3.66), business & investing (3.62) October 12, 2015

18 What determines a product’s viral marketing success? Modeling recommendation success -- by linear regression # recommendations # senders # recipients product price # reviews avg. rating x i : β i : Coefficient s : success Over 50K products October 12, 2015

19 Modeling recommendation success Variabletransformation Coefficient β i const *** # recommendationsln(r)0.426 *** # sendersln(n s ) *** # recipientsln(n r ) *** product priceln(p)0.128 *** # reviewsln(v) *** avg. ratingln(t) * R2R  # senders and receivers have negative coefficients, showing that successfully recommended products are actually more likely to be not so widely popular  more expensive and more recommended products have a higher success rate  avg. rating does not affect success much October 12, 2015 significance at the 0.01 (***), 0.05 (**) and 0.1 (*) levels

Contributions of the paper: Data statistics Propagation, cascade sizes Network effects Effectiveness of viral marketing on product and pricing categories Questions & Comments October 12,