1. INBAL YAHAV WOLFGANG JANK R.H. SMITH SCHOOL OF BUSINESS, UNIVERSITY OF MARYLAND E-Loyalty Networks in Online Auctions

2. Motivation Sellers Bidders Objective High profit High conversion rate Get the product ? Low price? Get the product Get the product (quality) Means Trust Feedback score Lit Auction design (e.g., open price, duration, etc.) IS THAT ENOUGH?? Actors

3. Research Questions 1. How to define and measure e-loyalty? 3. What factors drive loyalty in online auctions? 2. How does loyalty impact auction outcome (price, conversion)?

4. Data ~350 Sellers ~700 Repeating Buyers

5. Loyalty in the Literature Definition: repeating purchases Brand-switch literature: Probability of switching to another brand Distribution of purchases across different brands (commonly 2 brands)

6. Research Questions 1. How to define and measure e-loyalty? 3. What factors drive loyalty in online auctions? 2. How does loyalty impact auction outcome (price, conversion)?

7. Define and Measure eLoyalty Three steps measurements Construct eLoyalty network Transform network into loyalty distribution Transform the distribution into quantifiers using PC analysis

8. Define and Measure eLoyalty eLoyalty networks Bipartite graph with: First nodes set: sellers (red) Second node set: buyers (white) Arcs: purchases, with the width corresponding to the number of interactions

9. Define and Measure eLoyalty eLoyalty disribution Sellers Buyers 100% 70% 80% 2.Measure the perceived loyalty per seller (~distribution of the weighted in-degree) 1.Measure proportion of interactions per buyer (~normalized distribution of out-degree) 30%

10. Define and Measure eLoyalty Transform the distribution into two quantifiers (PC1, PC2) that measure the difference between the sellers perceived loyalty. m sellers (discrete grid) First & Second PCA Scores (~80% of the variation) Input PCA

11. Sellers Perceived eLoyalty: PCAs Most weight on medium-scores PC2 contrasts the moderate loyalty distribution from the extremes – distinguishes sellers that have neither extremely loyal nor extremely disloyal bidders Very little weight on low scores, very large weight on high scores (between 0.8 and 1 PC1 contrasts distributions of sellers with extremely loyal bidders with those that are little loyal

12. Research Questions 1. How to define and measure e-loyalty? 3. What factors drive loyalty in online auctions? 2. How does loyalty impact auction outcome (price, conversion)?

13. Modeling eLoyalty : Effect of eLoyalty on Price OLS/ WLS regression High volume sellers have multiple, inter-dependent auctions Low-volume sellers have only few auctions Violates regression assumption

14. Modeling eLoyalty : Effect of eLoyalty on Price Random-effects regression model Account for seller-specific variation Heteroscedasticity

15. Modeling eLoyalty : Effect of eLoyalty on Price Segment sellers into three groups

16. Modeling eLoyalty : Effect of eLoyalty on Price Segment sellers into three groups: model fit Low volumeMedium volume High volume R 2 =0.81 R 2 =0.77R 2 =0.83

17. Effect of eLoyalty on Price The effect of loyalty depends strongly on size of the seller: High volume sellers can extract huge price-premiums from loyal bidders The impact of loyalty is much smaller for sellers of smaller scale CoefficientMedium volumeLow volumeHigh volume (Intercept)-0.2104 StartPrice0.080.040.05 AuctionDuration000 log(ItemQuantity)0.120.140.08 Bidcount0.110.130.07 log(Pieces)0.190.080.36 Size0.03 0.07 log(SellerFeedback)0.04 0.12 log(Volume)-0.040.01-0.81 PC10.21-0.242.74 PC20.02-1.72-15.41

18. Summary Define and measure eLoyalty eLoyalty network Buyers loyalty ~ normalized distribution of out-degree Seller perceived loyalty ~ distribution of the weighted in- degree Transform the distribution into quantifiers using PC analysis Modeling eLoyalty: data segmentation Conclusions Loyalty has higher impact on high volume sellers Saturated market

19. Discussion The analysis can be replicated to other products; the results might change Temporal networks Examine the evaluation of eLoyalty We did not observe temporal effect in our data

20. More Information? Inbal Yahav iyahav@rhsmith.umd.edu http://www.rhsmith.umd.edu/faculty/phd/inbal/