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E-Loyalty Networks in Online Auctions

E-Loyalty Networks in Online Auctions. Inbal yahav  Wolfgang Jank R.H. Smith School of Business, University of Maryland. Motivation. Bidders. Sellers. Actors. We introduce the notion of eLoyalty. High profit High conversion rate. Get the product. Get the product (quality).

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E-Loyalty Networks in Online Auctions

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  1. E-Loyalty Networks in Online Auctions Inbal yahav  Wolfgang Jank R.H. Smith School of Business, University of Maryland

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

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

  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? 2. How does loyalty impact auction outcome (price, conversion)? 3. What factors drive loyalty in online auctions?

  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 100% 70% 30% 100% 80% 100% Sellers Buyers • Measure proportion of interactions per buyer (~normalized distribution of out-degree) Measure the perceived loyalty per seller (~distribution of the weighted in-degree)

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

  11. Sellers’ Perceived eLoyalty: PCAs 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 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

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

  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 volume Medium volume High volume R2=0.81 R2=0.77 R2=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

  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/

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