Mark Ferguson, G. Shang, P. Pekgün, and M. Galbreth Darla Moore School of Business University of South Carolina Estimating the Value of a Money- Back-Guarantee (MBG) Policy in an Online Retail Context 1

 Cost: processing returns  Benefit: higher willingness-to-pay The basic tradeoff for MBGs 2 no returns, $100 return & refund 1.Lots of research on the cost 2.Scant research on the benefit 3.Benefits are difficult to quantify consumer electronics sold online

How to quantify MBG value? 3 CompanyProduct priceForward shippingReturn shippingChange Amazonfull refundno refundconsumer payonce Best Buyfull refundnot clearconsumer payonce Dell85% to 100%no refundconsumer paynever HPfull refundno refundconsumer paynever Lenovo85%no refundconsumer paynever Sears85% to 100%no refundconsumer payonce Shurefull refundno refundconsumer paynever Sony85% to 100%no refundconsumer paynever Targetfull refundno refundretailer payonce Wal-Martfull refundno refundconsumer payonce 1.Three characteristics for a typical MBG 2.Little between-product variation in MBGs 3.Little longitudinal variation in MBG Hard to quantify the value of MBG

 Data from eBay: 3 appealing features 1.Match the three common characteristics  Refund for product price, no refund for forward shipping, buyer pays for return shipping 2.Variation in MBG policy for identical products 3.Consumer’s product valuation measured from auction prices How we quantify the value of MBGs 4 No MBGYes MBG Shipping chargeFree shipping

5 Structured web-crawling

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 Procedure: 1.Select product 2.Collect product information 3.Identify completed auctions of the product 4.Collect transaction information 5.Collect seller information  Outcome (after data cleaning)  2946 transactions of 86 consumer electronic products sold on eBay during 1 st quarter of 2013 Structured web-crawling 8

Product Price Differences: with MBGs versus without MBGs 9

Summary of Auctions Captured 10

 Desired economic interpretation:  If a seller switches from no-MBG to MBG, how much will consumers’ willingness-to-pay increase?  Main technical challenge:  Whether to offer MBG is an endogenous variable.  Consequence:  OLS biased  IV approach (e.g. 2SLS) biased IV approach  We use an error correlation based ML estimator to address endogeneity. Econometric approach 11

 Transaction Related: 1.Number of bids 2.Duration 3.Weekend/ time of day/ month 4.Order processing time 5.Shipping options (stand, economy, exped)  Seller Related: 1.eBay store 2.Seller tenure  Product Related: 1.Average price 2.Number of units sold 3.Number of reviews Regression Model Controls 12

 It depends on forward shipping cost:  It also depends on seller reputation:  More positive reviews increase value of MBG  More negative reviews decrease value of MBG Main estimation results 13 no returns, $100 return & refund Forward shippingValue of MBGHow much now? $0$5.2$105.2 $10$4.0$104 $20$2.8$102.8

Value of MBG as a function of the forward shipping charge 14

What should the shipping fee be? 15

 Value of MBG is smaller than 10% of the product value  Forward shipping fee:  Treated by consumers as an implicit restocking fee  Erodes the value of MBG very quickly  Makes the one-size-fits-all return policy even less optimal for online retailers  Our increase in valuation estimates can be combined with the cost of returns to construct a MBG optimization model Key findings 16

 A seller who offers free forward shipping and has an average reputation could expect 5.16% increase in consumer’s product valuation if it switches from not offering MBG to offering MBG  A forward shipping charge erodes the value of a MBG policy significantly - if 20% of total price paid is attributed to shipping, then the value of a MBG is close to zero  Positive and negative seller reputations have separate and opposing effects on the value of MBGs Key take-aways 17

Thanks for your participation and feedback! Questions? 18

 How does endogeneity arise?  DV: consumer’s product valuation  Endo. Regressor: seller’s decision to offer MBG  Some unobserved factors affect both  What are these unobserved factors?  Example: return-related seller reviews  Affect likelihood to offer MBG  Affect product valuation when there is MBG  Both IV and our approach account for these IV versus our approach 19

 However, IV also assumes:  Return-related reviews will also affect product valuation when there is no MBG.  That is, return experience matters even when there is no chance to return.  Not realistic…  In contrast, our approach does not make this assumption  We model two error correlations:  one with no-MBG, the other with MBG  they can be different 20 IV versus our approach