1. Modeling Seller Listing Strategies Quang Duong University of Michigan Neel Sundaresan Nish Parikh Zeqiang Shen eBay Research Labs 1

2. Motivation: Modeling eBay Sellers’ Activities A majority of eBay sellers are individuals or small sale operations (heterogeneous) eBay platform provides a wide variety of options for listing for-sale item 2

3. Goal Construct a behavior model: captures seller listing activities incorporates historical data and sale competitions across different product groups/markets Domain: eBay 3

4. Applications 1.Identify and foster good (listing) practices: advise and suggest good practices to average sellers. 2.Assist market design –For example, eBay platform changes: how changes impact sellers’ strategies 4

5. Related work Benefits of “Buy it now” [Anderson et al. 2004] Clustering sellers [Pereira et al. 2009] Statistical models of agent’ listing strategies [Anderson et al. 2007] Our model incorporates: dynamic elements interactions among sellers 5

6. Overview 6

7. Data Processing 1.Product Clustering: –Need to group listings of the same product –Use a catalog: match each listing to a product in the catalog Match product name and brand Count the number of matched words between product’s catalog description and listing’s title 7 Seller IDProduc t ID Start DateEnd Date PriceTitleShipping ‘ABC’1003/20/200 9 04/10/20 009 $100Silver Nano iPod Apple $0

8. Data Processing (cont.) 2.Data summarization: Assume sellers adjust their listings in 1-week intervals. For each 1-week interval, each product and each seller: –Average price –Relative average price –Number of listings –(Percentage of free-shipping listings) –(Percentage of featured listings) 3.Product category: seller adopt the same strategy for products in the same product category For example, product: black/silver iPhones; product category: iPhone 8

9. Markov Model: State and Action Representations 9 State: price relative price number of listings shipping feature State: price ({low, med, high}), relative price ({low,med,high}), number of listings ({low,med,high}), shipping ({free,not free}), feature ({yes,no}) Assumptions: –Markov property: only dependent on the immediate state (relaxed later) Action: Adjust price Adjust number of listings Adjust shipping cost Adjust feature selections

10. State-Action Model 10 State: price, relative price, number of listings, shipping, feature Past action Action: Adjust price Adjust number of listings Adjust shipping cost Adjust feature selections Probability: Pr(action|state)

11. Model Learning and Evaluation Learning Given training data D, learn model M’s transition: Pr(action|state) Each data point is computed over all listings for one product (in one particular product category) in a week for a particular seller. Evaluation Given testing data D’, compute the log likelihood of D’ with M: L(M)=avg(log(Pr(action|state)) Given two models M 1 and M 2 L(M 1,M 2 )= L(M 1 ) / L(M 2 ) (smaller than 1 means M 1 is better than M 2 ) Final measure: 1 - L(M 1,M 2 )  How much M 1 is better than M 2. 11

12. Empirical Study Examine activities of the best performing seller (S 0 ), second best seller (S 1 ), and an average seller (S 2 ). 3 months worth of data (2/3 for training, 1/3 for testing) Three product categories: charger, battery and screen protector (for iPhones) 12

13. Comparison with the Baseline Semi- uniform Model Semi-uniform model (M 0 ): –Pr(do-nothing|state) is 50% –other actions are randomly uniformly chosen. Results for top seller S 0 and second-best S 1 Sellers do adopt strategies for their listings 13 ChargerBatteryScreen protector M S0 vs M 0 77.9%69.8%77.4% M S1 vs M 0 67.1%62.8%57.7%

14. Comparison with the History- independent Model History-independent model (M h ): –does not incorporate the last action Results for top seller S 0 There are benefits of including information about last actions in capturing listing strategies 14 ChargerBatteryScreen protector M s0 vs M h 76.1%67.9%61.2%

15. Cross-product Analysis For seller S 0, across different product categories: –M 1 | D’ 1 (D’ 2 ): model trained on product category 1’s data, tested on product category 1(2)’s data The top seller appears to execute relatively different strategies for different product categories. 15 M 1 vs M 2 | D’ 1 M 2 vs M 1 | D’ 2 Charger vs Battery30.1%25.3% Charger vs Screen protector 36.9%22.1% Battery vs Screen protector 32.7%40.6%

16. Cross-seller Analysis Compare different sellers’ strategies for the same product categories: The best and second-best sellers have similar strategies in the two product categories: charger and battery, but different strategies for the screen protector. The top seller and the average seller diverge significantly for both charger and screen protector 16 ChargerBatteryScreen protector M s0 vs M s1 | D’ s0 10%5.6%45% M s0 vs M s2 | D’ s0 69%N/A60%

17. Sale-through Rate and Average Revenue Analysis We want to compare the effectiveness of seller 0 and seller 2’s strategies: –Sale-through rate –Average revenue Challenge: listings created at time t may affect sales of previously created listings Solution: –Listings sold < 2 weeks after posted are counted as the original action’s effect –Listing sold >= 2 weeks are counted as the newest action’s effect 17

18. Conclusions Contributions: –Introduce a model that captures sellers’ listing activities, accommodates probabilistic reasoning about their behavior, and enables the inclusion of historical information –demonstrate the application of our model in comparing listing strategies from different sellers across different product categories 18