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Modeling Seller Listing Strategies

Modeling Seller Listing Strategies. Quang Duong University of Michigan Neel Sundaresan Nish Parikh Zeqiang Shen eBay Research Labs. Motivation: Modeling eBay Sellers’ Activities. A majority of eBay sellers are individuals or small sale operations ( heterogeneous )

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Modeling Seller Listing Strategies

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

  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

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

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

  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

  6. Overview

  7. Data Processing • 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

  8. Data Processing (cont.) • 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) • Product category: seller adopt the same strategy for products in the same product category • For example, product: black/silver iPhones; product category: iPhone

  9. Markov Model:State and Action Representations 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 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 M1 and M2 L(M1,M2)= L(M1) / L(M2) (smaller than 1 means M1 is better than M2) • Final measure: 1 - L(M1,M2)  How much M1 is better than M2.

  12. Empirical Study • Examine activities of the best performing seller (S0), second best seller (S1), and an average seller (S2). • 3 months worth of data (2/3 for training, 1/3 for testing) • Three product categories: charger, battery and screen protector (for iPhones)

  13. Comparison with the Baseline Semi-uniform Model • Semi-uniform model (M0): • Pr(do-nothing|state) is 50% • other actions are randomly uniformly chosen. • Results for top seller S0 and second-best S1 • Sellers do adopt strategies for their listings

  14. Comparison with the History-independent Model • History-independent model (Mh): • does not incorporate the last action • Results for top seller S0 • There are benefits of including information about last actions in capturing listing strategies

  15. Cross-product Analysis • For seller S0, across different product categories: • M1 | 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.

  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

  17. 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

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