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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 l.jpg

Modeling Seller Listing Strategies

Quang Duong

University of Michigan

Neel Sundaresan Nish Parikh Zeqiang Shen

eBay Research Labs


Motivation modeling ebay sellers activities l.jpg
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


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Goal

Construct a behavior model:

  • captures seller listing activities

  • incorporates historical data and sale competitions

  • across different product groups/markets

    Domain: eBay


Applications l.jpg
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


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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



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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


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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


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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


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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)


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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.


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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)


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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


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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


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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.


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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


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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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