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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

Modeling Seller Listing Strategies

Quang Duong

University of Michigan

Neel Sundaresan Nish Parikh Zeqiang Shen

eBay Research Labs

motivation modeling ebay sellers activities
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
slide3
Goal

Construct a behavior model:

  • captures seller listing activities
  • incorporates historical data and sale competitions
  • across different product groups/markets

Domain: eBay

applications
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
related work
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
data processing
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
data processing cont
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
markov model state and action representations
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

state action model
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)

model learning and evaluation
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.
empirical study
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)
comparison with the baseline semi uniform model
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
comparison with the history independent model
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
cross product analysis
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.
cross seller analysis
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
conclusions
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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