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Shopbot 2.0-Integrating recommendations and promotions with comparison shopping

Shopbot 2.0-Integrating recommendations and promotions with comparison shopping. Presenter : Wu, Jia-Hao Authors : Robert Garfinkel , Ram Gopal , Bhavik Pathak ,Fang Yin. 國立雲林科技大學 National Yunlin University of Science and Technology. DSS (2008). Outline. Motivation

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Shopbot 2.0-Integrating recommendations and promotions with comparison shopping

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  1. Shopbot 2.0-Integrating recommendations and promotions with comparison shopping Presenter : Wu, Jia-Hao Authors : Robert Garfinkel , Ram Gopal , Bhavik Pathak ,Fang Yin 國立雲林科技大學National Yunlin University of Science and Technology DSS (2008)

  2. Outline • Motivation • Shopbot • Objective • Experiment 1 • Methodology • Experiment 2 • Conclusion • Personal Comments

  3. Motivation • The current shopbots only focus compare prices of a single product of which they are already aware. • As the electronic commerce continues to grow and the competition among online retailers becomes more intense, retailers turn to various strategies to attract sales on the web.

  4. VendorDescript Online Vendors Shopbot • A kind of bot that searches the web to find the best price for a product you’re looking for. Domain Descript Purchase Request Shopbot Buyer Product User Best Price Result

  5. Shopbot 2.0 Relatedness score If you pay the $25 , you will have free shipping Price : 24.88 + 4.98 (Shipping cost) = 29.86 Price : 25.62 (Free Shipping)

  6. Objective • The authors argue that shopbots are in the better position to offer such recommendations like choosing the best bet from the choice set. • The authors develop integer programming models for shopbots to integrate sales promotions and product recommendations.

  7. Experiment 1 • The authors collected recommendation data from Amazon.com • Run the regression on the top 100,500,1000 • Variable • Sales rank : the sales quantity of a book. • No. of reviews : the number of customer feedbacks for a book. • Average star : the aggregated rating for a book by customer.

  8. Experiment 1 • The base items have lower sales rank and higher number of reviews. • The result show that base item and items didn’t have necessarily related. • It is obvious that retailers do not always recommend the most related items.

  9. Methodology • Retailers might recommend items of profit maximization. • Inventory clearance and targeted promotions of writers or books. • The default choice for Amazon.com is always the top book in the list. • The author’s method that choosing the optimal best bet. • Use the baseline savings , if base item with each item in the choice set that the savings are higher than the baseline savings , this item is marked as an best bet. • Promotions • Free items : one free item can give you that you have high purchased items and at least amount of money is spent. • Dollars off coupons : a minimum purchase amount gets the shopper a coupon that can be used at the next time. • Free shipping : a minimum purchase amount gets the shopper free shipping.

  10. Methodology (Cont.) • An integer programming model • Choice set set be indexed by • xi be a binary variable indicating whether or not the ith book is purchased and paid for. • Similarly fi indicates whether that book is chosen to be received free. • The retailer price of the ith book in the choice set is pi , while p0 is the price of the base item.

  11. Methodology (Cont.) • Free items :

  12. Methodology (Cont.) • Dollars off coupons : • There is a set of dollar-off coupons indexed by k = 1,…, . • An order of total expenditure no less than tk dollars yields a cost reduction of dk dollars off the total price. • Let yk be a binary variable indicating whether or not the kth coupon is used.

  13. Methodology (Cont.) • Free shipping : • Let z be a binary variable indicating whether or not the shipping is free. • If the total value of the order exceeds F dollars then shipping is free, otherwise the shipping cost is fixed at s dollars. • Overall budget : • Red line : free items , Blue line : Coupons , Green line : Shipping

  14. Methodology (Cont.) • Objective : • Let Li denote the list price of the ith book. The objective for the shopper’s economic gain maximization plus any saving from applicable promotions.

  15. Experiment 2 • The average savings from author’s best bets for Amazon.com ($16.23) are 33% higher than the benchmark($12.19).

  16. Conclusion • It is found that demand elasticity of recommendations does not change when the best bet recommended items are not from the choice set vs. when they are from the choice set. • The result show that author’s method is better than Amazon.com. • The online shopping website should give some promotions and recommendation.

  17. Comments • Advantage • A good recommend model. • Drawback • … • Application • Electronic Commerce. • Online Shopping.

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