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Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
Stephen darori the  bidding process of e bay
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Stephen darori the bidding process of e bay

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  • 1. EBAY The Bidding Process
  • 2. INTRO: WHAT IS EBAY?  Founded on September 3, 1995  Successful company from the dotcom bubble  Founder is Perre Omidyar  Online auction company   Nearly 128 million users Interesting Fact:  One of the first items sold was a broken laser pointer for $14.83
  • 3. eBay user IDs ID more than 6% IDs more than 27% 55% 12% ID, but more than not used in past year other • Survey by AuctionBytes.com conducted in 2001 • Out of the 33 million members only 13 million may be active • The 33 million includes members on all of eBay's sites • Currently, 128 million eBay users • According to the US Census, there are 310.4 million people in the US • Probability that a random person is an eBay user = 41%
  • 4. AUCTION PRACTICES  Three different software types Vendio  Seller Pro  Channel Advisor   Process      Register user ID Search for products Bidding Buy it now eBay Express
  • 5. CORRELATION OF NUMBER OF ITEMS AND NUMBER OF BIDS  Research of 3500 items  How many bids does each item get?  How many bids would you expect an item to receive?
  • 6. EXPECTED VALUE OF NUMBER OF ITEMS AND NUMBER OF BIDS Number of Bids P(Number of Bids) e(Number of Bids) 0 1891 54% 0 1 797 23% .23 2 174 5% .10 3 125 4% .11 4 122 3% .14 5 87 2% .12 6 57 2% .10 7 43 1% .09 8 36 1% .08 9 33 1% .08 10 Total Number of Items 135 4% .39 3500 Expected Value 1.43
  • 7. Probability Distribution of Number of Bids
  • 8. ARE AUCTIONS REALLY WON AT THE LAST MINUTE?   Research of 3,500 items Took away items that received zero bids or won with ‘Buy It Now’  Left with 1,413  Out of those 215 were won in the last minute  When are the most items won in the last minute?  How many seconds before the end of the bidding period are items won?
  • 9. EXPECTED VALUE OF ITEMS WON IN THE LAST MINUTE OF BIDDING Seconds Won Before End Number of Items P(Seconds Won Before End) e(Seconds won Before End) 0 .93% 0 1 28 13.02% .13 2 1 .47% .01 3 8 3.72% .11 4 1 .47% .02 7.5 53 24.65% 1.85 15 53 24.65% 3.7 25 27 12.56% 3.14 37.5 31 14.42% 5.41 52.5 Total 2 11 5.12% 2.69 215 Expected Value 17.05
  • 10. PROBABILITY DISTRIBUTION OF SECONDS WON BEFORE END to to to to to
  • 11. HOW LIKELY IS IT THAT AN ITEM IS WON BEFORE THE LAST MINUTE?   Total Sample – Sample Used = Rest of Sample 3500 215 = 3285 Rest of Sample / Total Sample = Probability of Items Won Before Last Minute 3285 / 3500 = 94%
  • 12. CONCLUSION     Most items listed on eBay do not even receive bids. When accounted for in the last minute of bidding, the most items are won during the last 5 to 20 seconds. However, out of the sample taken, 94% of items are won before the last minute occurs. Therefore, unless special circumstances exist, it really isn’t necessary to keep constant watch on your bidding to ensure you win it.
  • 13. REFERENCES   http://en.wikipedia.org/wiki/EBay http://www.galttech.com/research/internetecommerce/ebay-for-beginners.php

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