new type of internet auction sites - bid20.com

Recently a friend of mine mentioned about a new twist to internet auction sites - bid20.com. The site claims that you can win item at a very low price. Is it really true?

How does bid20 auction work? Here are some steps:

1) Buy N bids at the price specified. Each bid is prices > Rs 10, in bundles specified by bid20
2) Go to the item listing and bid one at a time when u r not the highest bidder
3) The last bidder will be the winning bidder. All the other bidders who had bid lost the money for the bids they've made.

What does the winner get?

Let us consider an item that has an MRP of Rs 1000, and assuming that each bid costs you Rs 10, here are some scenarios:
1) You are the only bidder. In this case, you'll only bid once and your cost is Rs 10, and you'll get the item for Rs 10. This is good for the buyer, and a loss for bid20. This is a very rare case.
2) When there are more than one bidder, say 20 bidders. During the bidding process let us say that each bidder bids an average of 10 bids, and you are the last and the winning bidder. In this case there are a total of 200 bids, which means the price realized by bid20 is Rs 2000, which is good for bid20. This is also good for the winner because the cost for the winner is only Rs 100 (10 bids x Rs 10). However, there are 19 bidders who lost an average of Rs 100 each, and are potentially not happy about the loss. (Note: they might recover this loss by winning another auction - gamble?)
3) Scenario same as 2. However, the winner steps in just before the auction close, and after 190 bids have come in. He has more resources to bid at because he is starting from zero and the others at an average had already bid more than 15 times. Thus, the late entrant bidder, and more importantly very sparse bidder or a bidder who spends his bids miserly, is going to benefit a lot because he needs to spend fewer bids to win the auction. Bid20 is still not at loss. The winner here will maximize his returns. However, the initial bidders will have maximum dissatisfaction.

Who is at a loss?

Often, people do not realize the potential for loss. Auctions bring out impulsive nature in the bidders. Many of them don't analyze or realize how go gain maximum out of minimum bids. Also, people are used to ebay style bidding where the bidding does not cost you, and  one has to pay only as a winner. However, in bid20 style auctions, people start losing money (except the winner) with every bid. This is where the potential for a loss exists.

Business Model for Bid20:
1) Bid20 benefits a lot because (a) it gets money upfront by selling the bids - whether the buyer spends the bids or not is not its concern (b) The price/bid can be adjusted by it at the time of selling the bids, which makes it realize the price of the item at the rate it decides.
2) It can make a loss if there are not many bids on that item. However, bid20 says that it reserves a right to cancel the auction - it can protect itself.

The only concern for this business model is the user dissatisfaction. Over a long run, users realize that they're losing money when they bid and they not being the winner. When people realize that winning is possible only if they enter the auction at the closing stages, each auction will see traffic at the last moment. However, there is a catch here. The auction can get indefinitely extended if bids are received within 15 seconds of the auction end. Thus, there is no right time to enter the auction unless you see lots of people had already bid on the item.

Conclusion: The bid20 model will work and will have a buzz factor until users realize what they're getting into, and realize that they are accumulating losses. Once the buzz factor fades away and user realization happens, the user participation will go down. The Nov'09-Feb'10 Alexa traffic plots indicate this trend. Bid20 can continue with their model by bringing in new users; however, retaining the users might be difficult in the current model. Some new aspect must be introduced into this model to make it work over a longer period.

Notes from internet Auction History: There were auction sites like ubid, yahoo-auction, etc., that had the concept of extending the life of auction when the bids happen within certain time frame of the end of the auction. All these auction sites had decline in traffic while ebay kept gaining share. The sellers were happy to have fixed-time auction because of better operation management, while the buyers were happy to set a maximum price and time for an auction, and spend less time for buying an item. May be, sites like bid20 must take some hints from the internet auction site performance history and reasons for their demise, and change the way they operate so that they can survive for longer periods.

SEM by startups - can they win the war!

Several startups use SEM for marketing. Is this really a good idea? The answer is yes and no.

It is a 'yes' because people get instant exposure to these sites based on the bid words. Traffic to relatively unknown websites (like startups) are very welcome because website visitors are always the revenue generators.

Can this 'yes' transform into a 'no'? The answer is 'yes'.

Let us go back and look at SEM on Google. A startup xyz.com bids on keyword(s) and gets itself to the top rank for those keyword(s). Similarly, a very well known company pqr.com, which also has a good SEM click history bids on the same keyword set. The bid value of xyz.com will be higher than pqr.com. When the user searches using the keyword, Google will show these first xyz.com and then pqr.com in the advertising box. Let us consider the following scenarios:

1. xyz.com has a better ad content. The user is impressed and clicks on the advertisement. xyz.com is charged for this click. Many sites have very high bounce rates. The cause for the bounce off could be because of the site content, the site organization and flow, and also because of the trust level of the user on that site (xyz.com). Majority of times this click results in a loss to xyz.com. However, the SEM optimization engine thinks this as a success for xyz.com and increases its credibility/trust factor. A good vote in favor of xyz.com. If b% of users convert then the effectiveness of the amount spend is b%


2. The user does not click on xyz.com even though it is at a higher rank but, this user clicks on pqr.com because pqr.com is a known website. There are publications that indicate the effectiveness of having ads positioning at positions 2 or 3. At these positions, statistics indicate higher click through rate. This click for pqr.com (and not for xyz.com) reduces the trust factor for xyz.com.

For a relatively unknown xyz.com the probability of 2 is much higher than 1. Thus, the SEM optimization engine is likely to penalize xyz.com because it did not receive the clicks even though it was positioned higher. Now, if xyz.com wants to maintain its ranking, it needs to bid higher. Further clicks not-in-favor of xyz.com will cause xyz.com to bid higher for the same set of keywords. The same is not true for pqr.com, which has already established its trust factor. Also, the b% effectiveness due to bounce rate also adds to the SEM expenses of xyz.com.  Thus, in SEM space, startups rarely win a war against well established companies.

What are the alternatives?

One alternative is to reduce the cost of SEM while keeping the effectiveness of keywords higher. This is something SEM experts are known to achieve. These experts are known to find keywords that are effective and cost low (mainly due to lack of competition). However, their search volume is also very low. A startup has to identify and dynamically modify its keyword set on which it bids to increase visitors through SEM program. This is a search through a long tail of rare search keywords, and user typography errors. Can this search go on for ever?

There is this other alternative some companies had adopted to get to their target user base. Instead of making users come to their website, these companies started to go to the users by identifying areas where the users are more likely to visit. One such approach is to create applications on social networking sites like Facebook, Orkut, and MySpace. Partially, this was a step in right direction. Users, who rarely visit sites like xyz.com, would visit/add the application on facebook/Orkut/... with some curiosity, and after some clever marketing strategies by the application developers. After spending considerable amount of time on this application, these users get used to the content of the xyz application, which establishes a trust factor. Now, getting these users on social networking sites to xyz.com is another big hurdle. Note: I did not see an application that had converted the entire user base to its independent website. Thus, this step is 'partially' in the right direction. It only established the brand of the application. It would have been a completely successful approach if it had made people come to xyz.com.

Disclaimer: xyz.com and pqr.com were used just for explanation. These pointers are not to the actual websites (I did not check  if real websites exist and what they do). Also, my thrust on the trust factor being one of the variables in computing the bid value/ranking for a site by Google is based on some discussions I've read over the internet about Google's way of optimizing its SEM systems.

Netflix Prize Contest - recommendations

Netflix prize contest is about designing a recommendation system that can do 10% better than Netflix's own reference system. The grand price was set to $1 million. It had the buzz factor when it was launched. Very soon, people had realized that it is very difficult to achieve the 10% improvement target that was set by Netflix.

People tried several approaches. One of the very popular initial approaches was "Simon's Approach", which was almost similar to the LMS algorithm.

Researchers are trying several methods to grain incremental gain in the score. They are spending significant energy, time, and effort in achieving the target. Many of these researchers are combining their individual approaches to obtain further improvements.

Yes, after trying several approaches and tweaking the algorithm parameters, these researchers might reach the ultimate goal of 10% improvement. However, are all these approaches practical to implement? Even if these could be implemented very efficiently, do these really give good movie recommendations for the real user? Finally, can these approaches be used for recommendations in other domains?

This research has brought many teams to collaborate with each other. It has given graduate students a research topic to work on and get their thesis/publications. More importantly, this research has given a common ground for researchers to compare individual algorithms.

Finally, I'm not sure how much Netflix benefited from the practical use of the submitted algorithms and approaches. It has definitely benefited in terms of getting the right to use several algorithms in their recommendation system, and getting hundreds of researchers to work on a problem by paying a small amount (per annum per researcher).