computers, waiting for a man and things that he would bring with. These night owls formed an united group, with their enthusiasm ignited by the belief that their lives would rapidly improve following the live stream. You may have guessed what I am talking about if you don't stray far from the Internet world. Actually, there is no way for you to escape from buzzes about the Apple Product Launch, no matter the source was your Facebook ,Twitter or other social network platforms. News media also bombed people with their interpretation and views.
On the other hand, Apple company are busy with their own issues. Supplying the new iPhones to meet the demands of booming customers is not easy. There are countries and areas who are on the list of first release. Hong Kong was included luckily. However, as Apple's largest market, supply to mainland China was delayed due to license issue. Since residents of China cannot buy iPhones directly through online order (via Hong Kong website), many mobile sale agents from China rushed to Hong Kong to purchase the product in short supply. Due to Hong Kong identity restrict, they can only buy the products from Hong Kong residents. Chinese who are eager to try the new phones must pay much higher price than official one.
Such panic buying forms a large-scale and short-term market with high returns for people living in Hong Kong. The gap between the prices (one for official buying price and one for your selling price to sale agents) of an iPhone 6 plus can reached 5k ~ 7k HKD. As an individual seller, what I am concerned about is how to determine a price to make a good enough profit?
This is actually a pricing problem :)
However, it's not a normal pricing problem for a company to determine the proper price for a product. There's indeed one product (maybe of limited types, but we'll assume only one). But sellers and buyers are totally isolated from each other in real life.
As a seller, you want to determine the best price according to the market. In normal product marketing, sellers acquire detailed selling information from vendors, so they can adjust the price accordingly. But in this iPhone case, one seller are not accessible to the selling information of another directly. Moreover, if the seller goes directly to ask the bid offered by a sale agent, he(she) would probably feel the bid is too low and is reluctant to sell. One can enumerate consulting as many agents as possible. The best outcome is picking the highest price from all bids. Before you make a final deal with the agent, you may reconsider your customer.
Is it a good idea to sell the iPhone to a sale agent? Actually, there are different channels for one to sell his(her) iPhone. The most beneficial way is to sell it to the end user. Taobao.com maybe a good place to sell one's goods, but in this particular case, face-to-face transaction could be a more safe and convenient approach. Before that, the buyer and seller have to make an agreement on the price. There is actually a website for people to post iPhone's selling price in Hong Kong. http://www.itradephone.com
A seller can refer to others' bidding to determine his price for the iPhone. The virtual market, formed through an online platform, bridges one to other sellers, which in some sense resembles a real-world "single" amalgamate seller. These anonymous sellers formed tied relationship with same goal.
One can browse through pages of bidding record to choose a price. This method is empirical, and suitable to sellers who want to make quick choice. But let's be a little verbose and technical. If we assume 10% of bidding price is too high and 10% too low, we can avoid being too optimistic by plotting the distribution of the bidding price from the history records.
The analytical process can be can be done as follows:
1. Download the web pages and filter out the pricing data item.
2. Cleaning the data. Flush out the data that's below the Apple's official price, and unqualified input.
3. Process the data items to make a uniform integer type, e.g. 8000 (not 8,000 or 8000HKD)
4. Plot the histogram using python library.
5. Pick out a satisfying price according to the histogram.
The most challenging part is data preprocessing step. I will try to implement the coding in the next blog ;)
Very good post, I find your blog is very pratical, you will introduce some useful algorithms and tell us a pratical problem which it can solve, from this blog, I learn how to set a satisfying price using the data in web pages, I am also very interrested in implementing it.
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