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Essay, 12 pages (3000 words)

Amazon strategy essay

So in this paper, I will talk about the others I didn’t mention in midterm paper. And at the last in this paper, I will talk something about the Amazon in China. I think it will give American some interesting points. Buy the way, this year I just gave up using Amazon to find the Black Friday goods. I spent almost 4 hours on waiting at the gate of BestBuy instead of waiting on Amazon. And I still lost the change to get the best goods. Because of someone got all the tickets and sold them to the people who really needed them.

In this situation, I thought the 20th in the queue had nothing different with the 200th. Customer service Like I have mentioned in my midterm paper, Amazon put the customers on the first class and it spends a lot of employees to serve the customers. And Amazon also serves the best website service to customers. Amazon makes a date list to tell customers its deal. Like icloud Amazon has its Amazon cloud player, which allows customer to keep the music safe in the cloud and play it anywhere. Unlike iCloud, the music could be played on any devices which include Kindle Fire, Android and iPhone.

And Amazon also has its Amazon cloud drive, which is 5GB of free online storage. In this situation, Amazon is not only an online book store, but also an IT company. And customers’ feedback builds the community for customers to share the information and grade the products. All the customers read feedbacks before they do their shopping. And then in this situation, there is a biggest issue that we should face are we should trust the feedbacks? I will talk about this question later. DSS I mentioned DSS in my midterm paper and now I will introduce DSS specifically.

Decision Support Systems (DSS) are a class of computerized information system that supports decision-making activities. DSS are interactive computer-based systems and subsystems intended to help decision makers use communications technologies, data, documents, knowledge and/or models to complete decision process tasks. A decision support system may present information graphically and may include an expert system or artificial intelligence (AI). It may be aimed at business executives or some other group of knowledge workers.

Typical information that a decision support application might gather and present would be, (a) Accessing all information assets, including legacy and relational data sources; (b) Comparative data figures; (c) Projected figures based on new data or assumptions; (d) Consequences of different decision alternatives, given past experience in a specific context. And when we focus on data mining, I consider Amazon as the Data driven DSS. Because of most data driven DSSs are targeted at managers, staff and also products or services suppliers.

It is used to query a database or data ware house to seek specific answers for specific purposes. It needs several methods to collect data and analyze data. But when I notice that Amazon relies on its technology to become the best company, I think Amazon’s DSS is the knowledge driven. It is an IT company more than a B2C company. And when I read more materials, I found that Amazon considers itself as an IT company. In this way, Amazon relies it knowledge driven DSS to support its business. BI It is an important part for B2C company and Amazon is famous its recommendation system.

So in this part, I will focus on recommendation system. As I have mentioned in my midterm paper, association learning it the type of data mining that drives the Amazon recommendation system. In this kind of data mining, it is to find what the customers will need next. And I found some interesting definition about association rules in data mining. Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a relational database or other information repository.

An example of an association rule would be “ If a customer buys a dozen eggs, he is 80% likely to also purchase milk. Just think of that, when I raise my hand, there might be several actions that I can do next. The system will make a list of what I will do next, in this list it might include “ touch glasses”, “ touch hair”, “ touch ears” and “ touch nose. ” Every action has its probability of occurrence and the system will pick out the highest one. At last, the result will be returned to the system and be displayed to the users. An association rule has two parts, an antecedent (if) and a consequent (then). An antecedent is an item found in the data. A consequent is an item that is found in combination with the antecedent.

Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Support is an indication of how frequently the items appear in the database. Confidence indicates the number of times the if/then statements have been found to be true. In my opinion, association rules are useful for analyzing and predicting customer behavior in data mining. They play an important part in shopping basket data analysis, product clustering, catalog design and store layout.

After that, I focus on recommendation system and I will introduce four recommendation algorithms that are used most frequently. The first one is related recommendation, which is the simplest one. It means the website consider customer research, scan, shop and rank as the whole entity. And then the recommendation system picks out the key words such as authors, titles, brands and so on. After that the system research to find the products which are direct correlation. For example, if a customer buys a movie, the system will think the customer like the director and then recommend other movies made by this director.

This system prefers to find the words which are researched frequently and then recommend that items to customers. But on the other hand, the system doesn’t have any AI and always makes the mistakes. This system could understand the really need for a customer. So now almost every e-business websites give up this system. After that, there is a recommendation system which is called shortcomings of common recommendation system or person-to-person recommendation systems and in some part it is considered collaborative filtering recommendation.

In this system, it collects all the customer information and serves the individual. The person-based recommendation is the 1st collaborative filtering recommendation system. It separates people in different groups and the system wants to find the same favorites. For instance, if a group of people buy the same products in the website, these people should have the same favorites. The system wants to find the customers who scan the same products or buy the same products and then the system will analyze their data to compare the customers to find the customers who are more likely to have the same taste.

And then the system will recommend the products that customer A has but customer B doesn’t have. But the system doesn’t recommend every product, at first it will collect all the products and then sort the products to find the top product to recommend the customers. Person to person is more effective than related recommendation but it is limited by the large number of customers. Like Amazon or other large e-business companies, the quantity of their customers is almost 0. 3 billion so they couldn’t use the person-to-person recommendation system.

Because the computer needs 10 million times to compute the similarity between the customers. In contrast, when computer computes the similarity between the items, it will cost 100 thousand times, which will save a lot of time. And then Greg Linden who is cofounder, researcher, and senior manager in the Amazon. com Personalization Group designed and developed the item-to-item collaborative filtering. Generally speaking, this collaborative filtering is to find the items which have potential relationship.

And then the system is according to the customer shopping history to recommend the items which have potential relationship with the items that customer have bought. There is an example to explain the potential relationship, in 1990s the shopkeepers found the “ Beer and Nappies,” that beers and nappies always appeared in the young male customers’ baskets. The reason is that the young male customers will take a box of beer when they were asked to buy the nappies by their wives. Then the shopkeepers realized that if they recommended beer to the man who bought nappies, they would be always successful.

So there is a conclusion that if two different items are bought by the same customers, there is stronger potential relationship between two items. When the item to item recommendation system deals with the large number of customers and products, it will respond faster than person to person system. At first, my Amazon recommendation is this. And then I search “ apple. ” I click the apple mouse and then I return back to home page. When I click another item, the home page has changed. After I searched “ TV”, “ kindle fire”, “ beer” and “ nappies”, my home page has changed like this.

At last, I will introduce the special one the key words. In this article “ recommendation systems know everything”, the author said that when people don’t know what they really would like to take, they almost use the key words to research. And then the website will use those key words to recommend the items. Because the key words are considered as the tags for the items, for an item the more tags for an item, the more relationship it has with others items. The website will collect those tags and recommend the item which has the high relationship with the tags.

But if one item has too many tags, it will reduce the shipping range. It will limit the customers’ view on shopping. But there are still risks on recommendation system. The first one is how to avoid be impolite recommendation. Sometime the customers might be sensitive, so the system needs to use the special way to recommendation or just give up recommendation. And there is another question which is how to protect the customers’ privacy. Sometimes a person might not like one’s items are recommended to one’s friends such as the pills to lose weight or some privative items.

And in this situation, Amazon allows customers to delete their history. BI Need and Issue As we know, almost every customer on Amazon prefers to read the feedback at first and then they will depend on the feedback to choose which items they should buy. But it will lead to a question that “ Can we trust them? ” It is impossible to avoid business fake, so the customers and the company should build the system to tell the truth and fake. The wrong feedback will mislead the customers and then impact the sellers’ credit. And sometimes, one person could also employ some people to rank his competitors a low grade.

So the company needs to design a system to tell the useful and fake reviews. The most significant part costumer reviews Except the customer reviews, there is another need or a potential need should be solved, which is real time recommendation. Just thinking this situation, a person is just break up several days but the website just save the history when he was falling in love. And at that moment, the website wants to recommendation some items that are fit to couple. In this situation, website will hurt the person and lose the customer. Similarly to the impolite question, this question is also lack the real time data.

And the real time data could also improve the rate of successful and build the good customer impress. So this question should also need a system to deal with it. In this situation, the company need two different systems, one is the system to tell the truth and fake, the other is to build a real time data collection and analysis system. To solve this system, I found an article

“ Amazon customer reviews – can we trust them? ” In this article, a team of researchers from Cornell has developed a classifier to detect what they call “ deception opinion spam. But the whole system (of customer reviews) fold apart if made up reviews are given the same weight as honest ones. In their research, they contacted as many as possible of the top ranked Amazon reviewers and ended up with data from 166 top ranked Amazon reviewers, discovering that compared to the demographic of book buyers they were disproportionately male (70%)  They also differed from typical Amazon customer by being older, the peak age group was 51-60, and better educated – over half had Masters or Doctorate and 92% had at least college degree.

Another interesting finding is that 39% of the reviewers are also authors. As regards motivation, the research finds that 80% of their respondents are motivated to contribute reviews for “ self-expression” and “ enjoyment”, 85% them report being sent free books or other products to review by publishers and authors. And in this article, the searchers give the customers some tips to tell the fake reviews:

If there is only a single review of the book in question and it has five stars and lots of adjectives like “ great”, “ wonderful”, “ best book ever written”… hen suspect that it is a review from a friend of the author. If a reviewer has posted a few reviews but the ranges of items reviewed aren’t related to the book in question, then the chances are that these are dummy reviews added to make the book review look more real. Be suspicious if you find reviews of things that don’t normally need a review, such as a pack of razor blades or a toothbrush. Another clue as to a review being suspect is when a single review of an advanced title is coupled with two or three reviews of some very trivial books.

Ask yourself if the reviewer’s reading habits seem coherent. The final sign that a reviewer is motivated by being offered freebies rather than providing a balanced opinion is when they have produced a large number of reviews but they are all five star. How can you trust a reviewer who thinks that everything is fine about everything? And sometimes, the authors will break the rules, which could enhance their books quality. The useful way to find a useful review is to follow others reviews that are written by the same person.

And then to find if the reviews are all low scoring or depend on different books. After that the customer should also notice if the different books are written or published by the same authors or companies. So this system should have the ability to distinguish the useful information and useless information. All the functions could follow the researchers’ experiences. The process is like that “ collecting”, “ cleaning”, “ analyzing” and “ conclusion. ” In this process, most fake information will be picked out and the customers will get more useful reviews.

But if there is a kind of people who just read the worst books and write reviews, their reviews will also be cleaned in this process. So in this system, the issue is that it could not avoid the real reviews which are all low scoring. It still couldn’t solve this problem that when people avoid making negative reviews, they will just give positive reviews. And to be honest, it seems like this issue could be solved by technologies or knowledge but it still has a long time to go. When talking about the real time data collecting system, I think Amazon should connect with Facebook and twitter.

Amazon has two ways to solve this system. One is Amazon build its own social network. It allows customers to log by using their Amazon accounts. And the customers can share their items or their emotion. They can @ their friends by posting pictures or a web link. When customers use this social network as Facebook or twitter, Amazon will collect enough real time data and then it can recommend products more effectively and accurately. The second way is to serve the interface that allows people use their twitter accounts or Facebook accounts to log in Amazon.

And then when the customers update their Facebook or twitter, the Amazon could also take the real time data. But when Amazon wants to build this system, first of all Amazon should take the permit from the customers. Because when Amazon collects the real time data, it also means Amazon steal the customer private information. In this situation, Amazon should make a contract that Amazon will never sell the customer information and never reveal that information to the third party. After that when Amazon serves the interface to Facebook or twitter, I think it still has a long time to go.

Negotiation is a large part in business. Even though social network is the best way to take real time data, technologies and ethics are the hardest point. Amazon China This part is an additional part in my final paper. When I take the Data Mining course, my final is to analyze MCD and its opponent. In my final paper, I select the oversea market to analyze MCD and Yum. Sometimes the oversea market will illustrate some interesting questions which will never appear in US. In China, Amazon is facing the challenge that Amazon still uses the strategy which is used in US.

And Amazon China never uses the price war to win the market. In this situation, Amazon might lose Chinese market. There are two pieces of news in Chinese e-business field. One is just several days ago, Nov 11st, the biggest Chinese e-business company, Tmall, generates 19. 1 billion RMB turnover (almost 3. 03 billion Dollars), which is only in one day. But the other news is Amazon China CEO will leave this company. In China, the first thing the international e-business companies should know is the rule in Chinese market. To be honest, in China all the companies could make the price war.

A company could reduce the product price as low as it can without considering integrity. Just in Nov 11st, some companies posted the low price on the website but when customers want to buy the products, they found the products are sold out. Sometimes the companies make the lower price products as the strategy to make the customers shop in the website. In that day, the website raises the price and then makes a big discount and then the companies use this way to catch the customers’ attention. Even though those companies break the law, they aren’t received punishment.

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