商务英语-4人组-会议对话脚本

商务英语-4人组-会议对话脚本(总

8页)

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G: Good morning, Jingdong Corp. Can I help you?

N: Hello, I`d like to speak to h, please. G: May I ask who is calling, please?

N: My name is n of Tianjin University. Last time, we talk about the project about the big data applied to electronic commerce. When would it be convenient for you to come We can discuss the details.

G: Oh, wait for a second. I will check our schedule. Well, how about this afternoon N: Ok.

H: OK, everyone. Let’s get the ball rolling. Come and sit down. We will start the meeting. First of all, a few words on our company—Jingdong shopping mall. It is one of the largest e-business enterprises in China. And the products are in many species, nearly all of which can meet us in our daily lives, such as computers, mobile phones and other digital products, home appliances, clothing and so on.

G: With the rapid development of economy and the increased numbers of client, the competition between electronic supplier is fiercer than before. We expect that our company can realize the maximization of the interests

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through cooperating with your laboratory. I hope you can talk about your opinions.

L: Recent years, big data has applied into a lot of E-commerce. We believe that it will be very helpful for your request. Actually, our lab are doing some research about big data recently.

H:Can you tell the detail about the technique of big data

N: Big data have four attributes. They are volume, variety, velocity and value. First, the data volume is huge. From the level of TB, jumped to PB level. Second, it has various data types, including web log, video, picture, location, and so on. Third, it needs fast processing speed. We would quickly obtain high value from various types of data information. Fourth, as long as use data reasonably and focus on the correct and accurate analysis, we will obtain a high value in return.

H: I heard of big data, but always thought it was the amount of data is huge. Our company also did some projects related to huge data processing.

L: Just like he said. Big data is not just about its quantity. The more significant thing is its value hide behind the big data. And our target is using computer technology to do some data mining and find what is valuable.

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G: Our company has accumulated a lot of information, such as data about the commodities and the purchasing behavior. However, we merely made some statement analyses and sales ranking based on these data, which may ignore some other important details.

H：Listened to your discussion, I think the data that we own is not only in great numbers, but also has very big potential. Do you have any idea?

N：Do you know the story of beer and diaper

Through the statistics and analysis of the consumption data, some scholars found that the man who bought beer will buy diaper in general. Because American women who take care of baby at home, will ask their husband to buy diaper on the way home. Their husband may buy some bottles of beer when they buy diaper. As a result, some stores put beer and diaper in one place in order to get better sales.

H：Does this mean that we can find some commodity combinations from the data to recommend to the user, thus increasing their purchases.

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L: Actually, we can do more on these data. In the area of data mining, there is a special algorithm named collaborative filtering. It concerned about people’s action on buying, collecting and viewing on goods, which can find the similarity between different people. So we can recommend particular goods to particular person.

H：I think it's a brilliant idea.

G: That sounds really good. But I think it seems a little difficult to achieve this goal. We have tried to recommend the commodities which the customers may be interested in. Unfortunately, the amounts of economic investment didn’t obtain the corresponding results. So I wonder whether your lab has done any similar issues before?

N：We used to make e-commerce recommender system for some small and medium-sized enterprises. Their economic benefits are improved sharply.

H：But in your project, the amount of data is small, isn’t it?

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L: You are right. But in the developing of that recommend system, we also concerned about a lot of problem, and big data is under our consideration.

G: Do you mean that you have already implemented an excellent system that can meet our requirements perfectly?

N：Not like that, this recommendation systems used in different areas need to do some adjust.

L: What’s more, with the growing of our technology, we can promote such system in more ways. I believe that it can bring more benefit to your company.

H：Very well. Until now the topic we talk about is just for analyzing buying behaviors. Considering that in 2013, our company's active users had reached million. So we can really do something on this point. Now we just classify these users from the point of the purchase amount. We should consider this from more view points, what do you think

G: Nowadays, the foci of our customers are various. For instance, some pay attention to the quality of commodities. At the same time, others may

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keep a watchful eye on commodity price. However, we did not classify user's information from these aspects.

H: Well done. In this way, we can provide personalized marketing

strategies for the different categories of users, such as we can provide installment for higher credit users and more coupons for higher loyalty users. So we need to classify users in more dimensions.

G:Yeah, the more classifications there are, the better the results will be. I deem it’s advisable to regard this point as a breakthrough for further study.

N：What you just said can be realized by clustering and classification algorithm. We gather our users into some clusters. When we meet a new user, we just need to know which cluster he belongs to. And those two algorithms is the strengths of our laboratory. This is feasible.

G: As the technical consultant of our company, I have been trying to achieve a similar project but failed. I guess this is probably because the information we owned is so enormous that it can hardly use the traditional algorithms.

H：Whether your lab can make effective measures to solve this problem?

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L: Yes, of course. We have mentioned a feature about big data in the early time. One of its feature is velocity. Here, we have to put forward a new word named ‘Cloud Computing’. This is a new computing model which combined a lot of server into one cluster, which can promote the working speed very much.

N：This requires a lot of server clusters.

H：You don't need to worry about this point, if you have the confidence to achieve this goal, we try our best to meet your demand.

G:Do you have any other ideas or suggestions

N：We have discussed the user information data and shopping information data, what about store information data.

H：As you said, we really have a problem with this part. In order to help some shops which have very good development prospect but insufficient cash flow. We need provide different amount of loans for them.

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G: The problem is that how to determine the loan amounts of these shops. So it is necessary for us to predict their corresponding sales. What do you think about this?

L: What you said is another classical question, which is prediction problem. In the research of machine learning, there are a lot of models about prediction. If there are enough data, we can build a strict math model to make prediction. And it can guarantee a suitable accuracy.

G: As you mentioned above, this might be a math problem. But your major is computer science and technology, do you have confidence to finish the whole project?

N：Our laboratory happen to have student from department of mathematics. And computer science is a kind of mathematics in nature, we have the confidence to complete.

H：Listen to your introduction, we think your research project is great. It can really help our company. Next, we start to talk about specific cooperation issues.

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L: We will provide your company with support in algorithm, platform and developer and so on. We want your company providing us with support in data, fund and ground.

G: Okay, no problems. We will sort out the specific details of the contract. And I hope you can provide us with project description next time.

H:The meeting went off very well.

We honest expect with you establish good and long cooperation relation in the near future.

N：We will try our best to complete the project.

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