本文是工商管理专业的Essay范例,题目是“Predective Analytics in E-commerce Application(电子商务应用中的前瞻性分析)”,摘要:本文概述了机器学习和预测分析的原理。它解释了大数据的基本原理和不断发展的电子商务技术。近年来,网络上的业务增长迅猛。
世界各地的组织都意识到,电子商务不再只是通过互联网进行购买或销售,而更多的是关于用户体验的动态性和互动性。分析顾客行为,组织将能够提高销售和留住顾客。在电子商务组织中,有几种客户行为可以通过预测分析来预测。从你想要预测的结果开始是很重要的。本文的重点是跟踪客户的购买行为,通过在电子商务平台上向客户推荐商品来提高销售。通过一个案例研究,我们将了解如何使用python构建推荐系统。在本研究中,我们提出了一个智能的推荐系统,向顾客推荐商品。
Predective Analytics in E-commerce Application电子商务应用中的前瞻性分析
Abstract—This paper outlines the principal of machine learning and predictive analytics. It explains the fundamentals of big data and evolving E-commerce technology. Business over web has observed tremendous growth during recent times. Organizations around the globe are realizing that E-commerce is just no more about buying or selling over the internet, rather it’s more about how dynamic and interactive a user experience can get. Analyzing customers behavior, the organization will be able to improve sales and retain customers. There are several customer behaviours which can be predicted using predictive analytics in an E-commerce organisation. It is important to start with the outcomes that you would like to predict. This paper focuses on tracking customers purchasing behaviour to improve sales by recommending items to customers on an E-commerce platform. A case study has been presented in which we are going to see how to build recommender system using python. In this study we present an intelligent recommendation system to suggest items to customer.
I.Introduction介绍
In today’s generation of online commerce, predictive analytics technology plays very crucial role. There are several ways with which predictive analytics can help an organization to grow, it is important to categorize which use is relevant to your business and pick the area that will create the maximum opportunity by analyzing the desired targets. You may consider increasing the company revenue, detection of fraud, optimizing customer service, cost effective techniques, customer behavior insights. Once the appropriate target is selected predictive analytics can generate huge competitive advantage for an online retailer. Though there are few limitations, for instance models need to undergo quality check before implementation and further human intervention is necessary to maintain and run the model, however advantages outweigh the drawbacks.
There are numerous advantages for using predictive analytics in E-commerce and once deployed, benefits are observed instantly. Here are some leading trends that are making their ways to the forefront of the business today. Recommendation engines similar to those used in Netflix and Amazon uses past purchases and buying behavior to recommend new purchases to consumers. Risk engines to forecast market strategy, innovation engines for new product innovation, customer insight engines and optimization engines for complex operation and decision making. Today we are at the tip of iceberg in terms of applying predictive analytics to solve real world problems. Predictive analytics approach unleashed the might of the data. In short, this approach allows us to predict the future. Data science algorithms can effortlessly predict who will buy, cheat, lie, or die in the near future.
在当今的电子商务时代,预测分析技术扮演着非常重要的角色。预测分析有几种方法可以帮助组织成长,重要的是要分类哪些使用与您的业务相关,并选择通过分析预期目标可以创造最大机会的领域。你可以考虑增加公司收入,检测欺诈行为,优化客户服务,成本效益技术,洞察客户行为。一旦选择了合适的目标,预测分析可以为在线零售商产生巨大的竞争优势。虽然有一些限制,例如模型需要在实现之前进行质量检查,并且需要进一步的人工干预来维护和运行模型,但是优点大于缺点。在电子商务中使用预测分析有许多优点,一旦部署,就可以立即观察到好处。以下是一些领先的趋势,它们正在走向当今商业的前沿。推荐引擎与Netflix和亚马逊使用的推荐引擎类似,它利用过去的购买行为向消费者推荐新的购买行为。预测市场战略的风险引擎,新产品创新的创新引擎,客户洞察引擎和复杂操作和决策的优化引擎。今天,我们还处在将预测分析应用于解决现实问题的冰山一角。预测分析方法释放了数据的力量。简而言之,这种方法允许我们预测未来。数据科学算法可以毫不费力地预测谁会在不久的将来购买、欺骗、撒谎或死亡。
II. Introduction to Predictive modelling
Predictive modelling is an ensemble of statistical algorithms coded in a statistical tool, which when applied on historical data, outputs a mathematical function or equation. It can in turn be used to predict outcomes based on some inputs (on which the model operates) from the future to drive a business context or enable better decision making in general. Predictive modelling continues to generate great deal of interest in recent generation. (Konnie L. Wescott, R. Joe Brandon, 1999, 6). To understand what predictive modelling is, let us focus on terms highlighted previously.
A.Ensemble of statistical algorithms统计算法集成
Statistics are important to understand data. It tells volumes about data. How is the data distributed? Is it centered with little variance or it varies widely? Statistics helps us answer these questions. Algorithms, on the other hand are the blueprints of a model. They are responsible for creating, mathematical equations from historical data. They analyze the data, quantify the relations between the variables and convert it in to a mathematical equation. There are variety of them: Linear regression, logistics regression, clustering, decision trees, natural language processing and so on. These models can be classified under two classes: Supervised algorithms and unsupervised algorithms.
统计学对于理解数据很重要。它告诉我们大量的数据。数据是如何分布的?中心的变化是小的还是大的?统计数据帮助我们回答这些问题。另一方面,算法是模型的蓝图。他们负责从历史数据中创建数学方程。他们分析数据,量化变量之间的关系,并将其转化为数学方程。它们有很多种:线性回归、逻辑回归、聚类、决策树、自然语言处理等。这些模型可以分为两类:有监督算法和无监督算法。
Supervised algorithms: These are the algorithms wherein the historical data, an output variable in additional to the input variables. The model makes use of the output from historical data, apart from the input variables. The example of such algorithms includes Linear regression, Logistic Regression Decision Trees and so on.
Unsupervised algorithm: These algorithm work without an output variable in the historical data. The examples of such algorithm include clustering.
B.Historical dataHistorical数据
In general, model is built on historical data and works on the future data, Additionally, a predictive model can be used to fill the missing values in historical data by interpolating the model over sparse historical data. During modelling future data is unavailable hence historical data is used in sampling to act as future data.
通常,模型建立在历史数据的基础上,对未来数据进行处理。此外,预测模型可以通过对稀疏的历史数据进行插值来填补历史数据中的缺失值。在建模过程中,未来数据是不可用的,因此在采样中使用历史数据作为未来数据。
C.Mathematical function
Most of the data science algorithms have underlying mathematics behind them. In many of the algorithms, such as regression, equation is assumed and parameters are derived by fitting the data to the equation.
D. Business context
All the effort that goes into predictive analytics and all the worth, which accrues to data, is because it solves a business problem. Business problems can be anything and varies widely.
As discussed earlier, predictive modelling is and interdisciplinary field sitting at the interface and requiring knowledge of four disciplines such as statistics, algorithms, tools, techniques and business sense.
III.Recommender System
Recommender systems are widely used in the e-commerce market for personalized and unique recommendations of other products for each customer.” In a world where a site’s competitors are only a click or two away, gaining customer loyalty is an essential business strategy” (Reichheld and Sesser, 1990) (Reichheld, 1993) The recommended products can be anything for example physical goods, films, music, articles, social tags and services. The system enriches the online experience, increases the conversion rate and affects the revenues positively (Schafer, Konstan and Riedl, 1999). Theoretically, recommender systems are a “spectrum of systems describing any system that provides individualization of the recommendation results and leads to a procedure that helps users in a personalized way to interesting or useful objects in a large space of possible options”(Lampropoulus and Tsihrintzis 2015, p.1). A recommender system helps its user by filtering an overload of information by providing the most appropriate and valuable information for the specific user. To make recommendations, personal information about the user preference is required in order to predict the user’s rating for other items than they have been in touch before. There are three different methods of collecting knowledge about user preferences: implicit, explicit and mixing approach. The implicit approach does not require any active involvement from the user and is based on recording the user behavior. A typical example of implicit rating is a historic purchase data. The explicit approach is based on user interrogation by requiring the user to specify their preference for any particular item. Lastly, the mixing approach is a combination of the previous two. There are two main approaches of designing a recommender system: content-based methods and collaborative methods. By assuming that a user’s preferences remain unchanged through time, one can predict their future actions based on past user behaviors. In other words, all the information stored about the user will be used to customize the services offered. While, the main assumption for collaborative filtering is that similar users prefer similar items. This method relies entirely on interest ratings from the users and can be categorized into two different branches: model-based and memory-based. The model-based algorithms use statistical and machine-learning techniques to make predictions based on the underlying data. The memory-based methods can be further divided into two classes: user-based and item-based. User-based collaborative systems make user-user similarity calculations by matching the user against a database of other users who have similar interests. Items that the other users have bought but unknown to the specific user are offered as a recommendation for the specific user. The item-based collaborative system is, on other hand, based on matching a specific item to a database of other items. Thus, this approach is based on item relations rather than user relations and makes the final prediction based on similarities between items which have been rated by a common user.
In order to build a recommender system to recommend products to the customer we will be using collaborative filtering. Collaborative filtering works on just three pieces of data. A user or a customer, an item, and an affinity score between the user and the item.
IV.Examples of recommender system
In this section we will see few of the reputed E-commerce companies that utilize one or more variations of recommender system technology in their web sites.
A.Amazon.com
Amazon uses the recommender system in many aspects, Amazon videos, Amazon Appstore, Amazon logistics, web page recommendations, customer and seller services. Let’s see how Amazon uses each aspect in detail.
In books, Amazon used customer who brought feature. This feature is found on the information page for each book in the catalog. The first recommends books frequently purchased by customers who purchased the selected book. The second recommends authors whose books are frequently purchased by customers who purchased works by the author of the selected book.
B.Netflix
More than 80 percent of the TV show people watch on Netflix are discovered through the platform’s recommendation system. That means the majority of what you decide to watch on Netflix is the result of decision made by machine learning and algorithm. Netflix uses machine learning and algorithms to help break viewers preconceived notion and find shows that they might not have initially chosen.
C.eBay
The Feedback Profile feature at eBay.comallows both buyers and sellers to contribute to feedback profiles of other customers with whom they have done business. The feedback consists of a satisfaction rating (satisfied/neutral/dissatisfied) as well as a specific comment about the other customer. Feedback is used to provide a recommender system for purchasers, who are able to view the profile of sellers. The seller profile consists of historical rating from the sales made in past years and all the seller feedback and reviews are available for the customer.
eBay.com的反馈档案功能允许买家和卖家贡献反馈档案的其他客户,他们已经做了业务。反馈包括满意度评分(满意/中性/不满意)以及对其他客户的具体评论。反馈是用来为买家提供一个推荐系统,买家可以查看卖家的资料。卖家简介包括过去几年销售的历史评级,所有卖家的反馈和评论都可以提供给客户。
V. Case study
Let’s take an example of person purchasing a laptop from a E-commerce website. Addition to laptop one might need charging pads, mouse and additional warranties for damage. Knowledge of the customer’s purchasing desires and situations will create upsell and cross sell opportunities for the companies to sell the product and make some quick profits from the data available.
Up-sell means selling additional items in the same category along with the main motivational purchase. Cross-sell relates to selling addition items in different categories that the customer might desire.
If a person purchases a high end laptop, the person might be further interested in purchasing a high end game, gaming accessories, hard disk, router, antivirus software or Microsoft office suit. There are a few factors we might want to consider to determine the cross and upsell opportunities related to particular customer.
If we can predict such events, related or desired products can be recommended to customer.
In this case study we are going to see how to implement recommended items in python. In order to recommend the product to customer which similar people brought. In this case we will use data about which customer brought which products and based on that build an item to item affinity score and then use it to recommend items to customer. Here is a data file which includes the UserId and ItemId
在这个案例研究中,我们将看到如何在python中实现推荐的项目。为了向顾客推荐同类人带来的产品。在本例中,我们将使用有关哪些客户带来了哪些产品的数据,并基于这些数据构建一个项目对项目的亲和度得分,然后使用它向客户推荐产品。下面是一个包含UserId和ItemId的数据文件
The data file meant for representation consists of user ID and item ID. From the data we can see the use 1001 has purchased items 5001, 5002 and 5005
To extract information, we will load the file on jupyter notebook and build an affinity score between items based on users who purchased them.
We are going to find affinity of every item to other item and the way I’m going to do it is by finding out how many customers have bought both these products. The higher the customers who has brought the items, the higher is going to be the affinity score.
Once the affinity scores between each item have been printed. We see here Item 1 to 2 has a high affinity score of .4, whereas 5,001 to 5,003, there is no affinity at all.
In this list of affinity score, in order to recommend items to customer, we are going to go back to this table, go to all the records that are item one in the first column, and get the list of all the items two and their scores. And we can do that in descending order. And those items that you see here is what I want to recommend. Let’s further see how we can use the affinity scores to know which products can be recommended to customer 50001.
VI.Results结果
In the following case study, we were able to construct a simple recommender system based on customers purchasing behavior. We have taken in to consideration the item and user data to find the affinity score so that products can be recommended to customers. So for 5001, we see that 5002 and 5005 has a score of .4, of 5004 has .2, and 5003 has zero. We can further classify a threshold limit above which we will recommend items. For example, we are going to only recommend those items whose score is above a .25, then we would recommend the products 5,002 and 5,005 to the customer. Ryan Aminollahi
在接下来的案例研究中,我们可以基于客户的购买行为构建一个简单的推荐系统。我们考虑了item和用户数据,找到了亲和性得分,以便向客户推荐产品。对于5001,我们看到5002和5005的分数是。4,5004是。2,5003是0。我们可以进一步对阈值进行分类,超过这个阈值,我们将推荐一些项目。例如,我们将只推荐分数在。25以上的产品,然后我们将向客户推荐5002和5005产品。瑞安Aminollahi
VII. Conclusion and future of recommendation system
Current state of technology
The industry is trying to integrate various recommender system which works on Point of interest or meta data or group recommendations. Every system is built according to the requirements of the organization. Sofiya Mujawar, former Data Scientist at Big Data Solutions
In my opinion the recommender systems can be applied to ever more broader aspects which includes daily life issue. Recommender systems can be applied to solve daily life issue and recommend curse of the day, which includes day to day activity and food habits. Which provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Web services in particular suffer from producing recommendations of millions of items to millions of users. The time and computational power can even limit the performance of the best hybrid systems. For larger dataset, we can work on scalability problems of recommendation systems.
在我看来,推荐系统可以应用到更广泛的方面,包括日常生活问题。推荐系统可以解决日常生活问题,推荐日常活动和饮食习惯。它提供功能来跟踪营养消费,并说服用户以积极的方式改变他们的饮食行为。Web服务尤其容易受到向数百万用户推荐数百万项内容的影响。时间和计算能力甚至会限制最佳混合系统的性能。对于较大的数据集,我们可以研究推荐系统的可扩展性问题。
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