|
1.0 Introduction
Figure 1 Customer life-cycle economics in e-commerce (Reichheld and Schefter, 2000)图1电子商务中的客户生命周期经济学
Figure 1 Customer life-cycle economics in e-commerce (Reichheld and Schefter, 2000)
亚马逊现在已经成为提供世界上最多元化产品的在线零售商（Faulds、Mangold、Raju和Valsala，2018年）。亚马逊努力成为世界上最“以客户为中心”的公司，使其客户能够在公司网站上找到并发现他们想要的任何东西，并努力为客户提供最低价格的产品（Farah和Ramadan，2017年）。尽管亚马逊现在是世界上最大的在线零售商之一，但随着互联网2.0时代的到来以及阿里巴巴、eBay等众多强大竞争对手的出现，亚马逊在发展过程中面临着一些挑战（Kumar，2017年）。由于在线购物的客户转换成本很低，消费者往往不专注于在单个网站上购物，亚马逊如何保持客户忠诚度，让消费者在亚马逊花上很长时间是一个很大的挑战。如图1所示，随着客户对某些产品的忠诚度的提高，消费者也会增加在线购买产品的费用，这对电子商务业务的盈利能力非常重要。虽然亚马逊一直坚持以客户为中心的战略，但随着竞争的加剧，过去的战略还不足以应对挑战，但对于亚马逊来说，促进消费者在亚马逊的一次性消费并不重要，而是要保持消费者与亚马逊的长期关系，以满足消费者的需求。增强消费者忠诚度。基于此，亚马逊开始了营销策略的升级，基于关系营销的4R营销策略是其未来营销策略的选择之一。
Amazon has now become the online retailer that provides the most diversified products in the world (Faulds, Mangold, Raju and Valsala, 2018). Amazon strives to be the most "customer-focused" company in the world, enabling its customers to find and discover whatever they want on the company's website, and it works hard to provide customers with products with the lowest prices (Farah and Ramadan, 2017). Although Amazon is now one of the largest online retailers in the world, with the advent of the Internet 2.0 era and the emergence of many powerful competitors such as Alibaba, eBay, Amazon faces some challenges in its development (Kumar, 2017). Because there are low customer switching costs in online shopping, consumers tend not to concentrate on shopping at a single website, how Amazon maintains customer loyalty to allow consumers to spend long on Amazon is a big challenge. As it was shown in Figure 1 that with the improvement of customer loyalty towards certain products, consumers will also increase the amount of money spent on the products online, which is very important for the profitability of e-commerce business. Although Amazon has always insisted on customer-centric strategy, as the competition intensifies, the past strategy is not enough to meet the challenge, it is not important for Amazon to promote consumers’ a single time consumption in Amazon, but to maintain a long-term relationship between consumers and Amazon, so as to meet the needs of consumers to increase consumer loyalty. Based on this, Amazon starts a marketing strategy upgrade, 4R marketing strategy based on relationship marketing is one of the options of its future marketing strategy.
2.0 Main body主体
2.1 Strategy and information needs 战略和信息需求
Don E.Schuhz提出了基于4c营销理论的4R营销理论（Schultz，2001）。4R营销理论以关系营销为核心，着眼于企业与客户之间的长期关系，建立客户忠诚度，主要包括相关性、反应性、关系性和回报性四大策略（Willems等，2017）。
相关性意味着企业和客户是命运共同体（Kim等人，2017年）。建立和发展与客户的长期关系是企业管理的核心理念和最重要的内容。对于亚马逊来说，要实施相关策略，就需要考虑建立一站式的购物网站，这样消费者就可以通过亚马逊满足他们的所有需求，而不必寻找其他渠道来满足他们的购物需求，这样可以更好地提高消费者的忠诚度。亚马逊在实施相关策略时需要什么信息，就是了解消费者在购物前、购物后需要什么产品和服务，从而采取有针对性的措施。
Don E. Schuhz proposed 4R marketing theory based on 4C marketing theory (Schultz, 2001). 4R marketing theory takes relationship marketing as the core, focusing on long-term relationships between enterprises and customers to build customer loyalty, it mainly includes the four strategies of relevance, reaction, relationship and reward (Willems et al., 2017).
Relevance means that enterprises and customers are a community of fate (Kim et al., 2017). To establish and develop long-term relationship with customers is the core concept and the most important content of business management. For Amazon, to implement relevance strategy, it needs to consider establishment of a one-stop shopping website, so that consumers can meet all their needs through Amazon, and they do not have to find other channels to meet their needs in shopping, which can better improve consumer loyalty. What information that Amazon needs for implementing relevance strategy is to understand what products and services that consumers need before, after their shopping, so as to take targeted measures.
Reaction refers to how to think from a customer's point of view to find extrinsic or potential consumer demands promptly to take prompt measures to satisfy consumers (Schultz and Block, 2015). Therefore, to implement reaction strategy requires Amazon to take active communication with consumers by providing timely and professional answers to consumer inquiries. The information that Amazon needs for implementation of reaction strategy is to understand what consumers want to know, what kind of products and services that consumers need, what consumers are not satisfied with.
Relationship strategy means that the key for an enterprise to seize a market has been changed into establishing a long-term and stable relationship with consumers, the relationship has been changed from mutual interest conflicts into the common development of harmony (Schultz, 2001). For Amazon, the most important part of maintaining a good relationship with customers is to help customers to make the best purchasing decisions, so that consumers can buy their favourite products without regret. The information that Amazon needs for implementation of the relationship strategy is to master information of each product and comparative advantages of each product to provide consumers with the information in time to make optimal decisions.
Reward strategy refers to that enterprises should not only meet customer needs, but also bring economic benefits to themselves (Schultz, 2001). For Amazon, the implementation of the reward strategy requires that the services and products it provides should truly satisfy consumers to stimulate consumers to make purchase decisions, so as to satisfy their needs and improve their satisfaction while enhancing Amazon’s sales to realize a win-win situation. Amazon's implementation of its reward strategy needs to understand consumers’ extrinsic and potential demands to recommend products that consumers really need, thereby increasing sales revenue.
2.2 Business intelligence needs of Amazon
Amazon’s business intelligence needs are mainly reflected in the following areas.
First, Amazon’s relevance strategy requires the establishment of a community of customers and enterprises to effectively improve customer loyalty, so that customers can continue to shop on the website. The technical demand for this is to establish a perfect customer relationship management system. First of all, the management system should be able to collect, store and count all the information about consumer shopping to help customers to understand their own consumption, and it also helps Amazon to understand customer behaviour and preferences, so as to provide customers with targeted products and services.
In addition, Amazon also needs to establish a consumer behaviour assessment system to record consumer behaviour of shopping on the website and give appropriate incentives to allow consumers to form the habit of spending on the website to enhance their loyalty.
Second, according to Amazon's 4R strategy, it needs to maintain good communication with customers, so as to meet consumer advice and understand what consumers need promptly. From this perspective, its intelligence needs are reflected in the following four aspects: the technology used can receive consumer information promptly to provide timely feedback; it should ensure that consumers can easily access online communication services in any medium; it should ensure the stability of communication to avoid communication interruption; it should ensure the safety of information communication; the information and data in communication can be easily shared and stored in the company for statistical analysis.
Third, one of the most important things that Amazon needs to do to maintain good relationship with customers is helping customers to make the best buying decisions. From this perspective, its intelligence needs to reflect the perfect product contrast functions, so that consumers can understand what kind of products they need most. When customers choose a few key words, the system will give consumers several similar products to choose from, while the various details and information of each product will be displayed in the form of a list with data and pictures. In most cases, consumers do not understand the significance of these data well, even they know the data, they still fail to make the best purchase decision, therefore, the information system is required to provide consumers with some explanation for the meaning of the data for consumers’ product comparison, and a brief introduction of the characteristics of products as well as the scope and population that the products aim at will also be provided.#p#分页标题#e#
Finally, the information needs of Amazon's implementation of the reward strategy are to understand consumers’ extrinsic and potential demand, so as to recommend what consumers really need, thereby increasing Amazon's sales revenue. Thus, for Amazon, to establish a good e-commerce referral system is very important. On the one hand, a perfect referral system can be based on the characteristics of consumer behaviour in the platform to analyze consumer preferences to recommend targeted products, on the other hand, it is able to based on relevant big data mining and analysis of consumers’ potential demand for accurate forecasts to recommend more targeted products to consumers.
2.3 The existing information systems str
ategy 
2.3.1 Customer relationship management
After customers’ registration, Amazon's customer management system will record and provide customers with accounts. The main account information includes order management, payment settings, settings, personal settings, history browsing history, wish list and so on. Amazon's account information management system is detailed with personalized settings. For customers, Amazon’s customer relationship management information is detailed, the service is quick and easy to a large extent to meet the needs of consumers. For Amazon, its customer relationship management system records the information to provide strong information support for the enterprise’s inventory management, product updates, grasping the needs of consumers.
Overall, Amazon's current customer relationship system is already quite good, but it still needs to be further improved, for example: it should further increase customer switching costs. Customer switching costs refer to the barriers faced or costs increased when a customer switches from an enterprise to another, which is the sum of the costs that customers have to pay for switching, customer switching costs can include, time costs, effort costs, money costs and so on (Miyatake et al., 2016). So if consumers spend more money and more time on Amazon, then they get more valuable additional services from Amazon; if they leave Amazon, these extra valuable services will not be provided any more, this approach is likely to effectively increase customer loyalty.
2.3.2 Customer service system
The main way that Amazon uses to communicate with customers currently includes online communication, e-mail communication and telephone communication. As a whole, consumers can use their computers, cell phones, notebook PC and other media to communicate smoothly with Amazon's contact staff through these means of communication. The stability of communication and the security of information are well guaranteed. Consumers as a whole are satisfied with Amazon's customer service level, but if Amazon wants to implement 4R strategy, keep abreast of consumer needs and maintain better communication with consumers, further improvements are needed in their communication efficiency.
According to Social Presence Theory, it is considered that in order to optimize the communication effect, appropriate communication method should be selected according to the task type and the communication scenario (Li, Lin and Ho, 2017; Gupta, Bhatnagar and Jalal, 2018). When many consumers carry out real-time online communication with Amazon’s contact staff, they need to type a lot, this communication is less efficient, so in many cases, text communication is not the best way to communicate, the use of online voice communication way can greatly improve the efficiency of communication. Sometimes customers ask more complicated questions, the staff can not answer in a timely manner, so they often ask consumers to wait a bit, then the staff will go to consult other more experienced staff, thus increasing the consumers’ waiting time. Thus this one-on-one communication is often not the best way in many cases. Quickly and conveniently implementing many-to-one communication technologies can help Amazon to communicate with consumers better.
2.3.3 Product information comparison
The theory of complex purchase behavior points out that consumers have to go through various stages of information collection, comprehensive product evaluation and comparison, discreet prudent purchase decision and serious post-purchase evaluation in the process of purchasing (Grob, 2016; Zhou et al., 2017). Considering complex buying behavior, marketers should develop strategies to help buyers to quickly grasp product information, by understanding product features and comparative advantages to simplify consumers’ purchasing decision-making process, so as to help consumers to make the best purchasing decisions (Gupta and Arora, 2017).
Amazon's current introduction of product information is limited to a single product information included in a form, consumers can really use this form to clearly understand product information and data about brand, material, size, etc. However, when consumers want to compare information of different products, they need to open multiple web pages, which is not very convenient for comparing product information. In addition, at present, Amazon’s product information is more descriptive, which can not fully meet the needs of consumers. If consumers can know the information about a product in terms of price, material and other aspects of comparing with other similar products, it can help them to make buying decisions faster and easier.
2.3.4 Product referral system
The information needs of Amazon's implementation of the reward strategy are to understand consumers’ extrinsic and potential demands to recommend products that consumers really need, thereby increasing Amazon's sales revenue. At present, Amazon's referral system includes the following four aspects. The user behaviour record module is responsible for collecting behaviours that can reflect a user's preferences, such as browsing, purchasing, commenting, etc. The user behaviour analysis module establishes user preference model by analyzing a user's potential preferences and the levels of preferring for products according to the user's behaviour records. The product analysis module mainly analyzes product characteristics and the matching degree between products and a customer's needs, According to certain rules, it recommends products from the candidate set of products that target users are most likely to be interested in by using recommended algorithm.
Amazon's current product referral system is mainly based on consumer behaviour to analyze consumer preferences, so as to be targeted to recommend products that consumers may be interested in. It can be said that at present Amazon's product recommendation system can better find what a customer needs to provide targeted recommendations. However, if consumers want to buy something that they did not buy in the past, Amazon's product recommendations are less targeted because the consumers do not have a track record of buying those products and the referral system does not adequately analyze the consumer preferences.
Consumer group theory holds that all consumers who share the same characteristics will show the same or similar consumer psychology behaviour, because members of a same group generally have more frequent contact and interaction with each other, making them influence each other (Shi and Liao, 2017). So for Amazon, its product referral system can make some optimizations by analyzing the big data of the consumer behaviour of a same consumer group to which the consumers belong and then recommending products, so that even the consumers did not purchase certain products before, it can still provide targeted recommendations.
3.0 Information system strategy
3.1 Customer relationship management
To increase consumer loyalty and customer switching costs, Amazon can establish consumer behaviour assessment system to record and stimulate consumers’ shopping on the website, resulting in habitual consumer behaviour to increase consumer loyalty. Consumer behaviour assessment system refers to the implementation of quantitative evaluation based on consumer behaviour of shopping through the website, each time of consumers’ login, shopping, giving evaluation, sharing, recharging will be recorded and awarded with corresponding scores. If there is behaviour of return of goods, there will be no score for the consumer for the shopping this time. Such scores can be redeemed for a certain amount of money, or discount coupons or other attractive free services offered by Amazon, and more scores consumers acquire mean more attractive services and money can be converted to stimulate consumers to spend more to accumulate scores, and they can not earn scores if they do not shop on Amazon’s website and they can not accumulate scores, which undoubtedly increases customer switching costs and helps to increase consumer loyalty.
3.2 Communication
In order to better communicate with customers and provide better services, Amazon should introduce group chat technology to its online communication service. When a customer's problem can not be answered by a customer service staff and it needs the help of other colleagues such as product manager and logistics manager, the customer service staff can use the online invitation way to invite colleagues to join the discussion, so that the colleagues and the customers can communicate directly, until the customer is satisfied with the answer, the colleagues can withdraw from the chat group, so the advantage is that customers can first acquire professional answers, without having to wait for customer service staff to spend more time for asking colleagues to get the answers.#p#分页标题#e#
To improve communication efficiency, Amazon’s online customer service staff can use voice chat system, because efficiency of voice chat communication is much higher than efficiency of purely text communication. OMCS network voice video frame can be used in the voice system. This framework is currently one of the most commonly used web voice chat frameworks. The strengths include low latency, low background noise, smooth sound, no pause, no echo, etc. With this set of voice communication system, consumers can easily convey a complicated problem to customer service staff, which is of great practical value.
3.3 Product comparison
In order to better help consumers to make purchasing decisions, Amazon can integrate ShopEx or ECShop online store system into their own website, these two systems allow consumers to easily use commodity comparison function. Consumers can find the same type of products they need, there is a contrast function key set up for each product, as long as consumer clicks the contrast function key, the product will be included in the comparison range, while there will be 4-6 similar products for contrast. Product price, product material, origin and other information will be displayed in the same form, consumers can clearly see, most importantly, the bottom line of each comparison form will be a simple comparison report provided by Amazon, which introduces the characteristics of each product, what are the comparative advantages, the scope of application and so on. The contents of this report can be preset in the back-end administration of ShopEx or ECShop online store system.
3.4 Referral
Algorithms of Amazon's current product referral system are based on consumers’ purchase history and visit records, taking it as the main recommendation basis, while characteristics of consumer behaviour of consumer group are less considered. Therefore, Algorithms of Amazon's current product referral system can be further improved, characteristics of consumer groups can be taken as one of the basis for recommending products. For example, it can be seen from the figure above that, for 40-50 male consumers, 65% of consumers are willing to spend more money to buy technology products, spend less money to buy clothing. As a result, Amazon's system can use up-selling when it recommends products to 40-50 male consumers, recommending more advanced but more expensive technology products to them, and in addition, cross-selling strategies can be used to recommend middle and low-end men's clothing to men when they opt for technology products. For 30-40 years old female consumers, they are willing to spend more money on the purchase of clothing, at the same time, they will buy children's clothing, therefore, in recommendation for this part of consumers, Amazon can consider recommending children's wear.
4.0 Conclusion
Based on 4R theory, this essay puts forward some suggestions on how Amazon further improves its information system. The suggestions include four aspects, first of all, Amazon needs to establish a consumer behaviour assessment system, by recording consumers’ buying behaviour on the website and giving appropriate incentives to consumers to develop their habit of spending on Amazon’s website to enhance their loyalty. Followed by the application of group chat and voice technology to customer service, it will help Amazon to provide timely response to consumer consultation and have understanding of consumer needs. Third, to improve product comparison system can help customers to make the best purchase decision. Finally, based on characteristics of consumer groups to analyze consumer preferences, it will help Amazon to recommend targeted products to consumers to make recommendations more targeted.
Reference
Farah, M. F. and Ramadan, Z. B. (2017). Disruptions versus more disruptions: How the Amazon dash button is altering consumer buying patterns. Journal of Retailing and Consumer Services, 39(11), 54-61.
Faulds, D. J. et al. (2018). The mobile shopping revolution: Redefining the consumer decision process. Business Horizons, 1(5), 105-118.
Grob, M. (2016). Impediments to mobile shopping continued usage intention: a trust-risk-relationship. Journal of Retailing and Consumer Services, 33(11), 109-119.
Gupta, A. and Arora, N. (2017). Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. Journal of Retailing and Consumer Services, 36(5), 1-7.
Gupta, M., Bhatnagar, C. and Jalal, A.S. (2018). Clothing image retrieval based on multiple features for smarter shopping. Procedia Computer Science, 125, 143-148.
Kim, M. et al. (2017). Mobile shopping through applications: understanding application possession and mobile purchase. Journal of Interactive Marketing, 39(8), 55-68.
Kumar, R. (2017). 14: Wealth creation by Amazon. Strategic Financial Management Casebook, 413-440.
Li, Y. M., Lin, L. F. and C Ho, C. C. (2017). A social route recommender mechanism for store shopping support. Decision Support Systems, 94(2), 97-108.
Miyatake, K. et al. (2016). Reduction in consumers’ purchasing cost by online shopping. Transportation Research Procedia, 12, 656-666.
Reichheld, F. F. and Schefter, P. (2000). E-loyalty: your secret weapon on the web. Harvard Business School Press Boston.
Schultz, D. E. and Block, M. P. (2015). U.S. online shopping: facts, fiction, hopes and dreams. Journal of Retailing and Consumer Services, 23(3), 99-106.
Schultz, D. E. (2001). Marketers: bid farewell to strategy based on old 4Ps. Marketing News, 35(2), 7.
Shi, X. and Liao, Z. (2017). Online consumer review and group-buying participation: The mediating effects of consumer beliefs. Telemetric and Informatics, 34(5), 605-617.
Willems, K. et al. (2017). The path-to-purchase is paved with digital opportunities: an inventory of shopper-oriented retail technologies. Technological Forecasting and Social Change, 124(11), 228-242.
Zhou, Y. W. et al. (2017). Pricing and rebate strategies for an e-shop with a cashback website. European Journal of Operational Research, 262(1), 108-122.
|
 |
|||
| 网站地图 |
