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Guided Selling
Guided selling is a process that helps potential buyers of products or services to choose the product best fulfilling their needs and hopefully guides the buyer to buy. It also helps vendors of products (e.g. brands, retailer) to actively guide their customers to a buying decision and thus increases their conversion rate. Guided selling simplifies and automates the maintenance and deployment of all knowledge that is required to analyze customer needs, define the solution, and generate a proposal to fulfill those needs. A functional definition of the solution is provided to the customer, complete with commercial aspects of the proposal, such as prices, margins, texts, illustrations, and lay-outs. In addition, the technical specification of the solution (such as bills of materials and routings) is generated for manufacturing and distribution. Process and Practice Guided selling is put in practice with an information system that supports the central management and maintenance of kno ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
E-commerce
E-commerce (electronic commerce) is the activity of electronically buying or selling of products on online services or over the Internet. E-commerce draws on technologies such as mobile commerce, electronic funds transfer, supply chain management, Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and automated data collection systems. E-commerce is in turn driven by the technological advances of the semiconductor industry, and is the largest sector of the electronics industry. Defining e-commerce The term was coined and first employed by Dr. Robert Jacobson, Principal Consultant to the California State Assembly's Utilities & Commerce Committee, in the title and text of California's Electronic Commerce Act, carried by the late Committee Chairwoman Gwen Moore (D-L.A.) and enacted in 1984. E-commerce typically uses the web for at least a part of a transaction's life cycle although it may also use other techno ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Coronavirus Disease 2019
Coronavirus disease 2019 (COVID-19) is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The symptoms of COVID‑19 are variable but often include fever, cough, headache, fatigue, breathing difficulties, loss of smell, and loss of taste. Symptoms may begin one to fourteen days after exposure to the virus. At least a third of people who are infected do not develop noticeable symptoms. Of those who develop symptoms noticeable enough to be classified as patients, most (81%) develop mild to moderate symptoms (up to mild pneumonia), while 14% develop severe symptoms (dyspnea, hypoxia, or more than 50% lung involvement on imaging), and 5% develop critical symptoms (respiratory failure, shock, or multiorgan dysfunction). Older people are at a higher risk of developing seve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Intelligence
Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs. The ''Oxford English Dictionary'' of Oxford University Press defines artificial intelligence as: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri and Alexa), self-driving cars (e.g., Tesla), automated decision-making and competing at the highest level in strategic game systems (such as chess and Go). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recommender System
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders.Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh ZadeWTF:The who-to-follow system at Twitter Proceedings of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Collaborative Filtering
Collaborative filtering (CF) is a technique used by recommender systems.Francesco Ricci and Lior Rokach and Bracha ShapiraIntroduction to Recommender Systems Handbook Recommender Systems Handbook, Springer, 2011, pp. 1-35 Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person ''A'' has the same opinion as a person ''B'' on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person. For example, a collaborative filtering recommendation system for preferences in television programming could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Product Finder
Product finders are information systems that help consumers to identify products within a large palette of similar alternative products. Product finders differ in complexity, the more complex among them being a special case of decision support systems. Conventional decision support systems, however, aim at specialized user groups, e.g. marketing managers, whereas product finders focus on consumers. Area of application Usually, product finders are part of an e-shop or an online presentation of a product-line. Being part of an e-shop, a product finder ideally leads to an online buy, while conventional distribution channels are involved in product finders that are part of an online presentation (e.g. shops, order by phone). Product finders are best suited for product groups whose individual products are comparable by specific criteria. This is true, in most cases, with technical products such as notebooks: their features (e.g. clock rate, size of harddisk, price, screen size) may ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Customer Engagement
Customer engagement is an interaction between an external consumer/customer (either B2C or B2B) and an organization (company or brand) through various online or offline channels. According to Hollebeek, Srivastava and Chen's (2019, p. 166) S-D logic-Definition of customer engagement is "a customer’s motivationally driven, volitional investment of operant resources (including cognitive, emotional, behavioral, and social knowledge and skills), and operand resources (e.g., equipment) into brand interactions," which applies to online and offline engagement. Online customer engagement is qualitatively different from offline engagement as the nature of the customer's interactions with a brand, company and other customers differ on the internet. Discussion forums or blogs, for example, are spaces where people can communicate and socialise in ways that cannot be replicated by any offline interactive medium. Online customer engagement is a social phenomenon that became mainstream with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Preference Elicitation
Preference elicitation refers to the problem of developing a decision support system capable of generating recommendations to a user, thus assisting in decision making. It is important for such a system to model user's preferences accurately, find hidden preferences and avoid redundancy. This problem is sometimes studied as a computational learning theory problem. Another approach for formulating this problem is a partially observable Markov decision process. The formulation of this problem is also dependent upon the context of the area in which it is studied. Overview With the explosion of on-line information new opportunities for finding and using electronic data have been generated, these changes have also brought the task of eliciting useful information to the forefront. Researchers as well as major online catalog companies have come up with algorithms and prototypes of systems that can aid a user to be able to navigate through a complex and huge information space using some inf ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recommendation System
A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders.Pankaj Gupta, Ashish Goel, Jimmy Lin, Aneesh Sharma, Dong Wang, and Reza Bosagh ZadeWTF:The who-to-follow system at Twitter Proceedings of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Similarity Search
Similarity search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects. Nearest neighbor search and range queries are important subclasses of similarity search, and a number of solutions exist. Research in similarity search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, due to a manifestation of the so-called curse of dimensionality, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
The Long Tail
In statistics and business, a long tail of some distributions of numbers is the portion of the distribution having many occurrences far from the "head" or central part of the distribution. The distribution could involve popularities, random numbers of occurrences of events with various probabilities, etc. The term is often used loosely, with no definition or an arbitrary definition, but precise definitions are possible. In statistics, the term ''long-tailed distribution'' has a narrow technical meaning, and is a subtype of heavy-tailed distribution. Intuitively, a distribution is (right) long-tailed if, for any fixed amount, when a quantity exceeds a high level, it almost certainly exceeds it by at least that amount: large quantities are probably even larger. Note that there is no sense of ''the'' "long tail" of a distribution, but only the ''property'' of a distribution being long-tailed. In business, the term ''long tail'' is applied to rank-size distributions or rank-fre ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Online Lead Generation
In marketing, lead generation () is the initiation of consumer interest or enquiry into products or services of a business. A lead is the contact information and, in some cases, demographic information of a customer who is interested in a specific product or service. Leads may come from various sources or activities, for example, digitally via the Internet, through personal referrals, through telephone calls either by the company or telemarketers, through advertisements, and events. * In 2014, a study found that direct traffic, search engines, and web referrals were the three most popular online channels for lead generation, accounting for 93% of leads. * In 2018, Chief Marketer found that B2B marketers favored email, live events, and content marketing as their top three. * After the COVID-19 pandemic in 2020, Gartner identified increases in social and search engine optimization for B2B marketers, while B2C marketers favored digital advertising. Lead generation is often paire ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
