Concepts inOptimal marketing strategies over social networks
Marketing strategy
Marketing strategy is a process that can allow an organization to concentrate its limited resources on the greatest opportunities to increase sales and achieve a sustainable competitive advantage.
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Social network
A social network is a social structure made up of a set of actors (such as individuals or organizations) and the dyadic ties between these actors. The social network perspective provides a clear way of analyzing the structure of whole social entities. The study of these structures uses methods of social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.
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Viral marketing
Viral marketing, viral advertising, or marketing buzz are buzzwords referring to marketing techniques that use pre-existing social networks to produce increases in brand awareness or to achieve other marketing objectives (such as product sales) through self-replicating viral processes, analogous to the spread of viruses or computer viruses. It can be delivered by word of mouth or enhanced by the network effects of the Internet.
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Approximation algorithm
In computer science and operations research, approximation algorithms are algorithms used to find approximate solutions to optimization problems. Approximation algorithms are often associated with NP-hard problems; since it is unlikely that there can ever be efficient polynomial time exact algorithms solving NP-hard problems, one settles for polynomial time sub-optimal solutions.
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Pricing strategies
Pricing strategies for products or services encompass three main ways to improve profits. These are that the business owner can cut costs or sell more, or find more profit with a better pricing strategy. In the downturn of 2008-11, and since, when costs are likely already at their lowest and sales are hard to find, adopting a better pricing strategy is a key option to stay viable. Merely raising prices is not always the answer, especially in a poor economy.
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Hardness of approximation
In computer science, hardness of approximation is a field that studies the algorithmic complexity of finding near-optimal solutions to optimization problems. It complements the study of approximation algorithms by proving, for certain problems, a limit on the factors with which their solution can be efficiently approximated.
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Time complexity
In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the size of the input to the problem. The time complexity of an algorithm is commonly expressed using big O notation, which suppresses multiplicative constants and lower order terms. When expressed this way, the time complexity is said to be described asymptotically, i.e. , as the input size goes to infinity.
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