Choosing the right ad server for your company’s needs is not an easy process. The main problem is navigating in the blooming mass of propositions, available on the market. Time is one of the most valuable resources available, and you need the perfect tool that will help you save it, establishing your brand name in the process. Finding the right partner is extremely important, as switching between them brings a lot of inconvenience into the whole work process. Therefore, we wrote some tips that will help you to choose the Ad Server best suited for your business. Continue reading “How to choose relevant ad server for your business”
Business Process Outsourcing on Cloud- Reaping High ROI
Cloud brings in a great deal of benefits for different type of businesses, to cut costs and improve efficiency of different business processes. From SaaS to Iaas, there are about countless ways through which business can implement cloud to outsource their critical system and processes.
This article is going to discuss how in-take of cloud based solutions for BPO helps in gathering momentum.
Using Cloud to get The Maxim
You are going to get an ample of examples of where cloud computing has been done, for instance – an organic baby food manufacturer may be using the cloud to streamline its massive logistics, while other firms are thinking to sign up for cloud-based HR services.
This article focuses on the way various BPO companies are picking up clouds from market to test the environment, so that they execute more processes to their clients.
However, there is an interesting caveat to note here, is the suggestion that use of cloud is still in its early steps and 13% of business process outsourcing services is already cloud-based. With cloud call center services are able to cater massive amount of scope to businesses that are able to derive advantage from these benefits.
CLOUD- Driving Benefits for BPO
With cloud computing and business analytics, new life has been injected to BPO sector through reduced upfront costs and better use of data.
IT enabled BPO services has a large role to play, and they can be fine tuned to cut costs, but the best performing BPO deals are using IT to innovate and speculate.
Recent researches affirm that 20% of BPO projects are able to deliver sufficient business value for high performance. The findings from the research confirm the changing role of technology in BPO.
Software-as-a-service (SaaS) means businesses can introduce the applications used in BPO agreements without giving large upfront payments. Business magnets are of the opinion that best-of-breed technology is easily available and are easy to apply.
They further added, earlier BPO agreements are usually overloaded with cost-loaded technology. In past a business have to purchase license and then install heavy-duty business applications as a part of BPO deal, but now they can easily sign up for BPO services and easily scale up and down the number of users.
What is the concept of researchers on CLOUD, used in BPOs?
- 85% of high-performing BPO considers cloud service provider to be a strategic partner, compared with 41% of typical BPO services
- 75% of high-performance BPO involve senior leaders from both parties spending time to understand each other’s objectives and strategies
- 90% of high performers reported that client and provider were able to productively resolve conflicts
- 77% of high-performing BPO have successfully executed change management plans
- 85% of high-performing BPO proactively refine their objectives as the relationship matures
- 67% of BPO include business benefits beyond cost in the business case, compared with 26% of typical engagements;
- 58% of will consider service options with greater value, even at higher costs
- 56% of high performers seek competitive advantage through BPO
- 64% of BPO places more focus on capturing other benefits as they achieve cost reduction
- 54% have contract performance incentives in place
A new approach to cloud computing is helping enterprises to recognize more of the business transformative aspects, such as improvements to productivity, business agility and business continuity as they present plans and sells concept to management teams.
Author Bio: Emma Johnson is an outreach expert and is closely associated with lead generation and telemarketing services. She is in contact with inbound order taking services that are popular for their prompt answering services to appease both client and customer with quality outsourcing.
Today, in this world of information processing, business intelligence and business analytics, are gaining in importance very fast. The three major techniques or groups of algorithms which have gained a lot of visibility in recent times are fuzzy logic, neural networks and genetic algorithms. In this article, we discuss in brief about the possible business applications of genetic algorithms.
In general, genetic algorithms find their application in problem domains that have a complex fitness landscape with many criteria with divergent needs or fitness function curves. Typically, genetic algorithms find application in computational science, engineering, manufacturing, phylogenetics, bioinformatics, economics, chemistry, mathematics, physics and other related disciplines.
The most popular application of Genetic Algorithms is in the field of Search applications.
Mostly, in such search techniques, the end objective may be an optimization function where the genetic algorithm may be applied for Fast Search through a huge pool of possible solutions so as to find the most optimal solution fast, without compromising the fitness of purpose too much.
Similarly, problems which are often solved using genetic algorithms include time-table scheduling and job-scheduling problems. Many scheduling software packages use Genetic Algorithms as a predictor model. GAs have also been applied to classic engineering disciplines besides the information technology, communications, telecommunications, electronics and semiconductor industry. Genetic algorithms are also popular for usage as an approach to solve global optimization problems.
Some of the other popular applications of Genetic Algorithms are in computer-automated design, artificial creativity, automated design of industrial equipment, design of mechatronic systems, in the financial services sector, design of multi-objective decision making problems, bioinformatics, quality control, operations planning, chemical kinetics, clustering, code decryption, configuration applications, design of distribution systems, control engineering, computer network design, gene profiling, electronic circuit design, economics modelling, scheduling applications, marketing mix strategizing, molecular research, neural network based applications, software engineering, data compression, plant floor layout planning, and so much more.
By the way, did you read our article on the application of Fuzzy logic and Fuzzy set theory.
Today, in the world of data mining, business intelligence and analytics, techniques which can learn and provide decision support is gradually gaining in importance in leaps and bound. The three major techniques or groups of algorithms which have gained a lot of visibility in recent times are fuzzy logic, neural networks and genetic algorithms.
A genetic algorithm (GA) is a search based, self learning algorithm (technique) that imitates the theory of natural evolution based on selective screening of results based on fitness of purpose. This self learning algorithm is routinely used to generate solutions to multi-criteria decision making problems, optimization problems and search problems. Genetic algorithms have evolved from the more popular class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques mimicked from that of natural evolution like those of inheritance, selection, mutation and crossover.
The structure of a generic technique developed based on the genetic algorithm would follow the mention sequence given in the diagram below:
First while coding using a genetic algorithm, the first activity is to code the data-sets in such a way so as to be equatable for the fitness of purpose of the problem domain. Then typically, a random sample is chosen from the data set and the fitness of purpose of these data points are evaluated. Now for those points which have a higher fitness score, they are selected for further application of genetic algorithm optimization techniques like those of inheritance, selection, mutation and crossover. The other data points are rejected and the algorithm continues to recreate data-points from this pool of selected data points using the techniques of inheritance, selection, mutation and crossover.
Selection: During each successive iteration, a part of the existing population is selected to form a new generation sample pool. These data points are selected through a fitness-based purpose of usage in the problem domain, where fitter solutions (as measured by a fitness function) are selected preferentially. Some selection algorithms rate the fitness of purpose of each solution and preferentially select the best solutions. Other methods often rate only a random sample of the population, as this process may be very time-consuming.
Reproduction using Cross-over and Mutation
The next step is to generate the next generation population of data points from those selected earlier (using selection), through the operator algorithms called crossover (also called recombination), and/or mutation.
Cross-over: During cross-over, a pair (or triplet) of data points are chosen from the refined selection and they are combined and data-exchange takes place based on the individual coding. After the data exchange occurs multiple times, the new data points created from this process is returned to the selection pool to be operated by the selection operator.
Mutation: The coded data point obtained after the operation of selection on the sample pool of data points, is modified by tweaking one parameter to test its fitness factor and this continues for the entirely set of selected data points. The newly created generation of data-points are then tested using the selection operator again and the entirely process is restarted.
Although Crossover and Mutation are known as the main operators of genetic algorithm, there are also other operators such as regrouping, colonization-extinction, or migration in genetic algorithms.
The application of these operators and re-selection of these new generation data-points continue till the optimal fitness of purpose is attained. Common terminating conditions are often due to a solution satisfying minimum criteria, fixed number of generations being reached, allocated resources (complexity of computation, computation time, money) reached, the highest ranking solution’s fitness level is reaching or a fitness plateau has been reached such that successive iterations no longer produce better results, manual inspection produces better results or even combinations of these.
A fuzzy system is basically a control system based on fuzzy logic, which is a mathematical system that analyzes input values in terms of logical variables that take on continuous values between 0 and 1 instead of belongingness or non-belongingness to any set, as in crisp set theory or classical or digital logic, which operates either in 1 or in 0 for a logic to be true or false respectively. Fuzzy systems have become extremely popular in recent times for their various business applications.
Fuzzy logic on the other hand is the mathematical theory based on set theory, from which fuzzy systems have been developed. Essentially, fuzzy logic is based on fuzzy set theory which proposes that logical variables can take on continuous values between 0 and 1 instead of crisp belongingness or non-belongingness to any set, as in crisp set theory which operates either in 1 or in 0 for a logic to be true or false respectively.
Typically, a response in real life situation is not crisp, i.e. its not in black and white. Say one is asked to judge the performance of a technology application, typically the person (judge) will think that for every specification of feature there is a degree of acceptability in performance while in certain dimensions, the performance is not acceptable. This is precisely what fuzzy logic or fuzzy sets attempts to capture. Here, every item of a set may belong to multiple sets by a degree of belongingness and not by absolution of belongingness or non-belongingness to any set.
Today, Fuzzy sets and systems have taken a major interest from both the academia and the industry for its numerous and unlimited applicability and the scope. The first wave of application of these systems were primarily in process control and electronics, while slowly, these have made their impact felt in information systems development and deployment in business processes and activities. The largest applications are in the area of decision sciences in business intelligence and decision making, and is being thought of as one of the biggest change creating mathematical tool.
Fuzzy set theory provides a major newer paradigm in modeling and reasoning with uncertainty. Though there were several forerunners in science and philosophy, in particular in the areas of multivalued logic and vague concepts. Continue reading “Applications of Fuzzy Set Theory and Fuzzy Logic”
Over the past three decades, subtle changes have taken place in the theory and practice of marketing which has been reshaping companies. These changes have also been evident in marketing and management related information systems. The transaction based view and relationship based view of marketing is no more. Today, it is the era of information marketing or as popularly called database marketing. Continue reading “Marketing using Technology-The Sales and Marketing Productivity Systems”
Information management appears to be the talk of the day amongst all technology buzz. Information management or IM is the collection and management of information from one or more sources and the distribution of that information to one or more audiences. Management means the organization of and control over the structure, processing and delivery of information. Continue reading “Top 10 trends in Information Management technologies”