Web Mining

With the bang of the era of information technology, we have entered into an ocean of information. This information blast is strongly based on the internet; which has become one of the universal infrastructures of information. We can not deny the fact that, with every passing day, the web based information contents are increasing by leaps and bounds and as such, it is becoming more and more difficult to get the desired information which we are actually looking for. Web mining is a tool, which can be used in customizing the websites on the basis of its contents and also on the basis of the user interface. Web mining normally comprises of usage mining, content mining and structure mining.

Data mining, text mining and web mining, engages various techniques and procedures to take out appropriate information from the huge database; so that companies can take better business decisions with precision, hence, data mining, text mining and web mining helps a lot in the promotion of the 'customer relationship management' goals; whose primary objective is to kick off, expand, and personalize a customer relationship by profiling and categorizing customers.

However, there are numbers of matters that must be addressed while dealing with the process of web mining. Data privacy can be said to be the trigger-button issue. Recently, privacy violation complaints and concerns have escalated significantly, as traders, companies, and governments continue to gather and warehouse huge amount of private information. There are concerns, not only about the collection and compilation of private information, but also the analysis and use of such data. Fueled by the public's concern about the increasing volume of composed statistics and effective technologies; conflict between data privacy and mining is likely to root higher levels of inspection in the coming years. Legal conflicts are also pretty likely in this regard.

There are also other issues facing data mining. 'Erroneousness of Information' can lead us to vague analysis and incorrect results and recommendations. Customers' submission of incorrect data or false information during the data importation procedure creates a real hazard for the web mining's efficiency and effectiveness. Another risk in data mining is that the mining might get confused with data warehousing. Companies developing information warehouses without employing the proper mining software are less likely to reach to the level of accuracy and efficiency and also they are less likely to receive the full benefit from there. Likewise, cross-selling may pose a difficulty if it breaks the customers' privacy, breach their faith or annoys them with unnecessary solicitations. Web mining can be of great help to improve and line-up the marketing programs, which targets customers' interests and needs.

In spite of potential hurdles and impediments, the market for web mining is predicted to grow by several billion dollars in the coming years. Mining helps to identify and target the potential customers, whose information are "buried" in massive databases and to strengthen the customer relationships. Data mining tools can predict the future market trends and consumer behaviors, which can potentially help businesses to take proactive and knowledge-based resolutions. This is one of the causes why data mining is also termed as 'Knowledge Discovery'. It can be said to be the process of analyzing data from different points of view and sorting and grouping the identified data and finally to set up a useful information database, which can further be analyzed and exploited by companies to increase and generate revenue and cut costs. With the use of data mining, business organizations are finding it easier to answer queries relating to business aptitude and intelligence, which were very much complicated and intricate to analyze and determine earlier.


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What is Data Mining?

Data mining is the process in which there is analysis of data forming different angles and perspectives and summarizing the same data into the relevant information. This kind of information could be utilized to increase the revenue, cutting the costs or both.

Software is mainly used for analyzing data and also assists in accumulation of data for the different sources and categorize and summarize the given data into some useful form.

Though the data mining is new term, the software used for mining the data was previously used. With the constant upgradations of the software and the processing power, the market tools, data mining software has increased in its accuracy. Formerly, this data mining was widely used by the businessmen for the market research and the analysis. There were few companies that used the computers to examine through the column of the supermarket data.

The data mining is the technique of running the data through the sophisticated algorithms for discovering the meaningful correlations and patterns that would have otherwise remained hidden. It is very helpful, since it aids in understanding the techniques and methods of business and you can accordingly apply your own intelligence fitting in the current market trend. Even the future performances get enhanced by the predictive analysis.

Business Intelligence operations occur in the background. Users of the mining operation can just see the end result. The users are in apposition to get the results through the mails and can also go through the recommendation through web pages and emails.

The data mining process indicates the invention of trends and tactics. The moment you discover and understand the market trends, you have the knowledge of which article is sold more and which article is sold with the other one. This kind of tend has an enormous impact on business organization. In this manner, the business gets enhanced as the market gets analyzed in a perfect manner. Due to these correlations, the performance of business organization increases to a lot of extent.

Mining gives a chance or opportunity to enhance the future performance of the business organization. There is a common philosophical phrase that, 'he who does not learn from the history is destined to repeat the same'. Therefore, if these predictions are done with the help and assistance of the historical information (data), then you can get sufficient data for improvising the products of the business organization.

Mining enables the embedding of the recommendations in the applications. Simple summary statements and the proposals can be displayed within the operational applications. Data mining also needs powerful machines. The algorithms might be applied to a Java or a Dataset code for using the same. Data mining is very useful for knowing the trends and making future predictions based on the predictive analysis. It also helps in cost cutting and increase in the revenue of the business organization



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Data Entry Services - Using At Home Workers

Is there any question that the competition in the data entry market place is fierce? Just like in all other industries data entry service providers are always looking for ways to reduce cost, increase margins and look more attractive to the consumer. One way many companies have gone about this task is through the use of "at home workers". Is this a good idea? Does it impact the quality? Both are questions that should be investigated closely by both the provider and consumer.

Is this a good idea? First, let me state I am not against people trying to make a living by working at home. Honestly, I love the idea. Who doesn't dream of working for themselves, setting their own hours and working in their pajamas. My writing is focused more on the industry and quality of work, not the worker.

Let us get a feel for how companies utilize the at home worker. The use of this work force is broad, some companies actually outsource the majority of their work to this pool of workers, while some simply use the at home worker to fill in gaps (i.e. when the in house workloads become too great to handle). you might be thinking, "who cares?" as a consumer it should be a factor in selecting the right vendor for your company. Here are just a few pros and cons that should be considered:

Pros:

1. Cost savings to the service provider, reducing internal cost. vendors save money by reducing payroll, equipment cost, benefits, training, etc. One would hope these savings would be passed along to the client.

2. Increase to the current staff at hand. With more workers able to assist on a project, the time required to complete a data entry project should decreased. Most data entry service providers who choose to use at home workers utilize them either for ongoing projects, thus freeing up in house workers for new projects, or they use their in house staff for large ongoing projects and have the at home crew waiting in the wings for the new projects.

Cons:

1. Potential for security risk. I am not saying at home workers are evil and plotting to take your data, but let's be honest when there is a lack of direct supervision the occurrences of improper use of data increases. Keep in mind, not all at home workers are local to the vendor they work with. Depending on the type and sensitivity of the data you are outsourcing, data security should be a top priority.

2. Quality of work. The quality of the work, being of high importance, is many times much lower when completed by at home workers, when compared to the in house full time employee. In a conversation I recently had with a data entry vendor I learned that when they employed at home workers the accuracy level ranged anywhere from 78% to 92%, while the accuracy level of their in house employees ranged from 94% - 98%. This results because of lack of proper ongoing training and supervision.

It is necessary to note that in large, the at home worker is a dedicated, trustworthy and hard working group and if managed properly by the vendor can be a wonderful resource used to offer quality services. I simply recommend that during your due diligence in selecting the right data entry vendor, you ask how and where your work is being done. Should the company use at home workers ask about their training and quality controls.

I hope you find this information useful. Please feel free to contact me anytime, I am happy to answer all questions and help in any way I can.



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Tips on Getting Data Entry Freelance Work Online

One of the easiest jobs to get online is data entry work. You can work as a freelancer doing this job, either full-time or part-time. More and more companies around the world are trying to trim their overhead and save money by outsourcing data entry work to various freelance websites. The great thing about working through one of these websites is that you can work at home.

Find Online Freelance Websites

There are tons of freelance websites of employers seeking data entry workers. Some of the websites are free, while other sites cost some money to join, the pay sites are better and give you an opportunity to earn more money than the free sites. The free sites also have more scammers who will try to rip you off and make you work for free.

Work on Your Profile, Resume and Proposal

After you have joined one of these freelance websites you should work on your profile page and resume. The employers who will hire will be looking at these 2 things along with your proposal. The first thing the employer will see is your proposal which is similar to a cover letter. If your proposal is good, they will at your profile page and resume. Make sure your profile page depicts you as a professional hard worker who has experience in the field. Your resume should be written geared towards a position as a data entry worker, so try to only include your past experiences that relate to this job.

Brush Up on Your Data Entry Skills

Most data entry jobs will require you to enter loads of information on to a database in a short amount of time. Make sure that your typing skills are quick and accurate. You want to build up a good reputation, so double check your work and try to send your work a few days early if possible. Try to make sure your work has no errors, employers will usually be able to rate you on your work after they have paid you and you have finished the work. You will receive a bad rating if your work contains any errors and is handed in late. If you receive bad ratings, especially if you are new and just starting out, then it will be extremely difficult for you to land new jobs. New employers will be able to see what your previous employers have rated you and written about you.



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Data Mining - Critical for Businesses to Tap the Unexplored Market

Knowledge discovery in databases (KDD) is an emerging field and is increasingly gaining importance in today's business. The knowledge discovery process, however, is vast, involving understanding of the business and its requirements, data selection, processing, mining and evaluation or interpretation; it does not have any pre-defined set of rules to go about solving a problem. Among the other stages, the data mining process holds high importance as the task involves identification of new patterns that have not been detected earlier from the dataset. This is relatively a broad concept involving web mining, text mining, online mining etc.

What Data Mining is and what it is not?

The data mining is the process of extracting information, which has been collected, analyzed and prepared, from the dataset and identifying new patterns from that information. At this juncture, it is also important to understand what it is not. The concept is often misunderstood for knowledge gathering, processing, analysis and interpretation/ inference derivation. While these processes are absolutely not data mining, they are very much necessary for its successful implementation.

The 'First-mover Advantage'

One of the major goals of the data mining process is to identify an unknown or rather unexplored segment that had always existed in the business or industry, but was overlooked. The process, when done meticulously using appropriate techniques, could even make way for niche segments providing companies the first-mover advantage. In any industry, the first-mover would bag the maximum benefits and exploit resources besides setting standards for other players to follow. The whole process is thus considered to be a worthy approach to identify unknown segments.

The online knowledge collection and research is the concept involving many complications and, therefore, outsourcing the data mining services often proves viable for large companies that cannot devote time for the task. Outsourcing the web mining services or text mining services would save an organization's productive time which would otherwise be spent in researching.

The data mining algorithms and challenges

Every data mining task follows certain algorithms using statistical methods, cluster analysis or decision tree techniques. However, there is no single universally accepted technique that can be adopted for all. Rather, the process completely depends on the nature of the business, industry and its requirements. Thus, appropriate methods have to be chosen depending upon the business operations.

The whole process is a subset of knowledge discovery process and as such involves different challenges. Analysis and preparation of dataset is very crucial as the well-researched material could assist in extracting only the relevant yet unidentified information useful for the business. Hence, the analysis of the gathered material and preparation of dataset, which also considers industrial standards during the process, would consume more time and labor. Investment is another major challenge in the process as it involves huge cost on deploying professionals with adequate domain knowledge plus knowledge on statistical and technological aspects.

The importance of maintaining a comprehensive database prompted the need for data mining which, in turn, paved way for niche concepts. Though the concept has been present for years now, companies faced with ever growing competition have realized its importance only in the recent years. Besides being relevant, the dataset from where the information is actually extracted also has to be sufficient enough so as to pull out and identify a new dimension. Yet, a standardized approach would result in better understanding and implementation of the newly identified patterns.



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Basics of Web Data Mining and Challenges in Web Data Mining Process

Today World Wide Web is flooded with billions of static and dynamic web pages created with programming languages such as HTML, PHP and ASP. Web is great source of information offering a lush playground for data mining. Since the data stored on web is in various formats and are dynamic in nature, it's a significant challenge to search, process and present the unstructured information available on the web.

Complexity of a Web page far exceeds the complexity of any conventional text document. Web pages on the internet lack uniformity and standardization while traditional books and text documents are much simpler in their consistency. Further, search engines with their limited capacity can not index all the web pages which makes data mining extremely inefficient.

Moreover, Internet is a highly dynamic knowledge resource and grows at a rapid pace. Sports, News, Finance and Corporate sites update their websites on hourly or daily basis. Today Web reaches to millions of users having different profiles, interests and usage purposes. Every one of these requires good information but don't know how to retrieve relevant data efficiently and with least efforts.

It is important to note that only a small section of the web possesses really useful information. There are three usual methods that a user adopts when accessing information stored on the internet:

• Random surfing i.e. following large numbers of hyperlinks available on the web page.
• Query based search on Search Engines - use Google or Yahoo to find relevant documents (entering specific keywords queries of interest in search box)
• Deep query searches i.e. fetching searchable database from eBay.com's product search engines or Business.com's service directory, etc.

To use the web as an effective resource and knowledge discovery researchers have developed efficient data mining techniques to extract relevant data easily, smoothly and cost-effectively.


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What's Your Excuse For Not Using Data Mining?

In an earlier article I briefly described how data mining and RFM analysis can help marketers be more efficient (read... increased marketing ROI!). These marketing analytics tools can significantly help with all direct marketing efforts (multichannel campaign management efforts using direct mail, email and call center) and some interactive marketing efforts as well. So, why aren't all companies using it today? Well, typically it comes down to a lack of data and/or statistical expertise. Even if you don't have data mining expertise, YOU can benefit from data mining by using a consultant. With that in mind, let's tackle the first problem -- collecting and developing the data that is useful for data mining.

The most important data to collect for data mining include:

oTransaction data - For every sale, you at least need to know the product and the amount and date of the purchase.

oPast campaign response data - For every campaign you've run, you need to identify who responded and who didn't. You may need to use direct and indirect response attribution.

oGeo-demographic data - This is optional, but you may want to append your customer file/database with consumer overlay data from companies like Acxiom.

oLifestyle data - This is also an optional append of indicators of socio-economic lifestyle that are developed by companies like Claritas. All of the above data may or may not exist in the same data source. Some companies have a single holistic view of the customer in a database and some don't. If you don't, you'll have to make sure all data sources that contain customer data have the same customer ID/key. That way, all of the needed data can be brought together for data mining.

How much data do you need for data mining? You'll hear many different answers, but I like to have at least 15,000 customer records to have confidence in my results.

Once you have the data, you need to massage it to get it ready to be "baked" by your data mining application. Some data mining applications will automatically do this for you. It's like a bread machine where you put in all the ingredients -- they automatically get mixed, the bread rises, bakes, and is ready for consumption! Some notable companies that do this include KXEN, SAS, and SPSS. Even if you take the automated approach, it's helpful to understand what kinds of things are done to the data prior to model building.

Preparation includes:

oMissing data analysis. What fields have missing values? Should you fill in the missing values? If so, what values do you use? Should the field be used at all?

oOutlier detection. Is "33 children in a household" extreme? Probably - and consequently this value should be adjusted to perhaps the average or maximum number of children in your customer's households.

oTransformations and standardizations. When various fields have vastly different ranges (e.g., number of children per household and income), it's often helpful to standardize or normalize your data to get better results. It's also useful to transform data to get better predictive relationships. For instance, it's common to transform monetary variables by using their natural logs.

oBinning Data. Binning continuous variables is an approach that can help with noisy data. It is also required by some data mining algorithms.


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Understanding Data Mining

Well begun is half done. We can say that the invention of Internet is the greatest invention of the century which allows for quick information retrieval. It also has negative aspects, as it is an open forum therefore differentiating facts from fiction seems tough. It is the objective of every researcher to know how to perform mining of data on the Internet for accuracy of data. There are a number of search engines that provide powerful search results.

Knowing File Extensions in Data Mining

For mining data the first thing is important to know file extensions. Sites ending with dot-com are either commercial or sales sites. Since sales is involved there is a possibility that the collected information is inaccurate. Sites ending with dot-gov are of government departments, and these sites are reviewed by professionals. Sites ending with dot-org are generally for non-profit organizations. There is a possibility that the information is not accurate. Sites ending with dot-edu are of educational institutions, where the information is sourced by professionals. If you do not have an understanding you may take help of professional data mining services.

Knowing Search Engine Limitations for Data Mining

Second step is to understand when performing data mining is that majority search engines have filtering, file extension, or parameter. These are restrictions to be typed after your search term, for example: if you key in "marketing" and click "search," every site will be listed from dot-com sites having the term "marketing" on its website. If you key in "marketing site.gov," (without the quotation marks) only government department sites will be listed. If you key in "marketing site:.org" only non-profit organizations in marketing will be listed. However, if you key in "marketing site:.edu" only educational sites in marketing will be displayed. Depending on the kind of data that you want to mine after your search term you will have to enter "site.xxx", where xxx will being replaced by.com,.gov,.org or.edu.

Advanced Parameters in Data Mining

When performing data mining it is crucial to understand far beyond file extension that it is even possible to search particular terms, for example: if you are data mining for structural engineer's association of California and you key in "association of California" without quotation marks the search engine will display hundreds of sites having "association" and "California" in their search keywords. If you key in "association of California" with quotation marks, the search engine will display only sites having exactly the phrase "association of California" within the text. If you type in "association of California" site:.com, the search engine will display only sites having "association of California" in the text, from only business organizations.

If you find it difficult it is better to outsource data mining to companies like Online Web Research Services


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Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.


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Data Mining Explained

Overview
Data mining is the crucial process of extracting implicit and possibly useful information from data. It uses analytical and visualization techniques to explore and present information in a format which is easily understandable by humans.

Data mining is widely used in a variety of profiling practices, such as fraud detection, marketing research, surveys and scientific discovery.

In this article I will briefly explain some of the fundamentals and its applications in the real world.

Herein I will not discuss related processes of any sorts, including Data Extraction and Data Structuring.

The Effort
Data Mining has found its application in various fields such as financial institutions, health-care & bio-informatics, business intelligence, social networks data research and many more.

Businesses use it to understand consumer behavior, analyze buying patterns of clients and expand its marketing efforts. Banks and financial institutions use it to detect credit card frauds by recognizing the patterns involved in fake transactions.

The Knack
There is definitely a knack to Data Mining, as there is with any other field of web research activities. That is why it is referred as a craft rather than a science. A craft is the skilled practicing of an occupation.

One point I would like to make here is that data mining solutions offers an analytical perspective into the performance of a company depending on the historical data but one need to consider unknown external events and deceitful activities. On the flip side it is more critical especially for Regulatory bodies to forecast such activities in advance and take necessary measures to prevent such events in future.

In Closing
There are many important niches of Web Data Research that this article has not covered. But I hope that this article will provide you a stage to drill down further into this subject, if you want to do so!

Should you have any queries, please feel free to mail me. I would be pleased to answer each of your queries in detail.


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Data Management Services

In recent studies it has been revealed that any business activity has astonishing huge volumes of data, hence the ideas has to be organized well and can be easily gotten when need arises. Timely and accurate solutions are important in facilitating efficiency in any business activity. With the emerging professional outsourcing and data organizing companies nowadays many services are offered that matches the various kinds of managing the data collected and various business activities. This article looks at some of the benefits that accrue of offered by the professional data mining companies.

Entering of data

These kinds of services are quite significant since they help in converting the data that is needed in high ideal and format that is digitized. In internet some of this data can found that is original and handwritten. In printed paper documents and or text are not likely to contain electronic or needed formats. The best example in this context is books that need to be converted to e-books. In insurance companies they also depend on this process in processing the claims of insurance and at the same time apply to the law firms that offer support to analyze and process legal documents.

EDC

That is referred to as electronic data. This method is mostly used by clinical researchers and other related organization in medical. The electronic data and capture methods are used in the utilization in managing trials and research. The data mining and data management services are given in upcoming databases for studies. The ideas contained can easily be captured, other services being done and the survey taken.

Data changing

This is the process of converting data found in one format to another. Data extraction process often involves mining data from an existing system, formatting it, cleansing it and can be installed to enhance both availability and retrieving of information easily. Extensive testing and application are the requirements of this process. The service offered by data mining companies includes SGML conversion, XML conversion, CAD conversion, HTML conversion, image conversion.

Managing data service

In this service it involves the conversion of documents. It is where one character of a text may need to be converted to another. If we take an example it is easy to change image, video or audio file formats to other applications of the software that can be played or displayed. In indexing and scanning is where the services are mostly offered.

Data extraction and cleansing

Significant information and sequences from huge databases and websites extraction firms use this kind of service. The data harvested is supposed to be in a productive way and should be cleansed to increase the quality. Both manual and automated data cleansing services are offered by data mining organizations. This helps to ensure that there is accuracy, completeness and integrity of data. Also we keep in mind that data mining is never enough.

Web scraping, data extraction services, web extraction, imaging, catalog conversion, web data mining and others are the other management services offered by data mining organization. If your business organization needs such services here is one that can be of great significance that is web scraping and data mining


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Advantages of Outsourcing Data Conversion Services

Data conversion is the process of converting data from one format to another. In this era of IT revolution, data conversion services is a vital tool in getting information on finger tips. It has acquired a unique place in this internet driven, fast growing business world.

It gives handiness and security to business organizations in managing, updating and retrieving data. This services help firms to convert their precious data and gather papers into digital format for long-term storage. The data can be stored for the purpose of archiving, easy searching, accessing and sharing.

More and more highly experienced BPO companies are coming into this market providing full range of reliable and trustworthy data conversion services to their clients worldwide. These BPO companies are fully prepared with excellent infrastructure and skilled manpower as per clients' expectations and specifications.

Some of the data conversion services which are available in market are as follows:

    Document conversion
    HTML conversion
    XML conversion
    SGML conversion
    CAD conversion
    Image Conversion
    Book conversion
    PDF conversion
    Catalog conversion
    MS Excel conversion
    Indexing
    OCR / ICR Clean up, OMR

It is a process of changing bits from one format to another in order to get relative interoperability or ability to use new features. By outsourcing  this services companies can minimize the risk, cut down costs and thereby focus on their core issues. Offshore BPO companies are consistent, simple and one stop solution provider.

Advantages of outsourcing data conversion services:

    Focus on core business activities
    Avoids paper work
    Cuts down operating expenses
    Promotes business as effectively as possible
    Eliminates data redundancy
    Easy accessibility of data at any time
    Systemizes company's data in simpler format

If you are planning to outsource this kind of task to an external service provider, better make sure that the provider is consistent in quality, productivity and customer service operations. Automating any business by conversion services definitely increases the productivity of that company.


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Data Mining As a Process

The data mining process is also known as knowledge discovery. It can be defined as the process of analyzing data from different perspectives and then summarizing the data into useful information in order to improve the revenue and cut the costs. The process enables categorization of data and the summary of the relationships is identified. When viewed in technical terms, the process can be defined as finding correlations or patterns in large relational databases. In this article, we look at how data mining works its innovations, the needed technological infrastructures and the tools such as phone validation.

Data mining is a relatively new term used in the data collection field. The process is very old but has evolved over the time. Companies have been able to use computers to shift over the large amounts of data for many years. The process has been used widely by the marketing firms in conducting market research. Through analysis, it is possible to define the regularity of customers shopping. How the items are bought. It is also possible to collect information needed for the establishment of revenue increase platform. Nowadays, what aides the process is the affordable and easy disk storage, computer processing power and applications developed.

Data extraction is commonly used by the companies that are after maintaining a stronger customer focus no matter where they are engaged. Most companies are engaged in retail, marketing, finance or communication. Through this process, it is possible to determine the different relationships between the varying factors. The varying factors include staffing, product positioning, pricing, social demographics, and market competition.

A data-mining program can be used. It is important note that the data mining applications vary in types. Some of the types include machine learning, statistical, and neural networks. The program is interested in any of the following four types of relationships: clusters (in this case the data is grouped in relation to the consumer preferences or logical relationships), classes (in this the data is stored and finds its use in the location of data in the per-determined groups), sequential patterns (in this case the data is used to estimate the behavioral patterns and patterns), and associations (data is used to identify associations).

In knowledge discovery, there are different levels of data analysis and they include genetic algorithms, artificial neural networks, nearest neighbor method, data visualization, decision trees, and rule induction. The level of analysis used depends on the data that is visualized and the output needed.

Nowadays, data extraction programs are readily available in different sizes from PC platforms, mainframe, and client/server. In the enterprise-wide uses, size ranges from the 10 GB to more than 11 TB. It is important to note that two crucial technological drivers are needed and are query complexity and, database size. When more data is needed to be processed and maintained, then a more powerful system is needed that can handle complex and greater queries.

With the emergence of professional data mining companies, the costs associated with process such as web data extraction, web scraping, web crawling and web data mining have greatly being made affordable.


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New Method of Market Segmentation - Combining Segmentation With Data Mining

Marketers have the ability to get high-fidelity information on their target markets through market segmentation. Market segmentation is the process of categorizing potential customers based on certain variables, such as age, gender, and income. A market segment is a group of customers that will react in the same way to a particular marketing campaign. By gathering this information, marketers can tailor their campaigns to groups of prospects to build stronger relationships with them.

Marketers gather this demographic information through surveys, usually when the customer submits a product rebate or willingly participates in a customer satisfaction survey. Over the majority of the past few decades, market segmentation consisted of differentiating prospects based on very simple variables: income, race, location, etc. While this is definitely important information to have on your target market, modern market segmentation takes into account more integrated information.

Modern segmentation breaks the market into target clusters that take into account not only standard demographics, but also other factors such as population density, psychographics, and buying and spending habits of customers. By focusing on these variables in addition to standard demographics, you can gain deeper insight into customer behavior.

Using standard demographics, you can tailor your marketing pieces to specific groups of people. But, by including these more sophisticated variables in your segmentation process, you can determine achieve a higher degree of "lift" or return on your segmentation efforts.

Segmenting your market on these factors helps you realize your total opportunity and revenue potential. It can enable you to better compete with similar product or service providers and lets you know where you stand within the game. It can help you target untapped market opportunities and allow you to better reach and retain customers.

Market segmentation depends on the gathering of high-quality, usable data. Many companies exist to gather and sell massive databases of targeted customer information, as well as providing consultation services to help you make sense of data bought or already owned. The key to the process is determining the best way to split up data.

There are essentially two methods for categorizing customers. Segments can either be determined in advance and then customers are assigned to each segment, or the actual customer data can be analyzed to identify naturally occurring behavioral clusters. Each cluster forms a particular market segment.

The benefit of cluster-based segmentation is that as a market's behavior changes, you can adapt your campaigns to better suit the cluster. The latest techniques blend cluster-based segmentation with deeper customer information acquired via data mining. Data mining uses algorithms to interrogate data within a database, and can produce information such as buying frequency and product types.

This new method of market segmentation, combining segmentation with data mining, provides marketers with high quality information on how their customers shop for and purchase their products or services. By combining standard market segmentation with data mining techniques you can better predict and model the behavior of your segments.


Source: http://ezinearticles.com/?New-Method-of-Market-Segmentation---Combining-Segmentation-With-Data-Mining&id=6890243

Online Data Entry - A Guide For College Students to a Successful Data Entry Work!

Albert Einstein once quoted, "It is a miracle that curiosity survives formal education." This is especially true since many college students today have explored some options to have extra cash for their studies. Students have devised numerous ways to earn extra money. Many students conduct college fairs. Some have tried garage sale as well. Others do part-time work in fast food chains.

However, not many students have heard of how online data entry can help them in their financial needs. In this article, I would like to share some guidelines and insights on questions every student often has in online entry job.

Does this type of work require me to give full time hours?

This work only requires the time that you can spare, from 30 minutes to a couple of hours. It depends on how much workload you can handle and the hours that will fit your schedule. This work does not necessarily require you to log on a specified period. You work in the hours of your choosing and the time that is available in your part.

What exactly is online data entry?

In contrast to the offline data entry where data is keyed in from one form to the other (like word to excel), online entry work requires the full use of the internet where data gathering and submission is done directly to a server, a remote desktop or a web software.

What are the skills needed in this type of online work?

You can start with an average typing speed. You also need to know how to use an office suite program and you should have a basic knowledge of the internet where you will be required to search, download, install, copy, paste, etc.

What is the easiest way to begin in this online entry job?

The best way you can do to have a good start is to find yourself a reliable online program that can help and guide you with training tools, video tutorials, PC support and access to company lists of available work.

What are the examples of projects for this type of work?

Some past job projects in data entry include outsource work to update company profiles, writing product catalogs, typing short ads for online submission, etc.

Can I work in my laptop while I travel or is this just a home-based venture?

The best thing about this work is that it does not have strict requirements on any one. There are no specified experiences required. You only need the basic computer hardware that to get started. Absolutely anyone can try online data entry work as long as you have a computer and internet connection.


Source: http://ezinearticles.com/?Online-Data-Entry---A-Guide-For-College-Students-to-a-Successful-Data-Entry-Work!&id=3477439

Elevate Your Business With Data Entry Services

The sole aim of many organizations is to progress well in their objectives and hire people who are good and efficient in their work. However, sometimes, there are some work profiles that are mundane in nature but equally important like data entry services. You will be amazed to know that these services also play a crucial role in building the future of an organization.

In fact, with the coming of information technology, the data entry services have actually become a kind of industry, as various businesses need accurate and detailed information for various reasons. Thus they are relying on such services that not only help them in growing but are cost effective too. These data entry services are an asset for any organization irrespective of its size in both the terms of workforce, financial status and area. With the help of such services you are able to get the information on the market trends, your clients and moreover, about the status of your own business. Hence, there is a lot of demand for data entry services in order to do great business.

As you must be aware of the fact that data entry services can be time consuming; hence it requires efficient workforce to execute various tasks perfectly and diligently. Every transaction has to be recorded, processed and analyzed so that the management or the decision-makers can have a clear picture of the actual financial standing of the company. In fact, there are many organizations that are interested in the data of company so that they can strike a business deal with the company in the future; the competitors are also the one's who are constantly following the happenings of the company. However, the most important part that constitutes group are the shareholders, employees, creditors, consumers and the market in general. Therefore, this service plays a significant role in determining the future of the company. Thus, it is taken very seriously by many business enterprises for various reasons that can elevate their businesses by many fractions.

In fact, data entry services are now being outsourced from various leading vendors to further simplify the requirements of every business. Well, these services cover many business activities like document and image processing, data conversion, image enhancement, image editing, catalog processing, and photo manipulation. In fact, you can use data entry services for transferring hard or soft copy to any database format; insurance claims entry; PDF document indexing; online data capture; product catalogs to web based systems; online order entry and follow up; creation of new databases. Moreover, banks, airlines, government agencies, direct marketing services and service providers are using these services for better businesses.

The data services are also utilized for mailing lists; data mining and warehousing; data cleansing; audio transcriptions; legal documents; indexing of vouchers and documents; hand written ballot or card entry; online completion of surveys and responses of customers for various companies. Now its up to the company to whether go for a vendor or hire in-house staff to accomplish tasks in a better way; the main purpose of this service is to offer convenience that can help in curbing time as well as other resources.



Source: http://ezinearticles.com/?Elevate-Your-Business-With-Data-Entry-Services&id=777230