Visual Web Ripper: Using External Input Data Sources

Sometimes it is necessary to use external data sources to provide parameters for the scraping process. For example, you have a database with a bunch of ASINs and you need to scrape all product information for each one of them. As far as Visual Web Ripper is concerned, an input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values.

An input data source is normally used in one of these scenarios:

    To provide a list of input values for a web form
    To provide a list of start URLs
    To provide input values for Fixed Value elements
    To provide input values for scripts

Visual Web Ripper supports the following input data sources:

    SQL Server Database
    MySQL Database
    OleDB Database
    CSV File
    Script (A script can be used to provide data from almost any data source)

To see it in action you can download a sample project that uses an input CSV file with Amazon ASIN codes to generate Amazon start URLs and extract some product data. Place both the project file and the input CSV file in the default Visual Web Ripper project folder (My Documents\Visual Web Ripper\Projects).

For further information please look at the manual topic, explaining how to use an input data source to generate start URLs.


Source: http://extract-web-data.com/visual-web-ripper-using-external-input-data-sources/

Scraping Amazon.com with Screen Scraper

Let’s look how to use Screen Scraper for scraping Amazon products having a list of asins in external database.

Screen Scraper is designed to be interoperable with all sorts of databases and web-languages. There is even a data-manager that allows one to make a connection to a database (MySQL, Amazon RDS, MS SQL, MariaDB, PostgreSQL, etc), and then the scripting in screen-scraper is agnostic to the type of database.

Let’s go through a sample scrape project you can see it at work. I don’t know how well you know Screen Scraper, but I assume you have it installed, and a MySQL database you can use. You need to:

    Make sure screen-scraper is not running as workbench or server
    Put the Amazon (Scraping Session).sss file in the “screen-scraper enterprise edition/import” directory.
    Put the mysql-connector-java-5.1.22-bin.jar file in the “screen-scraper enterprise edition/lib/ext” directory.
    Create a MySQL database for the scrape to use, and import the amazon.sql file.
    Put the amazon.db.config file in the “screen-scraper enterprise edition/input” directory and edit it to contain proper settings to connect to your database.
    Start the screen scraper workbench

Since this is a very simple scrape, you just want to run it in the workbench (most of the time you want to run scrapes in server mode). Start the workbench, and you will see the Amazon scrape in there, and you can just click the “play” button.

Note that a breakpoint comes up for each item. It would be easy to save the scraped details to a database table or file if you want. Also see in the database the “id_status” changes as each item is scraped.

When the scrape is run, it looks in the database for products marked “not scraped”, so when you want to re-run the scrapes, you need to:

UPDATE asin
SET `id_status` = 0

Have a nice scraping! ))

P.S. We thank Jason Bellows from Ekiwi, LLC for such a great tutorial.


Source: http://extract-web-data.com/scraping-amazon-com-with-screen-scraper/

Using External Input Data in Off-the-shelf Web Scrapers

There is a question I’ve wanted to shed some light upon for a long time already: “What if I need to scrape several URL’s based on data in some external database?“.

For example, recently one of our visitors asked a very good question (thanks, Ed):

    “I have a large list of amazon.com asin. I would like to scrape 10 or so fields for each asin. Is there any web scraping software available that can read each asin from a database and form the destination url to be scraped like http://www.amazon.com/gp/product/{asin} and scrape the data?”

This question impelled me to investigate this matter. I contacted several web scraper developers, and they kindly provided me with detailed answers that allowed me to bring the following summary to your attention:
Visual Web Ripper

An input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values. You can find the additional information here.
Web Content Extractor

You can use the -at”filename” command line option to add new URLs from TXT or CSV file:

    WCExtractor.exe projectfile -at”filename” -s

projectfile: the file name of the project (*.wcepr) to open.
filename – the file name of the CSV or TXT file that contains URLs separated by newlines.
-s – starts the extraction process

You can find some options and examples here.
Mozenda

Since Mozenda is cloud-based, the external data needs to be loaded up into the user’s Mozenda account. That data can then be easily used as part of the data extracting process. You can construct URLs, search for strings that match your inputs, or carry through several data fields from an input collection and add data to it as part of your output. The easiest way to get input data from an external source is to use the API to populate data into a Mozenda collection (in the user’s account). You can also input data in the Mozenda web console by importing a .csv file or importing one through our agent building tool.

Once the data is loaded into the cloud, you simply initiate building a Mozenda web agent and refer to that Data list. By using the Load page action and the variable from the inputs, you can construct a URL like http://www.amazon.com/gp/product/%asin%.
Helium Scraper

Here is a video showing how to do this with Helium Scraper:


The video shows how to use the input data as URLs and as search terms. There are many other ways you could use this data, way too many to fit in a video. Also, if you know SQL, you could run a query to get the data directly from an external MS Access database like
SELECT * FROM [MyTable] IN "C:\MyDatabase.mdb"

Note that the database needs to be a “.mdb” file.
WebSundew Data Extractor
Basically this allows using input data from external data sources. This may be CSV, Excel file or a Database (MySQL, MSSQL, etc). Here you can see how to do this in the case of an external file, but you can do it with a database in a similar way (you just need to write an SQL script that returns the necessary data).
In addition to passing URLs from the external sources you can pass other input parameters as well (input fields, for example).
Screen Scraper

Screen Scraper is really designed to be interoperable with all sorts of databases. We have composed a separate article where you can find a tutorial and a sample project about scraping Amazon products based on a list of their ASINs.


Source: http://extract-web-data.com/using-external-input-data-in-off-the-shelf-web-scrapers/

Visual Web Ripper: Using External Input Data Sources

Sometimes it is necessary to use external data sources to provide parameters for the scraping process. For example, you have a database with a bunch of ASINs and you need to scrape all product information for each one of them. As far as Visual Web Ripper is concerned, an input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values.

An input data source is normally used in one of these scenarios:

    To provide a list of input values for a web form
    To provide a list of start URLs
    To provide input values for Fixed Value elements
    To provide input values for scripts

Visual Web Ripper supports the following input data sources:

    SQL Server Database
    MySQL Database
    OleDB Database
    CSV File
    Script (A script can be used to provide data from almost any data source)

To see it in action you can download a sample project that uses an input CSV file with Amazon ASIN codes to generate Amazon start URLs and extract some product data. Place both the project file and the input CSV file in the default Visual Web Ripper project folder (My Documents\Visual Web Ripper\Projects).

For further information please look at the manual topic, explaining how to use an input data source to generate start URLs.



Source: http://extract-web-data.com/visual-web-ripper-using-external-input-data-sources/

Various Data Mining Techniques

Also called Knowledge Discover in Databases (KDD), data mining is the process of automatically sifting through large volumes of data for patterns, using tools such as clustering, classification, association rule mining, and many more. There are several major data mining techniques developed and known today, and this article will briefly tackle them, along with tools for increased efficiency, including phone look up services.

Classification is a classic data mining technique. Based on machine learning, it is used to classify each item on a data set into one of predefined set of groups or classes. This method uses mathematical techniques, like linear programming, decision trees, neural network, and statistics. For instance, you can apply this technique in an application that predicts which current employees will most probably leave in the future, based on the past records of those who have resigned or left the company.

Association is one of the most used techniques, and it is where a pattern is discovered basing on a relationship of a specific item on other items within the same transaction. Market basket analysis, for example, uses association to figure out what products or services are purchased together by clients. Businesses use the data produced to devise their marketing campaign.

Sequential patterns, too, aim to discover similar patterns in data transaction over a given business phase or period. These findings are used for business analysis to see relationships among data.

Clustering makes useful cluster of objects that maintain similar characteristics using an automatic method. While classification assigns objects into predefined classes, clustering defines the classes and puts objects in them. Predication, on the other hand, is a technique that digs into the relationship between independent variables and between dependent and independent variables. It can be used to predict profits in the future - a fitted regression curve used for profit prediction can be drawn from historical sale and profit data.

Of course, it is highly important to have high-quality data in all these data mining techniques. A multi-database web service, for instance, can be incorporated to provide the most accurate telephone number lookup. It delivers real-time access to a range of public, private, and proprietary telephone data. This type of phone look up service is fast-becoming a defacto standard for cleaning data and it communicates directly with telco data sources as well.

Phone number look up web services - just like lead, name, and address validation services - help make sure that information is always fresh, up-to-date, and in the best shape for data mining techniques to be applied.

Equip your business with better leads and get better conversion rates by using phone look up and similar real-time web services.




Source: http://ezinearticles.com/?Various-Data-Mining-Techniques&id=6985662

Data Entry Services in India Are Getting Famous in the World!

Outsourcing has become the most profitable business in the world. This business is growing in India and other part of the world. These services are getting famous in the world and most of the business owners are saving their lots of money by doing outsourcing to different countries where India comes in top in the outsourcing. By outsourcing your offline and online information entry jobs, your company will maintain properly organized and up-to-date records of the employees and other important stuff. These jobs are usually done in the home environment.

India is very popular in providing the BPO services for their customers. There is large scale of BPO service providers running their business in India. The employees working in these offices are also very competent and trained. Data entry services in India is very popular all around the world because of having the access of BPO experts and the web data extraction experts.

What these BPO services provide you?

There are many business across the globe running on the outsource services, BPO services in India provides the ease of life to the business owner want quick and fast data entry work.

There are many well reputed firms working in India and doing their best to finish and deliver comes punctually. They're professional well equipped with the newest technology and software and more importantly with the professional labor work. They are fully trained and expert in their niche so if a business owner take the services then they get the in time work and quality. When you will select any BPO expert then you will find the following data entry expertise in these professional companies.

1. You will find the handwritten material with the help of experts.
2. Knowledge entry of e-books, directories, image files and etc.
3. You will also get the best services of data processing.
4. Business card knowledge entry
5. Bills and survey services which will help you to Maintain and correct records.
6. Alpha numeric data entry services
7. Data entry free trails.

Thousand of online BPO jobs are also available on the Indian big job portals and other data entry work. These services and work force reduce your workload and will enhance your productivity of your business. Outsourcing the right choice by any business owner because it reduces your total cost and you get the perfect and reliable work. When you approach to any professional service provider firm in India then it reduce the turnaround time and you get the professional data entry services.

Accurate, fast and reliable services are offered in India by the Bpo companies. Please visit Data Entry Services for more information.




Source: http://ezinearticles.com/?Data-Entry-Services-in-India-Are-Getting-Famous-in-the-World!&id=4708858

Data Processing Services - Different Types of Data Processing

Data processing services- To get proper information in specific and require data format and process your data which can be understand by people.

In the most of BPO (business process outsourcing) companies, converting your data (information) into right data format which is known as data processing services and also a very important part of the BPO company. There are many types of data process are available in the BPO industry such as check processing, insurance claim process, forms process, image process, survey processing and other business process services.

There is some important data processing services which can help to the business described as below:

Check-Processing: In any business, check processing is essential requirements to make easy online transactions. It will increase and make fast your business process.

Insurance-Claim-Processing: Sometime it is very complicated to handle. An insurance claim is an official request submitted to the insurance company demanding payment as per the terms of the policy. The terms of the insurance contract dictate the insurance claim amount.

Form-Processing: In the business, there are some important forms are used to process properly and receive accurate data or information. It is one of very crucial data online processing service.

Image-Processing: In electrical engineering and computer science, capturing and manipulating images to enhance or extract information. Image processing functions include resizing, sharpening, brightness, and contrast.

Survey-Processing: To make quick decision and want to market research, survey form is very much helpful in take proper decision or any important action.

Thus, these all important data process and conversion services can help any business to grow their profit and make business process very easy to access.




Source: http://ezinearticles.com/?Data-Processing-Services---Different-Types-of-Data-Processing&id=3874740

Information About Data Mining

The potential offered by data mining can be included in the category of the processes of the commercial enterprises and looking for information is not a purpose itself, but it is a very useful process if it is transformed into a real action. Thus, enterprises can choose to react to the different situations created by reality, such as the reduction of the number of customers, the loss of certain markets and so on. The next step after making this choice is the proper exploitation of the data, using different algorithms.

Very often, data mining turns out to be a complete failure and not a success, the measures adopted bot always being appropriate for the information obtained. All these elements which are mentioned above lead to the idea that there is a cycle with data mining and that there are four stages when it comes to this process.

First of all, you have to define the commercial possibilities and the data. Then, you have to get information from the existent data collections using data mining techniques, after which you have to make decisions referring to the subsequent actions using the results you obtain. Last but not least, you have to measure your results properly in order to identify other ways of exploiting the data, too. Of course, you should only be looking at the concrete results because the rest of them can meddle with the outcomes and can alter the quality of the ones you should be getting. Therefore, if you take these steps into consideration, you should be properly using data mining in administrating the activity of your company.

Get the best spyware remover for your computer from us, because we offer everything you need in terms of online spyware removal and not only.





Source: http://ezinearticles.com/?Information-About-Data-Mining&id=5214925

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

The author has a number of websites that provide financial calculators including the sites mortgage calculator amortization and refinance calculator mortgage.



Source: http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

An Easy Way For Data Extraction

There are so many data scraping tools are available in internet. With these tools you can you download large amount of data without any stress. From the past decade, the internet revolution has made the entire world as an information center. You can obtain any type of information from the internet. However, if you want any particular information on one task, you need search more websites. If you are interested in download all the information from the websites, you need to copy the information and pate in your documents. It seems a little bit hectic work for everyone. With these scraping tools, you can save your time, money and it reduces manual work.

The Web data extraction tool will extract the data from the HTML pages of the different websites and compares the data. Every day, there are so many websites are hosting in internet. It is not possible to see all the websites in a single day. With these data mining tool, you are able to view all the web pages in internet. If you are using a wide range of applications, these scraping tools are very much useful to you.

The data extraction software tool is used to compare the structured data in internet. There are so many search engines in internet will help you to find a website on a particular issue. The data in different sites is appears in different styles. This scraping expert will help you to compare the date in different site and structures the data for records.

And the web crawler software tool is used to index the web pages in the internet; it will move the data from internet to your hard disk. With this work, you can browse the internet much faster when connected. And the important use of this tool is if you are trying to download the data from internet in off peak hours. It will take a lot of time to download. However, with this tool you can download any data from internet at fast rate.There is another tool for business person is called email extractor. With this toll, you can easily target the customers email addresses. You can send advertisement for your product to the targeted customers at any time. This the best tool to find the database of the customers.

However, there are some more scraping tolls are available in internet. And also some of esteemed websites are providing the information about these tools. You download these tools by paying a nominal amount.

Maulik Jyotishi is the owner of scrapping expert website a company which specializes in web spider and data collection from web pages.




Source: http://ezinearticles.com/?An-Easy-Way-For-Data-Extraction&id=3517104

Spatial Data Mining Systems

Data mining systems are used for a variety of different purposes. Essentially, large amounts of data are stored in one particular spot, enabling organizations and companies to access information that will help them in their own marketing and surveillance strategies. By having access to all relevant data, a company can better employ their sales and production tactics. Companies and businesses can save large sums of money by researching past consumer behaviors and producing product in relation to how well it sold at certain times. This is just a small example of what data mining can do for a company.

Spatial data mining systems rely on the same principals. However, the data stored is related directly to special data. Spatial data mining systems are also used to detect patterns, but the patterns that are being looked for are geographical patterns. Up until this point geographical information systems and spatial data mining have existed as two separate technologies. Both systems have their own individual approaches to storing geographical data. Each system has derived from its own methods and traditions, making it difficult to cross the two. Geographical information systems tend to be much more basic and only provide the most simple form of functionality. Because there became a larger demand for geographically referenced data, the basic functions of GIS represented the massive need for more sophisticated methods of mining spatial data. There is a larger demand for geographical analysis and modeling as well as digital mapping and remote sensing.

Through spatial data mining, there have been numerous benefits experienced by those who make important decisions based on geographical information systems. Public and private sector organizations have recently become aware of the huge potential of the amount of information they possess in their thematic and geographical referenced databases. There are various types of companies who can benefit from geographical data. For example, those that are in the public health sector will use this data to determine the cause for epidemics such as disease clusters. In addition, some environmental agencies will use the information collected in these databases to understand the impact of land-use patterns that are in constant flux and how they relate to climate change. Geo-marketing companies will also find this information useful when they are conducting customer research regarding segmentation on spatial location.

However, spatial data mining systems force those who need them to face certain challenges. First of all, these databases tend to be extremely large and can be cumbersome to sort through when looking for specific information. Geographical information system datasets that already exist are usually split into featured and attributed components and this means that they are separated into hybrid data management systems. Both featured and attributed data systems require separate means of management. For example algorithmic requirements differ when it comes to relational data, which is in the attribute category and for topographical data, which falls under the feature category.

The two main systems for spatial data management are the raster and the vector. Depending on the needs of the data being used, it is important to analyze the benefits and downfalls of both systems.

Doing business in the 21st century doesn't have to be difficult - companies can enhance their marketing procedures through address validation software and various other list cleaning procedures so that they can target their market perfectly!




Source: http://ezinearticles.com/?Spatial-Data-Mining-Systems&id=4792735

The Benefits of Data Outsourcing

Data is the foundation of all companies and provides a source for multiplying your company with tremendous leaps and bounds. The benefits of data entry outsourcing are numerous with expansion of methodologies, which provide your business with many other numerous benefits. Data entry is a generalized term, which entails virtual services like data mining, data conversion, image processing, web data entry, data extraction and many others. All of these tasks are very much a stronghold in getting the processes of any company streamlined without wasting time and resources.

If you think about the benefits of data-entry outsourcing, it is necessary for any company to add the need of data entry along with other important resources, which go with this task. It is also important to know the format, which the final version of data is to be utilized. You want to go for data that is available for usage in a cross platform environment.

There are many benefits of outsourcing. In today's society, data-entry services offer peace of mine as well as a sigh of relief for business owners. Here are some benefits which data-entry outsourcing companies can offer your company:

o Your complete data management needs being taken care of. When you outsource your data-entry needs to an outside company, you are going to benefit with having managed and synchronized data. This will ensure that your company will save time. The best thing about outsourcing companies is that some of the managed data can be utilized for repository purposes.

o Time is very critical when dealing with competition. You want to get data in and out of your business in order to reap the maximum possible benefits in the least amount of time. Utilizing an outsourcing company minimizes your time spent while improving the efficiency of your business processes.

o Your sole purpose for utilizing data-entry outsourcing companies should be to receive quality as well as the most quantity for your dollar. Quality cannot be compromised. Quantity also needs to be delivered fast and on time. There is no leeway given when it comes to data entry work. Receiving data-entry work on time with fast turn around allows your business to benefit with the business overhead.

o Outsourcing companies are affordable and what does this mean for your business? This will reduce your business costs while maximizing your profits.

With these benefits mentioned, data entry outsourcing offers a lot of potential for expansion for your company. Learn More About Data Entry Service.




Source: http://ezinearticles.com/?The-Benefits-of-Data-Outsourcing&id=3331295

Data Mining For Professional Service Firms - The Marketing Mother Lode May Already Be in Your Files

No one needs to tell you about the value of information in today's world--particularly the value of information that could help grow your practice. But has it occurred to you that you probably have more information in your head and your existing files that you realize? Tap into this gold mine of data to develop a powerful and effective marketing plan that will pull clients in the door and push your profitability up.

The way to do this is with data mining, which is the process of using your existing client data and demographics to highlight trends, make predictions and plan strategies.

In other words, do what other kinds of businesses have been doing for years: Analyze your clients by industry and size of business, the type and volume of services used, the amount billed, how quickly they pay and how profitable their business is to you. With this information, you'll be able to spot trends and put together a powerful marketing plan.

To data mine effectively, your marketing department needs access to client demographics and financial information. Your accounting department needs to provide numbers on the services billed, discounts given, the amounts actually collected, and receivables aging statistics. You may identify a specific service being utilized to a greater than average degree by a particular industry group, revealing a market segment worth pursuing. Or you may find an industry group that represents a significant portion of your billed revenue, but the business is only marginally profitable because of write-offs and discounts. In this case, you may want to shift your marketing focus.

You should also look at client revenues and profitability by the age of the clients. If your percentage of new clients is high, it could mean you're not retaining a sufficient number of existing clients. If you see too few new clients, you may be in for problems when natural client attrition is not balanced by new client acquisition.

The first step in effective data mining is to get everyone in the firm using the same information system. This allows everyone in the office who needs the names and addresses of the firm's clients and contacts to have access to that data. Require everyone to record notes on conversations and meetings in the system. Of course, the system should also accommodate information that users don't want to share, such as client's private numbers or the user's personal contacts. This way, everyone can utilize the system for everything, which makes them more likely to use it completely.

Your information system can be either contact information or customer relationship management software (a variety of packages are on the market) or you can have a system custom designed. When considering software to facilitate data mining, look at three key factors:

1. Ease of use. If the program isn't easy to use, it won't get used, and will end up being just a waste of time and money.

2. Accessibility. The system must allow for data to be accessible from anywhere, including laptops, hand-held devices, from the internet or cell phones. The data should also be accessible from a variety of applications so it can be used by everyone in the office all the time, regardless of where they are.

3. Sharability. Everyone needs to be able to access the information, but you also need privacy and editing rights so you can assign or restrict what various users can see and input.

Don't overlook the issue of information security. Beyond allowing people the ability to code certain entries as private, keep in mind that anyone with access to the system as the ability to either steal information or sabotage your operation. Talk to your software vendor about various security measures but don't let too much security make the system unusable. Protect yourself contractually with noncompete and nondisclosure agreements and be sure to back up your data regularly.

Finally, expect some staffers to resist when you ask them to change from the system they've been using. You may have to sell them on the benefits outweighing the pain of making a change and learning the new system--which means you need to be totally sold on it yourself. The managing partner, or the leader of the firm, needs to be driving this initiative for it to succeed. When it does succeed, you'll be able to focus your marketing dollars and efforts in the most profitable areas with the least expense, with a tremendous positive impact on the bottom line.

Jacquelyn Lynn is a business writer and ghostwriter based in Orlando, Florida. She is the author or ghostwriter of more than 25 books, including Entrepreneur's Almanac; Online Shopper's Survival Guide; Make Big Profits on eBay (with Charlene Davis); In Search of the Five-Cent Nickel (with Don Abbott); and 11 titles in Entrepreneur Media's StartUp Guide series.

Jacquelyn writes and ghostwrites a wide range of materials, including articles, newsletters, brochures, social media copy, blogs, website copy, books, ebooks, white papers, special reports, and more. Visit her website at http://www.jacquelynlynn.com to sign up to receive free business tips via email.




Source: http://ezinearticles.com/?Data-Mining-For-Professional-Service-Firms---The-Marketing-Mother-Lode-May-Already-Be-in-Your-Files&id=4607430

Advantages of Data Mining in Various Businesses

Data mining techniques have advantages for several types of businesses, as well as there are more to be discovered over time. Since the era of the computer, things have been changing pretty quickly and every new step in the technology is equivalent to a revolution. Communication itself has not been enough. As compared to the present times, the data analyzers in the past have not achieved the chance to go further with the data they have in hand. Today, this data isn't used for selling more of a product but to foresee future risks as well as prevent them.

All are benefiting from modern these techniques even from smaller to large enterprises. They can now predict the outcome of a particular marketing campaign by analyzing them. However, in order for these techniques to be successful, the data must be arranged accurately. If your data is disseminated, you need to bring it in a meeting and then feed into the systems for the algorithms to figure it out. To put it shortly, no matter how small or big your business might be you always need to have the right system when collecting data from your customers, transactions and all business activities.

Advantages of Data Mining For Businesses

Businesses can truly benefit from its latest techniques; however, in the future, data mining techniques are expected to be even more concise and effective than they are today. Here are the essential techniques that you need to understand:

· Big companies providing the free web based email services can use data mining techniques to catch spam emails from their customer's inboxes. Their software uses a technique to assess whether an email is a spam or not. These techniques are first tested and validated before they are finally used. This is to ensure they are producing the correct results.

· Large retail stores and even shopping malls could make use of these techniques by registering and recording the transactions made by their customers. When customers are buying particular sets of product, it can give them a good understanding of placing these items in the aisle. If they want to change the order and placement of the item on weekends, it could be found out after analyzing the data on their database.

· Companies manufacturing edible or drinkable products could easily use data mining techniques to increase their sales in a particular area and launch new products based on the information they've obtained. That's why the conventional statistical analysis is rigid in scenarios wherein consumer behavior is in question. However, these techniques still manages to give you good analysis for any situations.

· In call centers, the human interaction is at its peak because people are talking with another people at all times. Customers respond differently when they talk to a female representative as opposed to talking to a male representative. The response of customers to an infomercial is different from their response to an ad in the newspaper. Data could be used for the benefit of the business and is best understood with the use of data mining techniques.

· Data mining techniques are also being used in sports today for analyzing the performances of players in the field. Any game could be analyzed with the help of these techniques; even the behaviors of players could be changed on the field through this.

In short, data mining techniques are giving the organizations, enterprises and smaller businesses the power of focusing on their most productive areas. These techniques also allow stores and companies to innovate their current selling techniques by unveiling the hidden trends of their customer's behavior, background, price of the products, placement, closeness to the related products and many more.



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Facts on Data Mining

Data mining is the process of examining a data set to extract certain patterns. Companies use this process to determine the outcome of their existing goals. They summarize this information into useful methods to create revenue and/or cut costs. When search engines are accessed, they begin to build lists of links from the first page it accesses. It continues this process throughout the site until it reaches the root page. This data not only includes text, but also numbers and facts.

Data mining focuses on consumers in relation to both "internal" (price, product positioning), and "external" (competition, demographics) factors which help determine consumer price, customer satisfaction, and corporate profits. It also provides a link between separate transactions and analytical systems. Four types of relationships are sought with data mining:

o Classes - information used to increase traffic
o Clusters - grouped to determine consumer preferences or logical relationships
o Associations - used to group products normally bought together (i.e., bacon, eggs; milk, bread)
o Patterns - used to anticipate behavior trends

This process provides numerous benefits to businesses, governments, society, and especially individuals as a whole. It starts with a cleaning process which removes errors and ensures consistency. Algorithms are then used to "mine" the data to establish patterns. With all new technology, there are positives and negatives. One negative issue that arises from the process is privacy. Although it is against the law, the selling of personal information over the Internet has occurred. Companies have to obtain certain personal information to be able to properly conduct their business. The problem is that the security systems in place are not adequately protecting this information.

From a customer viewpoint, data mining benefits businesses more than their interests. Their personal information is out there, possibly unprotected, and there is nothing they can do until a negative issue arises. On the other hand, from the business side, it helps enhance overall operations and aid in better customer satisfaction. In regards to the government, they use personal data to tighten security systems and protect the public from terrorism; however, they want to protect people's privacy rights as well. With numerous servers, databases, and websites out there, it becomes increasingly difficult to enforce stricter laws. The more information we introduce to the web, the greater the chances of someone hacking into this data.

Better security systems should be developed before data mining can truly benefit all parties involved. Privacy invasion can ruin people's lives. It can take months, even years, to regain a level of trust that our personal information will be protected. Benefits aside, the safety and well being of any human being should be top priority.




Source: http://ezinearticles.com/?Facts-on-Data-Mining&id=3640795

Benefits of Predictive Analytics and Data Mining Services

Predictive Analytics is the process of dealing with variety of data and apply various mathematical formulas to discover the best decision for a given situation. Predictive analytics gives your company a competitive edge and can be used to improve ROI substantially. It is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.

Predictive analytics can be helpful in answering questions like:

    Who are most likely to respond to your offer?
    Who are most likely to ignore?
    Who are most likely to discontinue your service?
    How much a consumer will spend on your product?
    Which transaction is a fraud?
    Which insurance claim is a fraudulent?
    What resource should I dedicate at a given time?

Benefits of Data mining include:

    Better understanding of customer behavior propels better decision
    Profitable customers can be spotted fast and served accordingly
    Generate more business by reaching hidden markets
    Target your Marketing message more effectively
    Helps in minimizing risk and improves ROI.
    Improve profitability by detecting abnormal patterns in sales, claims, transactions etc
    Improved customer service and confidence
    Significant reduction in Direct Marketing expenses

Basic steps of Predictive Analytics are as follows:

    Spot the business problem or goal
    Explore various data sources such as transaction history, user demography, catalog details, etc)
    Extract different data patterns from the above data
    Build a sample model based on data & problem
    Classify data, find valuable factors, generate new variables
    Construct a Predictive model using sample
    Validate and Deploy this Model

Standard techniques used for it are:

    Decision Tree
    Multi-purpose Scaling
    Linear Regressions
    Logistic Regressions
    Factor Analytics
    Genetic Algorithms
    Cluster Analytics
    Product Association


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Unleash the Hidden Potential of Your Business Data With Data Mining and Extraction Services

Every business, small or large, is continuously amassing data about customers, employees and nearly every process in their business cycle. Although all management staff utilize data collected from their business as a basis for decision making in areas such as marketing, forecasting, planning and trouble-shooting, very often they are just barely scratching the surface. Manual data analysis is time-consuming and error-prone, and its limited functions result in the overlooking of valuable information that improve bottom-lines. Often, the sheer quantity of data prevents accurate and useful analysis by those without the necessary technology and experience. It is an unfortunate reality that much of this data goes to waste and companies often never realize that a valuable resource is being left untapped.

Automated data mining services allow your company to tap into the latent potential of large volumes of raw data and convert it into information that can be used in decision-making. While the use of the latest software makes data mining and data extraction fast and affordable, experienced professional data analysts are a key part of the data mining services offered by our company. Making the most of your data involves more than automatically generated reports from statistical software. It takes analysis and interpretation skills that can only be performed by experienced data analysis experts to ensure that your business databases are translated into information that you can easily comprehend and use in almost every aspect of your business.

Who Can Benefit From Data Mining Services?

If you are wondering what types of companies can benefit from data extraction services, the answer is virtually every type of business. This includes organizations dealing in customer service, sales and marketing, financial products, research and insurance.

How is Raw Data Converted to Useful Information?

There are several steps in data mining and extraction, but the most important thing for you as a business owner is to be assured that, throughout the process, the confidentiality of your data is our primary concern. Upon receiving your data, it is converted into the necessary format so that it can be entered into a data warehouse system. Next, it is compiled into a database, which is then sifted through by data mining experts to identify relevant data. Our trained and experienced staff then scan and analyze your data using a variety of methods to identify association or relationships between variables; clusters and classes, to identify correlations and groups within your data; and patterns, which allow trends to be identified and predictions to be made. Finally, the results are compiled in the form of written reports, visual data and spreadsheets, according to the needs of your business.

Our team of data mining, extraction and analyses experts have already helped a great number of businesses to tap into the potential of their raw data, with our speedy, cost-efficient and confidential services. Contact us today for more information on how our data mining and extraction services can help your business.



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Data Mining - A Short Introduction

Data mining is an integral part of data analysis which contains a series of activities that goes from the 'meaning' of the ideas, to the 'analysis' of the data and up to the 'interpretation' and 'evaluation' of the outcome. The different stages of the technique are as follows:

Objectives for Analysis: It is sometimes very difficult to statistically define the phenomenon we wish to analyze. In fact, the business objectives are often clear, but the same can be difficult to formalize. A clear understanding of the crisis and the goals is very important setup the analysis correctly. This is undoubtedly, one of the most complex parts of the process, since it establishes the techniques to be engaged and as such, the objectives must be crystal clear and there should not be any doubt or ambiguity.

Collection, grouping and pre-processing of the data: Once the objectives of the analysis are set and defined, we need to gather or choose the data needed for the study. At first, it is essential to recognize the data sources. Usually data are collected from the internal sources as the same are economical and more dependable and moreover these data also has the benefit of being the outcome of the experiences and procedures of the business itself.

Investigative analysis of the data and their conversion: This stage includes a preliminary examination of the information available. It involves a preliminary assessment of the significance of the gathered data. An exploratory and / or investigative analysis can highlight the irregular data. An exploratory analysis is important because it lets the analyst choose the most suitable statistical method for the subsequent stage of the analysis.

Choosing statistical methods: There are multiple statistical methods that can be put into use for the purpose of analysis, so it is very essential to categorize the existing methods. The choice statistical method is case specific and depends on the problem and also upon the type of information available.

Data analysis on the basis of chosen methods: Once the statistical method is chosen, the same must be translated into proper algorithms for working out the results. Ranges of specialized and non-specialized software are widely available for data mining and as such it is not always required to develop ad hoc computation algorithms for the most 'standard' purpose. However, it is essential that the people managing the data mining method well aware and have a good knowledge and understanding of the various methods of data analysis and also the different software solutions available for the same, so that they may adapt the same in times of need of the company and can flawlessly interpret the results.

Assessment and contrast of the techniques used and selection of the final model for analysis: It is of utmost necessity to choose the best 'model' from the variety of statistical methods accessible. The selection of the model should be based in contrast with the results obtained. When assessing the performance of a specific statistical method and / or type, all other dependent and / or relevant criterions should also be considered. The other criterions may be the constraints on the company both in terms of time and resources or it may be in terms of quality and the accessibility of data.

Elucidation of the selected statistical model and its employment in the decision making process: The scope of data mining is not limited to data analysis rather it is also includes the integration of the results so as to facilitate the decision making process of the company. Business awareness, the pulling out of rules and their use in the decision process allows us to proceed from the diagnostic phase to the phase of decision making. Once the model is finalized and tested with an information set, the categorization rule can be generalized. But the inclusion of the data mining process in the business should not be done in haste; rather the same should always be done slowly, setting out sensible and logical aims. The final aim of data mining is to be an integral supporting part of the company's decision making process.




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Data Conversion Services - Transform Your Data Professionally

Data is least comprehensible when it is in a format unsupported by many of the usual data reading systems. This leaves us with a prudent choice - to convert data, which are in different formats to one or many of those commonly used formats. However, conversion shall get tricky and tedious if done by people who don't have any proficiency in transforming the formats. Data conversion services are being done by a team of experts who form a company and do this quite professionally. Resorting to such professionals would mean a lot to your business since they are the ones who are capable of producing desires output with the least possible proportion of error in the documented files that they submit at the end of the conversion process.

Many service providers especially from India have proven their capabilities of handling bulk projects quite regularly yet being able to deliver error-free documented forms of the desired output. To add, they have got the right mix of technological resources in the shape of few domestic systems in conjunction with a few resourceful application software with which they are able to deliver results every time. Plus, they also tend to put a line of validating systems in place so as to make sure there is not any undesirable change effected in the output once the conversion process is done. Once they manage to find a few mistakes or anomalies in the conversion system, they don't hesitate to overcome those by putting them under check and removing the bugs at necessary instances.

Only quite a few types of conversion are beholden as to be the primary ones having superior importance to several conversion mechanisms. In fact, the scenario is that the large scale and medium scale businesses demand only quite a few conversion mechanisms such as the PDF Conversion, XML Conversion, Book conversion, HTML Conversion to end with the OCR conversion. Through such a scattered set of primary data conversion practices, they lend their hand to assist in converting, extracting relevant amount of data from the sources, transcribing the rich set of essential information relevant to the current needs of the client and consolidating data so that on the whole the entire set of converted data is compatible to be read by the systems incorporated in the client's place.

No doubt, the data presented to them regardless of its format will be returned as a readily usable piece of information that can be customized to any form that we need it to see in. In most of the cases, they do not consume much of the time than what they should be given since they are dealing with several processes concurrently whose progress reflects on the output you receive from them. Moreover, they also shunt the possibility of these important data to be hacked by any third party by providing enough protection. Till niow, you have been reading about some benefits that you enjoy about the data conversion services, which do not charge much for working on your documents. Isn't it the biggest of all benefits?



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Advantages of Online Data Entry Services

People all over the world are enthusiastic to buy online data entry services as they find it cost effective. Most of them have an impression that they get quality services against the prices they have to pay. Entering data online is of a great help to business units of all sizes as they consider them as their main basis of profession.

Online data entering and typing services providers have skilled resources at their service who deliver quality work timely. These service providers have modernized technology, assuring cent percent security of data. Online data entry services include the following:

    Data entry
    Data Processing
    Product entry
    Data typing
    Data mining, Data capture/collection
    Business Process Outsourcing
    Data Conversion
    Form Filling
    Web and mortgage research
    Extraction services
    Online copying, pasting, editing, sorting, as well as indexing data
    E-books and e-magazines data entry

Get companies world wide quality services to business units of all sizes, some of the common input formats are:

    PDF
    TIFF
    GIF
    XBM
    JPG
    PNG
    BMP
    TGA
    XML
    HTML
    SGML
    Printed documents
    Hard copies, etc

Benefits of outsourcing online data entering services:

Major benefits of data entry for business units is that they get the facts and figures which helps in taking strategic decisions for the organization. The data projected by numbers turns to be a factor of evaluation that accelerates the progress of the business. Online data typing services maintain high level of security by using systems that are highly protected.

The business organization progresses because of right decisions taken with the help of superior quality data available.

    Save operational overhead expense.
    Saves time and space.
    Accurate services can be accessed.
    Eliminating the paper documents.
    Cost effective.
    Data accessible from anywhere in the world.
    100% work satisfaction.
    Access to professional and experienced data typing services.
    Adequate knowledge of wide range industrial needs.
    Use of highly advance technologies for quality results.

Business organizations find themselves blessed because of the benefits they receive out of outsourcing their projects on online data entering and typing services, because it not only saves their time but also saves a huge amount of money.

Upcoming business companies can focus on their key business functions instead of dealing with non-key business activities. They find it sensible to outsource their confidential and crucial projects to trustworthy online data entry services and remain free for their key business activities. These companies have several layers of quality control which assures 99.9% quality on projects on online data entry.



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