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.


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Outsourcing Data Entry Services

Data or raw information is the backbone of any industry or business organization. However, raw data is seldom useful in its pure form. For it to be of any use, data has to be recorded properly and organized in a particular manner. Only then can data be processed. That is why it is important to ensure accurate data entry. But because of the unwieldy nature of data, feeding data is a repetitive and cumbersome job and it requires heavy investment, both in terms of time and energy from staff. At the same time, it does not require a high level of technical expertise. Due to these factors, data entry can safely be outsourced, enabling companies to devote their time and energy on tasks that enhance their core competence.

Many companies, big and small, are therefore enhancing their productivity by outsourcing the endless monotonous tasks that tend to cut down the organization's productivity. In times to come, outsourcing these services will become the norm and the volume of work that is outsourced will multiply. The main reason for these kinds of development is the Internet. Web based customer service and instant client support has made it possible for service providers to act as one stop business process outsourcing partners to parent companies that require support.

Data entry services are not all alike. Different clients have different demands. While some clients may require recording information coupled with document management and research, others may require additional services like form processing or litigation support. Data entry itself could be from various sources. For instances, sometimes information may need to be typed out from existing documents while at other times, data needs to be extracted from images or scanned documents. To rise up to these challenges, service providers who offer these services must have the expertise and the software to ensure rapid and accurate data entry. That is why it is important to choose your service provider with a lot of care.

Before hiring your outsourcing partner, you need to ask yourself the following questions.

* What kind of reputation does the company enjoy? Do they have sufficient years of experience? What kind of history and background does the company enjoy?

* Do they have a local management arm that you can liaise with on a regular basis?

* Do the service personnel understand your requirements and can they handle them effectively?

* What are the steps taken by the company to ensure that there is absolutely no compromise in confidentiality and security while dealing with vital confidential data?

* Is there a guarantee in place?

* What about client references?

The answers to these questions will help you identify the right partner for outsourcing your data entry service requirements.


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Data Entry Services, Benefits of Data Entry Outsourcing

Nowadays Data Entry is the fastest growing term in BPO industry. It is a most valuable term for all types of organizations and it is used to manage all types of data in to easily accessible manners. In globalized business world it covered all core business activities. For all organizations, it is always a challenge to maintain their data and it can be satisfied by professional services.

For business organizations time is money and many organizations can't spend time to manage their data. To resolve this problem BPO industry has introduced Data Entry Outsourcing. Today many organizations are outsourcing their requirements to professional organizations. Outsourcing companies offer following services for various purposes:

• Textual Data Entry
• Numerical Data Typing
• Alphanumerical Typing
• Online Form Entry
• Offline Data Solution

Different types of data typing facilitate users to get best data management. Numerous organizations are seeking online data solution according to industry standards at cost effective rates. Today due to availability of so many service providers it is now much easy and flexible to outsource requirements.

Outsourcing can help insurance companies, medical firms, telecom companies, airline companies to maintain their data. In the past all data stored on paper and kept in backyards. So to identify any previous record was always a difficult task but today there are so many technical tools are available to store data in to electronic formats. Data conversion is also a part of data typing that used to convert one file format in to other one.

Let us check benefits of data entry outsourcing as per industry standards:

• No need to develop own infrastructure resources
• Accurate results at less investments
• Awareness with outsourcing BPO world
• Understanding about how to outsource requirements
• Access for industry standard tools and techniques
• Working experience with professionals

So it is always a wise step to outsource your requirements instead of investing larger amount for infrastructure development.


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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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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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PDF Scraping: Making Modern File Formats More Accessible

Data scraping is the process of automatically sorting through information contained on the internet inside html, PDF or other documents and collecting relevant information to into databases and spreadsheets for later retrieval. On most websites, the text is easily and accessibly written in the source code but an increasing number of businesses are using Adobe PDF format (Portable Document Format: A format which can be viewed by the free Adobe Acrobat software on almost any operating system. See below for a link.). The advantage of PDF format is that the document looks exactly the same no matter which computer you view it from making it ideal for business forms, specification sheets, etc.; the disadvantage is that the text is converted into an image from which you often cannot easily copy and paste. PDF Scraping is the process of data scraping information contained in PDF files. To PDF scrape a PDF document, you must employ a more diverse set of tools.

There are two main types of PDF files: those built from a text file and those built from an image (likely scanned in). Adobe's own software is capable of PDF scraping from text-based PDF files but special tools are needed for PDF scraping text from image-based PDF files. The primary tool for PDF scraping is the OCR program. OCR, or Optical Character Recognition, programs scan a document for small pictures that they can separate into letters. These pictures are then compared to actual letters and if matches are found, the letters are copied into a file. OCR programs can perform PDF scraping of image-based PDF files quite accurately but they are not perfect.

Once the OCR program or Adobe program has finished PDF scraping a document, you can search through the data to find the parts you are most interested in. This information can then be stored into your favorite database or spreadsheet program. Some PDF scraping programs can sort the data into databases and/or spreadsheets automatically making your job that much easier.

Quite often you will not find a PDF scraping program that will obtain exactly the data you want without customization. Surprisingly a search on Google only turned up one business, (the amusingly named ScrapeGoat.com http://www.ScrapeGoat.com) that will create a customized PDF scraping utility for your project. A handful of off the shelf utilities claim to be customizable, but seem to require a bit of programming knowledge and time commitment to use effectively. Obtaining the data yourself with one of these tools may be possible but will likely prove quite tedious and time consuming. It may be advisable to contract a company that specializes in PDF scraping to do it for you quickly and professionally.

Let's explore some real world examples of the uses of PDF scraping technology. A group at Cornell University wanted to improve a database of technical documents in PDF format by taking the old PDF file where the links and references were just images of text and changing the links and references into working clickable links thus making the database easy to navigate and cross-reference. They employed a PDF scraping utility to deconstruct the PDF files and figure out where the links were. They then could create a simple script to re-create the PDF files with working links replacing the old text image.

A computer hardware vendor wanted to display specifications data for his hardware on his website. He hired a company to perform PDF scraping of the hardware documentation on the manufacturers' website and save the PDF scraped data into a database he could use to update his webpage automatically.

PDF Scraping is just collecting information that is available on the public internet. PDF Scraping does not violate copyright laws.

PDF Scraping is a great new technology that can significantly reduce your workload if it involves retrieving information from PDF files. Applications exist that can help you with smaller, easier PDF Scraping projects but companies exist that will create custom applications for larger or more intricate PDF Scraping jobs.


Source: http://ezinearticles.com/?PDF-Scraping:-Making-Modern-File-Formats-More-Accessible&id=193321

Data Mining And Importance to Achieve Competitive Edge in Business

What is data mining? And why it is so much importance in business? These are simple yet complicated questions to be answered, below is brief information to help understanding data and web mining services.

Mining of data in general terms can be elaborated as retrieving useful information or knowledge for further process of analyzing from various perspectives and summarizing in valuable information to be used for increasing revenue, cut cost, to gather competitive information on business or product. And data abstraction finds a great importance in business world as it help business to harness the power of accurate information thus providing competitive edge in business. May business firms and companies have their own warehouse to help them collect, organize and mine information such as transactional data, purchase data etc.

But to have a mining services and warehouse at premises is not affordable and not very cost effective to solution for reliable information solutions. But as if taking out of information is the need for every business now days. Many companies are providing accurate and effective data and web data mining solutions at reasonable price.

Outsourcing information abstraction services are offered at affordable rates and it is available for wide range of data mine solutions:

• taking out business data
• service to gather data sets
• digging information of datasets
• Website data mining
• stock market information
• Statistical information
• Information classification
• Information regression
• Structured data analysis
• Online mining of data to gather product details
• to gather prices
• to gather product specifications
• to gather images

Outsource web mining solutions and data gathering solutions has been effective in terms of cost cutting, increasing productivity at affordable rates. Benefits of data mining services include:

• clear customer, service or product understanding
• less or minimal marketing cost
• exact information on sales, transactions
• detection of beneficial patterns
• minimizing risk and increased ROI
• new market detection
• Understanding clear business problems and goals

Accurate data mining solutions could prove to be an effective way to cut down cost by concentrating on right place.


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