Benefits of Predictive Analytics and Data Mining Services

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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Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.

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Facts on Data Mining

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.

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Resume Extraction: To Grab Best Candidate

Resume Extraction: To Grab Best Candidate

Selecting the eligible and potential employee for the organization is the most significant task of any company. Success rate of any company totally depends on the assortment of talented and experienced candidates. Quality is of prime significance than quantity and for this, having the best resume analyzer is a good idea. The tasks related to recruitment should be performed well by the HR department.

Examination of a perfectly apt candidate is the main concern of the qualitative resume software. A number of myriad aspects are considered for the resume assessment. There posses a competition of various talents that candidate possesses. Before recruitment of any applicant, his job analysis is performed by the HR department. For this purpose performing resume extraction becomes essential and resume analyzer is the medium to do so.

Proficient software performs a helpful task at job portals. The resume analyzer parses all the resumes and filters them on the basis of presence of keyword. It facilitates to match the particular keyword with every available resume. Presence of keywords indicates that the candidate is short listed while absence refers rejection. As these days everyone needs fast results performing resume extraction becomes essential to save time and money.

Resume analyzer helps in accepting and rejecting the resume of the candidates. It position or rank the candidates in to a list, this criteria is based on the presence of the keywords and the required apt information about the candidate. Resume software implements the standard policies for formatting the process of resume extraction and uploads this important data into your available database. This data is available in the text format. Essential information like name, qualifications, contact details, certifications, last work experience etc present in resume is uploaded into the database.

This information is used to match the criteria of the required job post. Ranking of the candidates helps to opt for the most suitable and skilled candidate among the list of thousands.

Resume extraction is one of the essential aspects to sort out the potential candidate.

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Data Mining: Its Description and Uses

Data Mining: Its Description and Uses

Data mining also known as the process of analyzing the KDD which stands for Knowledge Discovery in Databases is a part of statistics and computer science. It is a process which aims to find out many various patterns in enormous sets of relational data.

It uses ways at the fields of machine learning, database systems, artificial intelligence, and statistics. It permits users to examine data from many various perspectives, sort it, and summarize the identified relationships.

In general, the objective of data mining process is to obtain info out of a data set and convert it into a comprehensible outline. Also, it includes the following: data processing, data management and database aspects, visualization, complexity considerations, online updating, inference and model considerations, and interestingness metrics.

On the other hand, the actual data mining assignment is the semi-automatic or automatic exploration of huge quantities of information to extract patterns that are interesting and previously unknown. Such patterns can be the unusual records or the anomaly detection, data records groups or the cluster analysis, and the dependencies or the association rule mining. Usually, this involves utilizing database methods like spatial indexes. Such patters could be perceived as a type of summary of input data, and could be used in further examination or, for example, in predictive analysis and machine learning.

Today, data mining is utilized by different consumer-focused companies like those in the financial, retails, marketing, and communications fields. It permits such companies to find out relationships among the internal aspects like staff skills, price, product positioning, and external aspects like customer information, competition, and economic indicators. Additionally, it allows them to define the effect on corporate profits, sales, and customer satisfaction; and dig into the summary information to be able to see transactional data in detail.

With data mining process, a retailer can make use of point-of-scale customer purchases records to send promotions based on the purchase history of a client. The retailer can improve products and campaigns or promotions that can be appealing to a definite customer group by using mining data from comment cards.

Generally, any of the following relationships are obtained.

1. Associations: Data could be mined to recognize associations.
2. Clusters: Data are sorted based on a rational relationships or consumer preferences.
3. Sequential Patters: Data is mined to expect patterns and trends in behavior.
4. Classes: Data that are stored are utilized to trace data in predetermined segments.

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How Data Mining is Useful to Companies?

How Data Mining is Useful to Companies?

Every business, organization and government bodies are collecting large amount of data for research and development. Such huge database can make them to have the information on hand when required. But most important is that it takes much time to find important information from the data. "If you want to grow rapidly, you must take quick and accurate decisions to grab timely available opportunities."

By applying the process of data mining, you can easily extract and filter required information from data. It is a processing of refining data and extracting important information. This process is mainly divided into 3 sections; pre-processing, mining and validation. In pre-processing, large amount of relevant data are collected. The mining section includes data classification, clustering, error correction and linking information. The last but important is validate without which you can not make trust on information. In short, data mining is a process of converting data into authentic information.

Let's have look on how data mining is useful to companies.

Fast and Feasible Decisions: To search information from huge bundle of data require more time. It also irritates a person who is doing such. With annoyed mind one can not take accurate decisions that's for sure. By having help of data mining, one can easily get information and make fast decisions. It also helps to compare information with various factors so the decisions become more reliable. Data mining is helpful in every decision to make it quick and feasible.

Powerful Strategies: After data mining, information becomes precise and easy to understand. While making strategies, one can easily analyze information in various dimensions. This analysis helps to get real idea about the strategy implementation. Management bodies can implement powerful strategies effectively to expand business boundaries.

Competitive Advantage: Information is easily available and precise so that one can compare it with competitors' information. It is very much required that you must compare the data otherwise you will have to suffer in business. After doing competitive analysis, one can make corrective decisions to go ahead from competitors. This way company can gain competitive advantage.

Your business can get all the benefits of data mining at cutting rates through outsourcing.

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Data Scrapping

Data Scrapping

People who are involved in business activities might have came across a term Data Scrapping. It is a process in which data or information can be extracted from the Portable Document Format file. They are easy to use tools that can automatically arrange the data that are found in different format in the internet. These advanced tools can collect useful information's according to the need of the user. What the user needs to do is simply enter the key words or phrases and the tool will extract all the related information available from the Portable Document Format file. It is widely used to take information's from the no editable format.

The main advantage of Portable Document Format files are they protect the originality of the document when you convert the data from Word to PDF. The size of the file is reduced by compression algorithems when the file are heavier due to the graphics or the images in the content. A Portable Document Format is independent of any software or hardware for installation. It allows encryption of files which enhances the security of your contents.

Although the Portable Document Format files have many advantages,it too have many other challenges. For example, you want to access a data that you found on the internet and the author encrypted the file preventing you from printing the file, you can easily do the scrapping process. These functions are easily available on the internet and the user can choose according to their needs. Using these programs you can extract the data that u need.

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