Why Common Measures Taken To Prevent Scraping Aren't Effective

Bots became more powerful in 2014. As the war continues, let’s take a closer look at why common strategies to prevent scraping didn’t pay off.

With the market for online businesses expanding rapidly, the development teams behind these online portals are under great amounts of pressure to keep up in the race. Scalability, availability and responsiveness are some of the commonly faced problems for a growing online business portal. As the value of content is increasing, content theft has become an increasing problem in the form of web scraping.

Competitors have learned to stay ahead of the race by using bots to scrape. While how these bots could be harmful is something worth talking about, it is not the main scope of this article. This article discusses some of the commonly used weapons to fight bots and brings to light their effectiveness in reality.

We come across many developers who claim to have taken measures to prevent their sites from being scraped. A common belief is that these below listed techniques reduce scraping activities significantly on a website. While some of these methods could actually work in concept, we were interested to explore how effective they were in practice.

Most Commonly used techniques to Prevent Scraping:

•    Setting up robots.txt – Surprisingly, this technique is used against malicious bots! Why this wouldn’t work is pretty straight forward – robots.txt is an agreement between websites and search engine bots to prevent search engine bots from accessing sensitive information. No malicious bot (or the scraper behind it) in it’s right mind would obey robots.txt. This is the most ineffective method to prevent scraping.

•    Filtering requests by User agent – The user agent string of a client is set by the client itself. One method is to obtain this from the HTTP header of a request. This way, a request can be filtered even before the content is served to the request. We observed that very few bots (approximately less than 10%), used the default user agent string which belonged to a scraping tool or was an empty string. Once their requests to the website were filtered based on the user agent, it didn’t take too long for scrapers to realize this and change their user agent to that of any well known browser. This method merely stops new bots written by inexperienced scrapers for a few hours.

•    Blacklisting the IP address – Seeking out to an IP blacklisting service is much easier than having to perform the hectic process of capturing more metrics from page requests and analyzing server logs. There are plenty of third party services which maintain a database of blacklisted IPs. In our hunt for a suitable blacklisting service, we found that using a third party DNSBL/RBL service was not effective as these services blacklisted only email spambot servers and were not effective in preventing scraping bots. Less than 2% of scraping bots were detected for one of our customer’s when we did a trial run.

•    Throwing CAPTCHA – A very well know practice to stop bots is to throw CAPTCHA on pages with sensitive content. Although effective against bots, CAPTCHA is thrown to all clients requesting the web page irrespective of whether it is a human or a bot. This method often antagonizes users and hence reduces traffic to the website. Some more insights to the new NO CAPTCHA Re-CAPTCHA by Google can be found in our previous blog post.

•    Honey pot or Honey trap – Honey pots are a brilliant trap mechanism to capture new bots (scrapers who are not well versed with structure of every page) on the website. But, this approach poses a lesser known threat of reducing the page rank on search engines. Here’s why – Search engine bots visit these links and might get trapped accidentally. Even if exceptions to the page were made by disallowing a set of known user agents, the links to the traps might be indexed by a search engine bot. These links are interpreted as dead, irrelevant or fake links by search engines. With more such traps, the ranking of the website decreases considerably. Furthermore, filtering requests based on user agent can exploited as discussed above. In short, honey pots are risky business which must be handled very carefully.

To summarize, these prevention strategies listed are either weak or require constant monitoring and regular maintenance to keep them effective. In practice bots are far more challenging than they actually seem to be.

What to expect in 2015?

With increasing need for scraping, the number of scraping tools and expert scrapers are also increasing which simply means bots are going to be an increasing problem. In fact, the usage of headless browsers i.e, browser like bots which are used to scrape are increasing and scrapers are no longer relying on wget, curl and html parsers. Preventing malicious bots from stealing content without actually disturbing the genuine traffic from humans and search engine bots is just going get harder. By the end of the year, we could infer from our database that almost half of an average website’s traffic is caused by bots. And a whopping 30-40% is caused by malicious bots. We believe this is only going to increase if we do not step up to take action!

p.s. If you think you are facing similar problems, why not request for more information? Also, if you do not have the time or bandwidth for taking such actions, scraping prevention and stopping malicious bots is something we provide as a service. How about a free trial?

Source:http://www.shieldsquare.com/why-common-measures-taken-to-prevent-scraping-arent-effective/

How You Can Identify Buying Preferences of Customers Using Data Mining Techniques

The New Gold Rush: Exploring the Untapped ‘Data Mining’ Reserves of Top 3 Industries

In a bid to reach new moms bang on time, Target knows when you’ll get pregnant. Microsoft knows Return on Investment (ROI) of each of its employee. Pandora knows what’s your current music mood. Amazing, isn’t it?

Call it the stereotype of mathematician nerds or Holy Grail of predictive analysts of modern day, Data Mining is the new gold rush for many industries.

Today, companies are mining data to predict exact actions of their prospective customers. That means, when a huge chunk of customer data is seen through a series of sophisticated, formatted and collective data mining process, it can help create future-ready content of marketing and buying messages, diminishing scope of errors and maximizing customer loyalty.

Also a progressive team of coders and statisticians help push the envelope as far as the marketing and business tactics are concerned by collecting data and mining practices that are empowering.

Mentioned below is a detailed low-down of three such industries (real estate, retail and automobile) where LoginWorks Software has employed the most talented predictive analysts and comprehensive behavioral marketing platforms in the industry. Let’s take a look.

Real Estate Industry Looks Past the Spray-And-Pray Marketing Tactic By Mining User Data.

A supremely competitive market that is to an extent unstructured too, the real estate industry needs to reap the advantageous benefits of data mining. And, we at LoginWorks Softwares understand this extremely well!

Our robust team of knowledge-driven analysts make sure that we predict future trends, process the old data and rank the areas using actionable predictive analytics techniques. By applying a long-term strategy to analyze the trend and to get hold of the influential factors that are invested in buying a property, our data warehouses excels in using classical techniques, such as Neural Network, C&R Tree, linear regression, Multilayer Perception Model and SPSS in order to uncover the hidden knowledge.

By using Big Data as the bedrock of our Predictive Marketing Platform, we help you zero-in on the best possible property available for your interest. Data from more than a dozen of reliable national and international resources to give you the most accurate and up-to-the minute data. Right from extracting a refined database of one’s neighbourhood insights to classic knowledge discovery of meaningful l techniques, our statisticians have proven accuracy. We scientifically predict your data by:

•    Understanding powerful insights that lead to property-buying decisions.
•    Studying properties and ranking them city-wise, based on their predictability of getting sold in the future.
•    Measuring trends at micro level by making use of Home Price Index, Market Strength Indicator, Automated Valuation Model and Investment analytics.

Our marketing platform consists of the mentioned below automated features:

Data Mining Techniques for Customer Relationship Management and Customer Retention in Retail Industry

Data mining to a retailer is what mining gold to a goldsmith would be! Priceless, to say the least. To understand the dynamics and suggestive patterns of customer habits, a retailer is always scouting for information to up his sales and generate future leads from existing and prospective consumers. Hence, sourcing your birth date information from your social media profiles to zooming upon your customer’s buying behaviour in different seasons.

For a retailer, data mining helps the customer information to transform a point of sale into a detailed understanding of (1) Customer Identification; (2) Customer Attraction; (3) Customer Retention; and (4) Customer Development. A retailer can score potential benefits by calculating Return on Investment (ROI) of its customers by:

•    Gaining customer loyalty and long-term association
•    Saving up on huge spend on non-targeted advertising and marketing costs
•    Accessing customer information, which leads to directly targeting the profitable customers
•    Extending product life cycle
•    Uncovering predictable buying patterns that leads to a decrease in spoilage, distribution costs and holding costs

Our specialised marketing team targets customers for retention by applying myriad levels of data mining techniques, in both technological and statistical perspective. We primarily make use of ‘basket’ analysis technique that unearths links between two distinct products and ‘visual’ mining techniques that helps in discovering the power of instant visual association and buying.

Role of Data Mining in Retail Sector

Spinning the Magic Wheel of Data Mining Algorithms in Automobile Industry of Today

Often called as the ‘industries of industries’. the automobile industry of today is robustly engrossed in constructing new plants, and extracting more production levels from existing plants. Like food manufacturing and drug companies, today, automakers are in an urgent need to build sophisticated data extraction processes to keep themselves all equipped for exuberantly expensive and reputation-damaging incidents. If a data analytics by Teradata Corp, a data analytics company, is to be believed then the “auto industry spends $45 billion to $50 billion a year on recalls and warranty claim”. A number potentially damaging for the automobile industry at-large, we reckon!

Hence, it becomes all the more imperative for an automobile company of repute to make use of enhanced methodology of data mining algorithms.

Our analysts would help you to spot insightful patterns, trends, rules, and relationships from scores and scores of information, which is otherwise next to impossible for the human eye to trace or process. Our avant-garde technicians understand that an automative manufacturing industry does not interact on one-to-one basis with the end consumers on a direct basis, hence we step into the picture and use our fully-integrated data mining feature to help you with the:

•    Supply chain procedure (pre-sales and post-sales services, inventory, orders, production plan).
•    Full A-Zee marketing facts and figures(dealers, business centers, social media handling, direct marketing tactics, etc).
•    Manufacturing detailing (car configurations/packages/options codes and description).
•    Customers’ inclination information (websites web-activities).

Impact of Big Data Analytics of Direct Vehicle Pricing

Bottom line

To wrap it all up, it is imperative to understand that the customer data is just as crucial for an actionable insights as your regular listings data. Behavioural data and predictive analysis is where the real deal lies, because at the end of the day it is all about targeting the right audience with the right context!

Move forward in your industry by availing LOGNWORKS SOFTWARES’ comprehensive, integrated, strategic and sophisticated Data Mining Services.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/can-identify-buying-preferences-customers-using-data-mining-techniques/

Living Your Dream Life When Others Are Scraping Their Windshields

One Internet marketer once remarked that he knew he was successful when he was awakened one early winter morning by the sound of his neighbors scraping the ice off of their windshields so that they could drive to work. As he burrowed deeper under his covers, he grinned to himself and realized that while he wasn't sipping cocktails aboard his own yacht yet, he had reached an enviable measure of success. If you're ready to begin living your dream life, then you're ready to become an Internet marketer.

What does living your dream life mean to you? Does it mean being able to be your own boss and not having to adhere to another's schedule? Does living your dream life mean taking vacations to exotic locations? The beauty of Internet marketing is that you can realize your goals and live the life you choose. You can do all the things you've dreamed of doing and go to the places you've dreamed of seeing. You answer to no one but yourself, although most Internet marketers learn that they still have a boss even after they quit their jobs. That new boss, actually bosses, are your customers and you'll have to do a great job in order to get them and keep them.

Most Internet marketers, the ones who truly have an entrepreneurial spirit, are naturals when it comes to pleasing customers. They want to earn their business, they care about their needs and their likes and dislikes and will go out on a limb to satisfy their customers. While customer service is severely lacking in many of today's businesses, without it, you'll be dead in the water before you even get started. If living your dream life is important to you, plan now to offer the kind of customer service that no one else can rival.

Imagine living your dream life for the rest of your life. Imagine being able to take your family to the kinds of places you've only seen in movies and read about in books. Imagine having money in the bank to tide you over when times are tough and having the peace of mind of knowing that your home is not at risk. You can have all of this and more if you are willing to work hard. The rewards for Internet marketers aren't instant, but they are sweet and they can be yours.

Source: http://ezinearticles.com/?Living-Your-Dream-Life-When-Others-Are-Scraping-Their-Windshields&id=2983939

How to Deal with Content Scrapers

There are few approaches that people take when dealing with content scrapers. The Do Nothing Approach, Kill them all approach, Take Advantage of them approach.

The Do Nothing Approach

This is by far the easiest approach you can take. Usually the most popular bloggers would recommend this because it takes A LOT of time fighting the scrapers. This approach simply recommends that “instead of fighting them, spend your time producing even more quality content and having fun”. Now obviously if it is a well-known blog like Smashing Magazine, CSS-Tricks, Problogger, or others, then they do not have to worry about it. They are authority sites in Google’s eyes.

However during the Panda Update, we know some good sites got flagged as scrapers because google thought their scrapers were original content. So this approach is not always the best in our opinion.

Kill them all Approach

The exact opposite of the “Do Nothing Approach”. In this approach, you simply contact the scraper and ask them to take the content down. If they refuse to do so or simply do not reply to your requests, then you file a DMCA (Digital Millennium Copyright Act) with their host. In our experience, majority of the scraping websites do not have a contact form available. If they do, then utilize it. If they do not have the contact form, then you need to do a Whois Lookup.

Whois Lookup

You can see the contact info on the administrative contact. Usually the administrative, and technical contact is the same. The whois also shows the domain registrar. Most well-known web hosting companies and domain registrars have DMCA forms or emails. You can see that this specific person is with Hostgator because of their nameservers. HostGator has a form for DMCA complaints. If the nameserver is something like ns1.theirdomain.com, then you have to dig deeper by doing reverse IP lookups and searching for IPs.

You can also use a third party service for DMCA.com for takedowns.

Jeff Starr in his article suggest that you should block the bad guy’s IPs. Access your logs for their IP address, and then block it with something like this in your root .htaccess file:

1    Deny from 123.456.789

You can also redirect them to a dummy feed by doing something like this:

1    RewriteCond %{REMOTE_ADDR} 123\.456\.789\.

2    RewriteRule .* http://dummyfeed.com/feed [R,L]

You can get really creative here as Jeff suggests. Send them to really large text feeds full with Lorem Ipsum. You can send them some disgusting images of bad things. You can also send them right back to their own server causing an infinite loop which will crash their site.

The last approach that we take is to take Advantage of them.

Source:http://www.wpbeginner.com/beginners-guide/beginners-guide-to-preventing-blog-content-scraping-in-wordpress/

Customer Relationship Management (CRM) Using Data Mining Services

In today's globalized marketplace Customer relationship management (CRM) is deemed as crucial business activity to compete efficiently and outdone the competition. CRM strategies heavily depend on how effectively you can use the customer information in meeting their needs and expectations which in turn leads to more profit.

Some basic questions include - what are their specific needs, how satisfied they are with your product or services, is there a scope of improvement in existing product/service and so on. For better CRM strategy you need a predictive data mining models fueled by right data and analysis. Let me give you a basic idea on how you can use Data mining for your CRM objective.

Basic process of CRM data mining includes:

1. Define business goal
2. Construct marketing database
3. Analyze data
4. Visualize a model
5. Explore model
6. Set up model & start monitoring

Let me explain last three steps in detail.

Visualize a Model:

Building a predictive data model is an iterative process. You may require 2-3 models in order to discover the one that best suit your business problem. In searching a right data model you may need to go back, do some changes or even change your problem statement.

In building a model you start with customer data for which the result is already known. For example, you may have to do a test mailing to discover how many people will reply to your mail. You then divide this information into two groups. On the first group, you predict your desired model and apply this on remaining data. Once you finish the estimation and testing process you are left with a model that best suits your business idea.

Explore Model:

Accuracy is the key in evaluating your outcomes. For example, predictive models acquired through data mining may be clubbed with the insights of domain experts and can be used in a large project that can serve to various kinds of people. The way data mining is used in an application is decided by the nature of customer interaction. In most cases either customer contacts you or you contact them.

Set up Model & Start Monitoring:

To analyze customer interactions you need to consider factors like who originated the contact, whether it was direct or social media campaign, brand awareness of your company, etc. Then you select a sample of users to be contacted by applying the model to your existing customer database. In case of advertising campaigns you match the profiles of potential users discovered by your model to the profile of the users your campaign will reach.

In either case, if the input data involves income, age and gender demography, but the model demands gender-to-income or age-to-income ratio then you need to transform your existing database accordingly.

For any queries related to Data mining CRM applications, please feel free to contact us. We would be pleased to answer each of your queries in detail.

Source:http://ezinearticles.com/?Customer-Relationship-Management-%28CRM%29-Using-Data-Mining-Services&id=4641198

Data Mining Is Useful for Business Application and Market Research Services

One day of data mining is an important tool in a market for modern business and market research to transform data into an information system advantage. Most companies in India that offers a complete solution and services for these services. The extraction or to provide companies with important information for analysis and research.

These services are primarily today by companies because the firm body search of all trade associations, retail, financial or market, the institute and the government needs a large amount of information for their development of market research. This service allows you to receive all types of information when needed. With this method, you simply remove your name and information filter.

This service is of great importance, because their applications to help businesses understand that it can perform actions and consumer buying trends and industry analysis, etc. There are business applications use these services:

1) Research Services
2) consumption behavior
3) E-commerce
4) Direct marketing
5) financial services and
6) customer relationship management, etc.

Benefits of Data mining services in Business

• Understand the customer need for better decision
• Generate more business
• Target the Relevant Market.
• Risk free outsourcing experience
• Provide data access to business analysts
• Help to minimize risk and improve ROI.
• Improve profitability by detect unusual pattern in sales, claims, transactions
• Major decrease in Direct Marketing expenses

Understanding the customer's need for a better fit to generate more business target market.To provide risk-free outsourcing experience data access for business analysts to minimize risk and improve return on investment.

The use of these services in the area to help ensure that the data more relevant to business applications. The different types of text mining such as mining, web mining, relational databases, data mining, graphics, audio and video industry, which all used in enterprise applications.

Source:http://ezinearticles.com/?Data-Mining-Is-Useful-for-Business-Application-and-Market-Research-Services&id=5123878

Data Extraction, Web Screen Scraping Tool, Mozenda Scraper

Web Scraping

Web scraping, also known as Web data extraction or Web harvesting, is a software method of extracting data from websites. Web scraping is closely related and similar to Web indexing, which indexes Web content. Web indexing is the method used by most search engines. The difference with Web scraping is that it focuses more on the translation of unstructured content on the Web, characteristically in rich text format like that of HTML, into controlled data that can be analyzed stored and in a spreadsheet or database. Web scraping also makes Web browsing more efficient and productive for users. For example, Web scraping automates weather data monitoring, online price comparison, and website change recognition and data integration. 

This clever method that uses specially coded software programs is also used by public agencies. Government operations and Law enforcement authorities use data scrape methods to develop information files useful against crime and evaluation of criminal behaviors. Medical industry researchers get the benefit and use of Web scraping to gather up data and analyze statistics concerning diseases such as AIDS and the most recent strain of influenza like the recent swine flu H1N1 epidemic.

Data scraping is an automatic task performed by a software program that extracts data output from another program, one that is more individual friendly. Data scraping is a helpful device for programmers who have to generate a line through a legacy system when it is no longer reachable with up to date hardware. The data generated with the use of data scraping takes information from something that was planned for use by an end user.

One of the top providers of Web Scraping software, Mozenda, is a Software as a Service company that provides many kinds of users the ability to affordably and simply extract and administer web data. Using Mozenda, individuals will be able to set up agents that regularly extract data then store this data and finally publish the data to numerous locations. Once data is in the Mozenda system, individuals may format and repurpose data and use it in other applications or just use it as intelligence. All data in the Mozenda system is safe and sound and is hosted in a class A data warehouses and may be accessed by users over the internet safely through the Mozenda Web Console.

One other comparative software is called the Djuggler. The Djuggler is used for creating web scrapers and harvesting competitive intelligence and marketing data sought out on the web. With Dijuggles, scripts from a Web scraper may be stored in a format ready for quick use. The adaptable actions supported by the Djuggler software allows for data extraction from all kinds of webpages including dynamic AJAX, pages tucked behind a login, complicated unstructured HTML pages, and much more. This software can also export the information to a variety of formats including Excel and other database programs.

Web scraping software is a ground-breaking device that makes gathering a large amount of information fairly trouble free. The program has many implications for any person or companies who have the need to search for comparable information from a variety of places on the web and place the data into a usable context. This method of finding widespread data in a short amount of time is relatively easy and very cost effective. Web scraping software is used every day for business applications, in the medical industry, for meteorology purposes, law enforcement, and government agencies.

Source:http://www.articlesbase.com/databases-articles/data-extraction-web-screen-scraping-tool-mozenda-scraper-3568330.html