There is a danger here, the easy use of GUI tools on large amount of data available is tempting and which makes users tempt to use black box methodologies available in the tools to solve their business problems. They mistakingly assume data mining is all about using a tool and running the data on the tool, this can be very hazardous and dangerous as actions are taken on business decisions.

Little knowledge is dangerous while applying powerful models.

Since the underlying logic used for model building in automated tools are unknown (their internal assumptions are unclear). Therefore it is very easy to do it in the wrong way assuming it is the right solution. Such mistakes are still made in companies.

People sometimes might argue that there is always a simpler solution to problems. I am the person who belives in Occam’s Razor, which means simplest explanation is always the best way. This means the interpretation of the model and the final solution should be as simple as possible. However,  method of arriving at solution must be as detail as possible taking every factor, element into consideration. Analysts must not make the mistake of over simplyfying the method and make too many assumptions.

A lot of knowledge workers believe that predictive modeling process can be automated by using tools and statistical software. These software are certainly useful but cannot replace the intellectual  of the analyst.

The processof building the model must be as detailed as possible, it has to tried & tested on various data and measured by various parameters. All possible factors must be considered while building the model.

Therefore Analyst must understand the underlying algorithm , process of arriving score, model design etc.

More on such articles regarding online marketing, data mining, data modeling can be found at http://www.praveenkodur.com/blog

Even a using a tool as simple as a spreadsheet will be extremely helpful in providing a mental representation of business situation. Most commonly used statistical techniques can be implemented in a spreadsheet as simple as MS EXCEL.

There are some very important techniques to business intelligence analysis. Most importantly defining the objective and performance indicators, these are metrics that are used to estimate performance of an object (entity). The next is developing mathematical relationships between variables and metrics through finding patterns. The last is What-If analysis is by determining variations in the output metric by changing the input variables.

The advantage of using mathematical models is beyond increasing performance and ROI. It helps knowledge workers in deeper analysis of the business and underlying product/domain. This will increase awareness in the company, knowledge transfer within the company,  and higher desire to learn better things. It encourages intellectual thinking within the company and promote people with good analytical skills who can offer great value to the company.\

There are many techniques like regression and classification, which are some of the popular mathematical models,however predictive analytics are not limited to these methods.

Regression:      Linear Regression, kNN, CART, Neural Net

Classification: Logistic Regression, Bayesian Methods, Discriminant Analysis, Neural Net, kNN, CART.

There are some limitations and advantages of each of the methods. The right model and right mathematical technique to be choosen for each problem. The underlining business value that needs to be increased with each of the techniques.

Best techniques are formulates after and testing and evaluating each approach and measuring the impact of success or performance.  All mathematical models use simple statistical techniques, however the value is in mapping the business problem into a mathematical problem, this requires some intellectual talent.

Models developed can be extremely useful in business critical process like sales, marketing and product.

More on such topics will be published on http://www.praveenkodur.com/blog/

Decisions are taken by knowledge workers at all levels of the company. Most of the knowledge workers generally adopt decision making style based on experience, gut feel, instinctive & spontaneous techniques. Such methods soon become predictable & stagnant. In a long run it does not prove useful in rapidly changing economic, technology & business environment. In rapidly changing technological environment, process become increasingly complex,

To stay competitive in a dynamic business environment requires a decision making style which is supported by knowledge driven by information & data. Analytics can provide a strategic value in providing competitive advantage to the companies.

For example, Internet company observes a lot of customer attrition on their website. These users may switch to a competitive website. The company is looking to reduce this churn by offering a discount or a trial. To increase the effectiveness of campaign is to recognize those customers and estimate their probability to discontinue the service. From a given list of customers we can a implement a model to estimate the probabilities and arrive at a smaller subset of users to be targeted for the campaign. This smaller set of users will be the majority of the future discontinued users.

Such tools and methodologies support knowledge workers in making better decisions. Generally several options are available to users before making a decision. This actually helps them choose the best methodology.

Often there is a enormous amount of effort is required to extract knowledge from data. Data is present in most raw format almost incomprehensible. Data needs to be transferred to human readable format and then has to be processed (sometimes tortured) to extract information and then to knowledge to make it useful for knowledge workers.

More articles in this blog http://www.praveenkodur.com/blog will talk in detail about BI systems.

Extracting knowledge from data requires deployment of various systems, process and maintenance. Installing Transaction servers to maintaining data warehousing systems to creating decision support systems. The idea of a Decision support system is to promote a scientific and rational approach to management.

Business intelligence architecture must be designed to function efficiently with cost effective components. There are various components such as logging systems, data warehouse, BI methodologies.

Focus for many companies such as Banks, Insurance companies, Telecommunication companies is to track customer end to end, from the time money spent on the customer acquisition to the time revenue is generated from the consumer for product/service until the customer attrites from the firm. Every activity in the chain must be closely observed to evaluate the true value of customer.  Companies worldwide are creating process to deal with customers  individually.  This can help them devout more attention to customers who are more valuable to the business and let go customers not valuable to business.

Data Mining requires a lot of effort/techniques and focus to centre their business around the customer than a product. Companies have to constantly keep a watch on what their customers are doing, keep in mind their past actions, discover knowledge from their actions (gain knowledge)  and finally use the knowledge cleverly to make decisions to make profit.

However data mining is not always beneficial for the user, consider the fact a model recommends a service to a user instead of the product A. however if the business makes more profit on product and very negligible amount on service. Model recommendations may not be implemented. However it helps in understanding such customers.

Good question to ask is how can a consumer company with large base of customers can individually deal with each consumer. This can be accomplished intelligently by deploying effective technology solutions which is customisable based on data mining models and techniques. Nowadays the customer is the data entry operator who enters data into the system at various points and these are captured by the system.  Consider your bank, your touch points are ATM, Bank Branch, customer care who responds on phone, mail, hard mail & lastly your account /loan/credit card that you hold of the bank. Each transaction records your behaviour which can be an additional knowledge that bank is made aware of, this knowledge can be used by the bank to learn more about you and customise the next interaction or next touch point instance with you.

The transaction system records every instance of transaction of the user enabling the bank to analyse the nature of every transaction and update your profile with rich insights. The knowledge discovery doesnt end here. It needs support of data warehousing system along with extremely good data mining models to be able to take action / make decisions and deal with each customer. More on this will be written in more detail in coming articles.

The blog  http://www.praveenkodur.com/blog/ will be updated with more such analytics, online marketing articles.

Most website users have already used Google Analytics as online visitor reporting tool, however new features like Custom reports / Advance segmentation has upgraded it to an analysis tool. With Google Analytics you can achieve detailed analysis to make well informed decisions for online marketing & Internet business in general.

Some of the new features in Google Analytics are:

Advanced Segmentation: This is a most important feature to segment GA data as per your criteria.

a. Define segments based on any Site metrics, Traffic, Content, E-Commerce or Systems parameters available.
b. Apply these advanced segments on any report to view slice of data as per your criteria.
c. Apply more than one segment, along with overall visits for comparison.
d. Use predefined segments available by default.
e. Understand behavior of users for each segment and differentiate your action for each of them.
f. This feature along with regular expression can identify key pain points & opportunities that can make huge difference to your website.
g. E.g Segment1: Visitors landing on home page and bouncing from site.
h. E.g Segment2: Visitors landing on inner pages.

Custom Reports: This is a user defined report, where you can choose to identify elements in rows & columns in a data table.

a. Choose desired dimension parameters in rows and your desired metrics in columns.
b. Select other sub-dimensions for drilling down the reports. You can select drill down parameter to three levels.
c. Sub dimension chosen will be a slice of data from top level dimension. Since you have the ability to choose any dimensions for both levels, you can get absolutely wonderful segments.
d. E.g : First level dimension can be Geography, drill down to Top Landing pages & Network location.
e. E.g: First level can be Count of Visits, drill down to Traffic Sources, Top landing pages.
f. These reports can be saved, exported, sent to email and viewed anytime online.

Motion charts: Great Visualizing techniques like motion charts are available for users to pictorially view metrics over a time period. You can choose other site metrics as bubble sizes with different colors.

a. You need give Google motion chart API code access to your GA account.

Data Export API: Use Google Analytics API to import data from Google Servers. This is very useful as you can store GA information in your database or use it third party reporting software.

a. Export Data on Dimensions & metrics which can be used in combination for a custom report.
b. Common calculation such as formula for Bounce rate (bounces/entrances), Exit rate (exits/pageviews) and more can be found here http://code.google.com/apis/analytics/docs/gdata/gdataReferenceCommonCalculations.html

Flash & Dynamic Page tracking: Use ga.js new code for tracking dynamic pages with _trackpageview function.

a. Track Videos & various actions like pause, play, stop etc.
b. View Developer Docs present at http://code.google.com/apis/analytics/docs/

User Defined functions: You can configure Google Analytics to store user attributes like Age, Income, demographic, psychographic attributes. The website needs to capture these attributes in first place and using the setvar function in javascript code, you can pass these values to Google Servers. Setvar function overwrites new values for same cookie, to avoid this there is another function called supersetvar, which can be used to append future values to existing ones.

a. You could slice,dice, segment data on Age, Income & custom defined values.
b. View the list of functions which can be used to customized data representation in GA. http://code.google.com/apis/analytics/docs/gaJS/gaJSApi.html

Internal Site Search: Configuring your GA for site search can be done by enabling in main settings panel. You also have to give the parameter which stores the search keywords in URLs.

a. Search Exits, Bounce Rate, User behavior during search, post search and pre-search.

Ecommerce Tracking: You can use GA to store your e-commerce transactions. This is done by enabling e-commerce settings. You also need to place additional code in Thank You! Page to capture the elements of transaction such as price, product name, quantity etc.

a. You can find more info here about installing the Ecommerce tracking. You can even track 3rd party shopping transaction. (can be done by _linkByPost function) http://www.google.com/support/googleanalytics/bin/answer.py?hl=en&answer=55528 http://www.google.com/support/analytics/bin/topic.py?hl=en&topic=11001

Adwords Integration: Integrating Adwords keyword, impression, cost, cpc, clicks metrics on GA will be extremely useful as you can segment, slice, dice cost, cpc, impression via Geography, content, landing pages, visitor behavior, loyalty etc.

a. Adwords optimization is done based on conversion, however using GA we would be able to identify various segments of users such as Visitors doing research, Visitors browsing FAQs, Visitors commenting, browsing etc. Therefore we would be able to attach value to each segment and hence can better optimize Adwords campaigns based on different actions done by users rather than just conversion.

Coming articles will contain more information on GA regular expressions, using Goals, filters and advance implementation on pageviews etc.

This Article is originally available only in http://www.praveenkodur.com/blog/

Some of the common uses of analytical models are:

Acquiring new customers: Acquiring new customers is generally a big cost for the company, with increase in prices on various acquisition channels, companies find it hard to reduce cost in acquiring good profitable customers. Predictive modeling to certain extent becomes very useful in reducing costs in acquiring right customers and increase profitability. It also helps in designing marketing offers, special campaigns for customers to reach out to them.

Predictive models use historical data of customer attributes to understand relationships between attributes and their specific response or behavior. The output of predictive model is generally to predict a future response of the customer with their present data. These models are generally used to rank a list of prospective customers on the likelihood of their predicted response. This is very useful as it helps take present decisions. Other factors can also be fit into the response such as risk of acquiring customers like credit risk, or the cost of retaining customer. We can also predict response to each specific product, which helps in better targeting the customer and increases chances of acquiring them or even for cross selling products

(http://www.stochasticsolutions.com/pdf/CrossSell.pdf).

Additionally, there might be situations where it might not be easy to connect both customer and the response desired. Additionally there may be situations where various responses of customers are also valuable. For example, a customer might purchase a product after visiting the site 4 times, each time the customer performs a action like search, sending an enquiry, contacting user, reading knowledge material etc. Each action may be valuable to business therefore need to included in the model. In all such cases we need to use proxies to understand purchase behavior. Model building is a tedious task, but very worthy effort in increasing profitability.

In process there are additional outputs generated like customer profiling, customer segmentation, clustering and affinity pairs which is very valuable in developing products for customers.

http://en.wikipedia.org/wiki/Predictive_analytics

Customers Retention: The second problem is of retaining existing customers, companies are willing to provide offers to retain customers. The data mining problem will translate into finding the customers at risk (or customers who are looking to switch) and additionally identify those customers who are more likely to change behaviour due the marketing offer given by the company. (http://www.stochasticsolutions.com/pdf/FinanceRetention.pdf).

The data useful will be behavior of customers before attrition for certain time duration along with their demographics, attitudinal & behavioral patterns.

Predicting customer attrition rate is separate from predicting their behavioral change because of promotional offer. These models are extremely useful where markets are saturated and acquiring new customers becomes increasingly difficult.

Marketing techniques for retaining customer could backfire sometimes, resulting in loss of customer due to persuation from the company. There are also certain customers who would attrite irrespective of any marketing offered to them. Capturing this behavioral difference can be done through Control Groups, Test Groups & Hold Groups. This method is called as Differential Response modeling or Incremental impact model or Uplift model or Net model. Here is more info on the same: http://en.wikipedia.org/wiki/Uplift_modelling, and few white papers on the same http://www.stochasticsolutions.com/pdf/SavedAndDrivenAway.pdf. Customers in Control Group are randomly targeted, Customers in Test Groups are targeted based on model, Hold Group Customers are not considered for targeting of offer. Results of test and experiment is used in building the Net Model.

The industries where these techniques will be highly useful are Financial Services, Retail, Telecom, Internet Companies and Software Houses.

Do check back for more articles on this topic.

List of resources to find more information about Analytics

1) http://www.destinationcrm.com/Articles/CRM-News/Daily-News/Predictive-Analytics-Can-Pinpoint-Profitable-Customers-52164.aspx

2) http://scientificmarketer.com/search/label/response

3) http://www.redclaymedia.com/response_modeling.php

4) http://stochasticsolutions.com/retention.html

5) http://www.information-management.com/specialreports/2008_62/10000747-1.html?ET=dmreview:e323:1015879a:&st=email

6) http://www.information-management.com/issues/2007_52/10001990-1.html

7) http://www.predictiveanalyticsinsight.com/articles/callcenter.htm

http://www.predictiveanalyticsworld.com/predictive_analytics.php

9) http://www.marketingprofs.com/4/shearer1.asp

10) http://semphonic.blogs.com/semangel/2009/01/predictive-analytics-getting-a-legup-on-where-analytics-is-headed-.html

The Original article is present on http://www.praveenkodur.com/blog

It is very essential for a financial company to know, which user to target for what product and when. The goal of data analytics and data mining department is to arrive at accurate answers to these 3 questions, (Which User, What Product, When), trust me it is a rewarding exercise for the bank, users and definitely the analytics manager.

The problem is very simple. A customer may have purchased a single product or multiple products from a bank. The company would ideally like if all its customers bought all of its products. This possibility however does not exist, therefore without any targeting model; the company would be wasting money, time and resources by randomly targeting customers for its products.

Using a little sophistication of data mining techniques, we would be able to build a targeting model or a response model on customer base. A response model is a predictive model which estimates the future behavior of a given customer even before it occurs. Even with a fair amount of accuracy it can be very useful for company in saving majority of costs in various departments/channels. There are various types of data available for analysis like demographic, behavioral, psychographic / attitudinal. The above data can be very useful in data mining methods, however the cost, usefulness and expertise to store / retrieve data has to be assessed and judged.

Giving an example: A customer has savings account in a bank, based on the information given to the bank along with account status and history, we could target her as a prospect for a personal loan product based on a logic / algorithm.

The logic / algorithm could be as simple as if a customer from metro with account balance of Rs 1,00,000 or more for over 6 months should be a prospect for home loans Rs 20,00,000 or personal loans over Rs 3,00,000. This is a simple example; however it could be a complicated neural network or logistic regression model.

Cross Sell Models: A customer of a product A, is a likely prospect for product B if his account activity positively predicts likelihood of purchase. Target model has to predict purchase of product B.

Up Sell Models: A customer can be targeted for higher segment product in the same category based on his account status and demographics. Normal saving accounts customers can be sold for Premium segments customers, if his account balance is over a period of time.

Deep Sell Models: A customer can be sold more of same product based on his need. Target model has to predict his need.

These target models are also called response models that only predict the purchase behavior of a customer for a given product. This model is ineffective as the cost of randomly targeting a customer for product is LESS THAN cost of targeting using predictive response model.

The main reason is because of below limitations of traditional response model

A) All customers behave in two ways: Purchase or DO NOT Purchase

B) Behavior of customers during absence of any marketing activity.

C) Maximum lifetime value to be derived from customer is unkown, this will result in underselling or selling lower value products when higher products could have been sold.

IF these issues are taken care of by strong statistical modeling then loss making response model can be made profitable.

The original article was found on http://www.praveenkodur.com/, Copy rights are reserved.

Disclaimer: These are my personal views and not a view of any organization or body.

If a company has 1 million keywords in PPC account and is managed by 5 people then 2 Lakh keywords are managed per person!!.

This is a wrong strategy for growth. Google is spending billions of dollars to make life easier for advertiser, they are launching new features & tools regularly which gives more insights into the PPC accounts. There are lots of other companies which have launched various products around PPC management such as Betterppc’s Ad optimization, KeywordSpy, Compete Search Analytics, Shoemoney Tools and many more.

These tools along with the Google tools can greatly help reduce the PPC management process, time, and effort, increasing efficiency. Bigger accounts do not need as many people to manage. One single person can do the job of 20 people effectively, if the tools are used effectively.

PPC Management is like looking at stock market. A good investor is always looking at price of the stocks he has invested or stocker broker is looking at price of the all companies on regular basis. The difference is that in PPC you are spending money every day, while in stock you just buy once.

There are software’s available for keyword research, duplicate keyword identification, Keyword structuring many more.

With advance skills in Microsoft Excel along with the help of these tools can make life a lot simpler and easy with less work and less resources.

Google has launched Ad planer, upgraded the Google keyword tool by displaying numbers instead of relative graphs. The amount of time required to create a media plan or to come up with right set of keywords for PPC campaigns has been greatly reduced. Additionally Google has create a campaign library which contains a list of template which can be used to reproduce campaigns of the given order.

The ammunition and tools which Google offers now and in future will help make PPC Management easy, effective and less time consuming.

PPC managment requires daily changes to bids and/or keywords. But the point is let Google worry about making things easy for you in terms of managing PPC campaigns.

The Copyright of this Article is reserved by http://www.praveenkodur.com/

Optimization Tips from Adwords Team

Adwords 101 : Part 1

Adwords Optimization Part 1

Adwords Optimization – Assesing your Website

Adwords Optimization – Account Structure

Adwords Optimization – Keyword Basics

Adwords Optimization – Beyond Keyword Basics

Adwords Optimization – Ad Text Optimization

Generating New Keywords

Managing and Optimizing Bids

Landing Page quality Update

Website Optimizer Strategy

ROI Matters – Part 1 of 3

ROI Matters – Part 2 of 3

ROI Matters – Part 3 of 3

Google’s Help on Dynamic Keyword Insertion , is a must read for large advertisers

Other Adwords optimization resources shared by various advertisers around the world..

A) 32 Very Good Useful Tips on Adwords : This is list of Top tips put in one document. 13 calls to action verbiage ideas: Browse our store, Submit Your RFP (RFI), See our client list, Free Shipping, Order today, ship tonight. Download our brochure, Free Consultation, Free demo, Free quote, Free Report, Start now, Call 24/7.

B) Google’s Expanded Broad Match : Find out What is Google’s Expanded Broad Match, why google displays ads after remembering previous searches, how to test it?

C) Protecting from Expanded Broad Match : How to prevent from showing in expanded broad match.

D) Reaching Local Auidence : If you are a local business looking to reach local audience, then this can be helpful.

E) Using Video Ads in Adwords : Explaining the usage of Video Ads in Adwords Account.

F) Know about Placement Performance Report : The new feature Placement Performance Report greatly helps in getting insights into the account and performance in content network. Find out how to use it.

G) Search Query Performance Report : Search query reports generates keywords which have actually been searched on google to reach your website. This would help in generating negative keywords, additional potential traffic/profit keywords .

H) Adwords for Auto Sites/Forums : Find out how to manage adwords accounts in Auto Sites and Forums.

I) Top 10 Mistakes to Avoid in Adwords Account : What not to do & How not to manage your Adwords Accounts.

J) Tips to pay less and get more : Simple tips to optimize your account to spend less on adwords. You can also read the 5 Tips for Spending less on Adwords.

K) Adwords Expert Tips & Techniques: How new developments affect the adwords account management

L) Optimizing PPC Campaigns for B2B Businesses

M) Landing Page Optimization : 10 Tips you should never forget on optimizing landing pages.

N) Best way to generate PPC TRaffic and perform PPC Marketing

Small Changes to Ads which can boost your CTR

There are various small changes that can be made to adword Ad Text which can boost your CTR way above the competitors. You can find the exhaustive list on various blogs, Some of them are given below:

• Adding … in description Increases CTR: A test points out that adding 3 dots … at the end of the description line 2 will increase the CTR of the Ad.

• Plural & Singular : Small changes from singular to plural can boost your CTR

• Speaking to User as 1st Party : Using phrases like You Will Get can sometimes lead to higher CTR. Addressing the user directly as 1st person will help you boost CTR.

Few Adwords Tips for Beginners starting out on Advertising Online:

1. How to Get Started with Adwords
2. 8 Tips for Advertising Online :
3. Broad, Extact, Phrase Match :
4. Adwords Tip for Beginners :
5. Good Tips for PPC Starters:

You must watch the 2 hour Video on Perry Marshall Talking About Adwords.

Read a comprehensive description on how to test A/B testing of Text Ads, Landing Pages with Adwords.

These resources & knowledge on Adwords may not be present in the Google Adwords Learning Center

For Appearing for Google Advertising Professional Exam you could visit the Adwords learning center http://www.google.com/adwords/learningcenter/

This is my personal list of bookmarks on Adwords. However do keep a looking for more postings on Adwords Tips.

Copyright of Advanced Adwords Learning Center is reserved by praveenkodur.com

Google, Yahoo has come a long way from the days it used ignore webmasters when Google started out to helping webmasters/site owners with optimizing websites. Google has created a special channel of communication with webmasters/site owners for helping them design search engine friendly websites to make information available to users easily.

Link Building

Google clarifies Link exchange is fine as long as it does not look excessive and spam. This means the link exchange schemes & programs deliberately increasing links in attempt to achieve ranking is bad. Buying and selling links is bad. Any marketing done excessively is bad for websites. The discussion on this on webmasterworld can be found over here. Google in its Webmaster guidelines had stated that any link exchanges schemes, buying, selling links are not good for website rankings. As per Google the links are editorial votes to webpages and have to given naturally to add value to visitor.

A) Google introduces Paid Link Reporting service, find mattcutts story over here and here. Link selling sites loose the ability to transfer PageRank.

B) Google Introduces No-Follow attribute

C) Link Building with Google Bookmarks

D) Link Baiting Advice by Google & by MattCutts

Search Engine Optimization

Search Engines are interested in providing rich, useful & relevant content to end users. This can happen only when site owners publish rich content & design a search engine friendly site.

Search engines would want to index all the content provided by site owners. It is therefore in their Business Interest to help webmasters provide more content to search engines. Search Engines therefore encourage webmasters to publish more content online both quality & quantity.

Website owners are looking to drive traffic from Search Engines by optimizing websites. Search Engines are looking to provide relevant, quality results to end users. Therefore it is a Win-Win solution for both. The only glitch is Spam.. Spam is defined as any activity where this equation is disturbed.

Search Engine spam is defined as any activity by website owner which is in the business interest of webmaster but not in business interest of Search Engine. However there is no such thing as “Webmaster Spam” when the site owner’s business interest is not protected by search engines.

SEO Tips by Google

• SEO Basics
• Google Sitemaps
• Using Robots Meta Tag
• All about Robots.txt
• Quality Guidelines for Webmasters
• Duplicate Content Caused by Url
• Understand Duplicate Content
• SEO for Flash Files
• Server Location and SEO
• Using Google Webmaster Central Tools
• New Robots Meta Tag check: X-Robots Tag and Unavailable_after Meta tag
• Canonical Urls
• Google Webmaster Help Center

Some of the active channels the information & Tools online are:

A) Google Webmaster Central

B) Google Webmaster Discussion Group

C) Google Sitemaps

SEO Tips by Yahoo

Yahoo.com also offers extensive help and awareness program for webmasters for optimizing websites for higher ranking on search engines. In some areas like Dynamic Url rewriting they offer more advanced help than Google.com

• Basic SEO on Yahoo
• Ranking Factors on Yahoo
• Yahoo Webmaster Guildelines
• Yahoo Site Explorer & Sitemaps
• How Yahoo Handles Redirection
• Reporting Duplicate content through Urls & Faqs on Dynamic Urls. WMW Discussions on dynamic urls in yahoo is very useful
• Removing Urls from Search Index
• Yahoo Store Help

Web Master Tools offered by Yahoo

1) Yahoo Site EXplorer

2) Yahoo Search Blog

This is an article which is worth bookmarking for all SEO, SEM Managers, as the resources listed has to be referred by everyone in this field.

The golden fact is everyone (Search Marketing world) is aware of it but very very few people have the patience to constantly observe & learn for maintaining their website rankings on Top of Search Engines.

Copyright of this Article is reserved by praveenkodur.com