Every process, business or company can be considered to contain a list of inputs, transformations / systems (working) and finally the output. Of course all of these operate in a set of external and internal conditions (called environment). This real world phenomena can possibly be represented in mathematical form in terms of flowchart & numbers.

Pictorial and mathematical representation will help in optimization and alignment to the business KPIs. Coming to KPIs, these are set of metrics which measure performance of the business. These are performance metrics that are tracked, measured & monitored on regular basis.

Mathematical models contains an additional loop (feedback loop) in normal flow chart, gets formed from output to system as a feedback of performance. This feedback contains information on what to improve and  other intelligence that is fed back into the system. The feedback can be however not necessarily limited to effectiveness and efficiency of the system. First is the ability of the system to meet the desired output/ objectives. Second is the relationship between input and output, measure of how good is the relationship.

More articles on analytics, online marketing is present on http://www.praveenkodur.com/blog/

Users must understand that all mathematical models will only help support a decision or help solve a problem. At the time of decision making a knowledge worker generally has one or more alternatives to solve the problem. A list of feasible solutions will need to be chalked out from all possible solutions. The best solution or optimum solution to be derived from the list of feasible solutions again based on criteria such as profitability, cost, response etc.

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

1) When the dependent variable contains missing values, simply eliminate the records.

2) Correctly Identify slices of data and Substitute with measure of central tendency like Median, Mean & Mode. Identifying the right slice is also important. You can group by various parameters and take a central tendency. Choose the one with highest bias (chi-square)

3) If the missing value forms a Normal distribution pattern, find the missing value by normal inverse function.

4) Treating the missing values as a dependent variable in a regression equation. Use the multiple linear regression function to impute the missing variable. You can try other methods instead of regression like classification, decision tree etc.

5) Use business logic to understand the missing values.

6) Check the data capturing process, there could a error present at source of data entry. Also it helps identify if the missing data points are at random or non-random. If it is random missing error then you can use simple imputations, however if it is non-random then you need advanced techniques to impute values. Also look at bias in the particular column, if the bias is significant then you need advanced techniques. If bias is minimal then you can proceed with simple imputation.

7) Identify the list of possible values for the missing data set. Try and replace each possible value and create different data sets and build the model. Calculate differences in accuracies and consistency based on different substitutes. This way you can even add variation of the values into the missing element and remove bias.

Use regression to determine the distribution of the values in place of missing values. Create a What-If scenario by imputing every range of value.

9) Do nothing remove missing values and duplicate records of sample data set to increase the size of the data set.

10) Measure similarlity of records like vectors. The similarity is the cosine function between records, and find similar records to the missing data values.

11) Use logistic regression to measure likelihood of observed or likelihood of missing. If value missing the output is 0, else 1. The rest of the variables (non-missing) act as independent variables. This does not predict anything but only a likelihood of finding the variable missing. Records with same probability or closest probability is considered similar and missing data is donated.

Multiple imputation generally yields better results but it requires high-end statistical software for computation. It becomes necessary to use the help of statistical software.

This article is originally found on Praveen Kodur http://www.praveenkodur.com/blog/.

Internet Banking is a different arena of web analytics, which require a totally different perspective and problem solving capabilities. The rest of the article will talk about some problems and solutions in such areas.

In traditional ecommerce sites, KPIs and key metrics are tracked by setting up page-tracking and Goals. Users are tracked through funnel process, like movement from one page to another page until the sales confirmation page. Usability and productivity of the page is evaluated by bounce rate, exit rate, conversion rate and click stream behavior on each of these pages. 

However, consider a case of Internet Banking, it is very interesting that such funnel navigation or bounce rate gives a very little information about its effectiveness, here is why? Lets say a user has a bank account and is using Internet Banking for transaction.

There are various things are tracked to understand his behavior, a person who wants to transfer funds from one account to another will do it even if it is a difficult process and does not fully understand, a user will try and figure out the ways to understand process and transfer funds online. Therefore the drop out rate in funnel navigation is very less, unlike ecommerce sites. Most users will inevitable complete the funnel and navigate all important pages to reach thank you page, even if they do face difficulties. They would invariably figure out how to do a transaction online.

The problem for web analytics guy is, a lot of users seem to be using internet banking very well but how do you find out where exactly is the problem, what needs to be mended.

Here are some areas of data to be looked into, that gives us insights in this direction.

1)     Pageviews of Error pages.

a.      How many times error pages were thrown up?

b.      Source of error pages, which page led to error pages most and their ranking.

c.      Errors per transaction.

2)     Pageviews of help section and FAQs, which part of help section was most viewed and their ranking.

3)     Total Pageviews per page per transaction. Example, a fund transfer has 4 pages, how many times each of the pages were viewed per transaction.

4)     Offline feedback on errors and complaints on Internet banking transaction.

5)     Average time taken per page online

a.      Rank pages on average time per transaction

6)     Number of fraudulent transactions or security complaints.

These are my personal views and opinion on this subject. Please do let me know your views as comments.

This article is originally found on http://www.praveenkodur.com/ Copy right reserved on http://www.praveenkodur.com/

1. Emails Marketing campaigns with no referral tracking

2. SEO traffic has no referrals in some analytics tools

3. Traffic from Bookmarks.

4. A Viral Marketing Campaign

People who take advantage of direct traffic:

Domain name purchasers, typosquatters, cybersquatters whose intention is to purchase a domain name with typo mistake of popular brand, hence hoping to generate traffic. For more information please visit SEOmoz

Affiliate Marketers, Adsense money makers whose main intention is to make money through arbitrage.

Tracking Direct Traffic

Tracking Radio Ads and offline advertising internally: Some advertisers advertise their brand in Radio and give out the URL such as “http://radio.example.com” which is 301 redirected to URL “http://www.example.com/xyz.php?trackingcode=number”. This way the impact of advertising on the channel can be tracked. This will help in comparing the effectiveness between

1. Two different Ads..

2. Impact on other channel due to advertising on this channel and its co-relation

3. Same Ad performing differently in different cities, regions, geographies.

4. You could further extend to record Page Views, Time on Site and other details..

Google has launched tracking of Adwords audio campaigns in Google Analytics..

Below is an Interesting Example of my blog: The search engine traffic, referral traffic to my blog can be easily analyzed and explained as they are fairly straightforward. SEO, Referral traffic analysis requires no expert, even a layman can understand if they look at Google Analytics Report. However, the interesting part is the Direct Traffic Analysis, which initially I failed to understand until a few months back, hence I have decided to share this analysis in this Direct Traffic post.

Traffic Details

Traffic Share – Medium Wise

Direct Traffic User Details

What can we analyze from the given data?

What Google Analytics says?

1. 13.97% of users are direct, how are they finding my blog even though there is no offline Ads, no TV Ads, no Radio Ads whatsoever?

2. 49.37% of Direct Visitors are new visitors. How could this be explained?

3. Blog has 80.38% bounce rate (for direct visitors) which is high Can we figure out where the problem lies? What is wrong with the site?

4. Average Time by Direct Visitor is 41 seconds. This is a time taken for glancing a webpage, not even reading it.

This means half my direct visitors are new, they glance the page in 41 seconds and most of them leave without doing anything. Not sure how they are finding my site. They mostly visit 1 page of my site and bounce off the site like it was yuck.

Google Analytics does not give any details apart from the above.

Analysis from the above data

The direct users are landing on inner pages of the site. Its very unlikely they are typing the entire URL in the address bar…These Direct Users must be from RSS feeds, bookmarks.

Another data to look at is number of comments.. I get about 12-15 comments in a day most of which are junk and some genuine ones.

1. Users could be clicking on blog posts on their RSS feeds and land on specific page.. They then read and/or post a comment and leave the page.

    

   This is the reason for bounce rate being so high. The users read the blog post and leave; hence bounce rate is not relevant figure to look at.

 

2. Users visit one page and leave, therefore the Average time spent calculated is wrong. Google can only track time correctly if users visit more than one page.The reason being that when a new page is requested from the server then the log files records the time at which the new page was requested from server by the same user.The difference in time at which the page 1 was requested and page 2 was requested is calculated as time spent on the site. So, consider a example when a single page was requested then time spent by user on that page is cannot be determined. Hence average time here is the time spent on site by the users who browsed more than 1 page divided by the total number of users who visited the page.

 

3. Users are finding the blog through blog search engines, blog directories and other blog sites.
   1. They subscribe the RSS feed after which they click on blog posts in RSS Feed.
   2. Users may be finding the site through Book-marking sites, Social media sites add it to their own bookmarks, where users directly find the link to inner page of the site.

Direct Traffic is the most targeted traffic, as these visitors really read the content on your website.
Most returning visitors return through Direct Traffic, especially when you are not advertising offline. They are ideally the best source for returning visitors, hence business can sell their content easily.

This Article is Originally found at praveenkodur.com

Copyright @ 2007 Praveenkodur.com

In Search Engine optimization the cost incurred are mainly is on effort, time, agency cost/employee cost. While as in Search Engine Marketing, the costs are Advertising cost(which is CPC, CPA, CPM etc) and the cost of time/effort.

It is necessary to track the value derived from Visitor Engagement on the site and revenue generated,

Visitor Engagement Tracking

Visitor engagement depends on objective and size of the business,

1) Defining Visitor Engagement depends completely on the content present on your web site.

a. On a Shopping site: Engagement has to be product sale, product search etc..

b. On a Blog: It is the number of comments posted, subscribing RSS feeds, Average time spent on the site, No of page views per visit can be engagement metric.

c. On a Corporate Business Site: People might just visit to pick up telephone number, address of the business. Therefore none of the metrics work.

d. On a News Site: It is number of subscription RSS,Email. It is again time spent on article, number of comments posted, no of page views per visit.

e. On a Forum site: Registrations are a value.

f. Software Downloads Site: Number of downloads are a value.

2) Getting Users to Subscribe your RSS feed is a value. These users will read your content more than repeat visitors. It shows the measure of content popularity.

3) Users favourite the page, site in social bookmarking sites, social network & media sites like Digg.com, Reddit.com, Del.icio.us, stumbleupon.com is also a value provided by SEM traffic.

4) If Links to your blog post increases from SEM traffic, due to people positing your link elsewhere in answers etc, then it is a big value..

5) Users might land on specific detail page pick up the phone and dial the number given on page. Measuring telephone call rate is necessary.

6) For smaller business getting targeted traffic to their website is a value, since it adds to brand building etc.. if not sales..

7) For larger websites, brand is already in place, hence traffic which has a direct effect on revenue is more of a value than mere traffic from.

If the mode of revenue generation is advertising then attracting just traffic, might be worth

For small shopping sites setting up ecommerce tracking is very useful and necessary

This can be done by using the option present in Analytics Settings à Profile Settings àEdit. Give details of Currency, decimal places etc.

Tracking Ecommerce Transaction through Google Analytics

Once you opted Ecommerce tracking, you must edit the receipt page, which is the page after transaction, install a script into this page passing the order value into the Analytics system. Google has provided a comprehensive material on setting up ecommerce transaction.

For other business they need to monitor, track the objective of SEM campaigns. If not the engagement of the visitor on the site.

Optimizing SEO, SEM Campaigns

1) Generally Specific Keywords have higher conversion rate but are lower in volume

2) It is important to add as many new specific long tail keywords as possible…it need effort in research on new keywords on regular intervals.

3) The Head of the keyword list is pretty much fixed with little variations but more effort has to be made in making the tail longer…..that is where maximum effort and time as it can change the ROI of the campaigns.

4) Competitive keywords are always costly and extra effort can change CPC minimally…

a. However the efforts has to be done on Landing page creation..

b. Small changes in conversion rate by new landing page optimizations can dramatically increase ROI

5) Constant Generation of long tail keywords and new keywords are always the key…

6) Using different landing pages for each set of keywords is very good

a. Domain name keywords can be landed on home page

b. Category Specific Headings can be landed on respective section on the site

c. Product keywords or Specific keywords must be landed on inner most pages of the site or respective product pages.

7) Make sure your website ranks for all brand terms including the misspellings of your domain name etc..

a. This can be done by creating a page with list of all misspelled keywords

Using Misspellings can be a great way of generating low cost traffic…but remember there can be 2 types of users typing misspellings

a. Users who accidently typed a misspelling, will most likely click on “Did you mean – Correct Spelling”

b. Users who are uneducated or illiterate,

So you need to decide on keeping such keywords based on the quality of traffic you generate

9) For Large Websites tracking of every keyword to last metric of calculating business ROI is essential…For example:

a. Banking website could track the number of savings accounts, number of loans applied per every keyword along other details such as location, amount of loan, income of user and other other quality measurements which will help determine the quality of keyword.

b. This will help them to make a decision of increase bid rates, adding more same kind of keywords etc..

10) Track phone Calls, If you are running a Click to Call Campaign then provide the keyword which generated the call to call center executive. After the call process collect the feedback of the user and measure the business quality of the keyword, which is again fed back into the Adwords campaigns.

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