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

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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