IntroductionA clickstream is the recording of the parts

of the screen a computer user clicks on while web browsing or using another software application.

As the user clicks anywhere in the webpage or application, the action is logged on a client or inside the web server, as well as possibly the web browser, router, proxy server.

IntroductionClickstream analysis is useful for web activity analysis,

software testing, market research, and for analyzing employee productivity.

Clickstream as defined by Internet Advertising Bureau (IAB) :

“The electronic path a user takes while navigating from site to site, and from page to page within a site.

It is a comprehensive body of data describing the sequence of activity between a user’s browser and any other Internet resource, such as a Web site or third party ad server”

MethodologyThe click stream data is analyzed to identify different

paths taken by the visitors and the sequence of pages that lead to payment of membership fee.

Based on this analysis, specific strategies are recommended to maximize the revenue for the website.

The main point of clickstream tracking is to give webmasters insight into what visitors on their site are doing.

DataData is obtained from the site in the form of click

stream records. Each record consists of the details of clicks by the visitors and each record contains the following details:Server IP Client IP Time stamp with Date Status: HTTP Status codeURL requested: has three subfields namely The request

method, resource requested and the protocol used No. of bytes transferred

The country of origin for a specific request is identified using the IP address.

DataURL is used to identify the information/web page browsed

by the visitors. Time stamp of each click is used to sequence the movement

of the visitors across different pages in the website. Identifying a unique user session is an important step in

the analysis of click stream data. Inactivity for more than 30 minutes is considered as a break of session.

This is an approximation since there could be multiple users accessing from the same IP, or the same user accessing from different IPs.

Due to lack of more data available we consider hits from each unique IP as belonging to a unique user for a unique session.

Technology-Enabled Approaches The Web provides marketers with huge

amounts of information about users This data is collected automatically

Server-side data collection Log file analysis - historical data Real-time profiling (tracking user Clickstream analysis)

Client-side data collection (cookies)Data MiningThese techniques did not exist prior to the

Internet. They allow marketers to make quick and responsive

changes in Web pages, promotions, and pricing. The main challenge is analysis and interpretation

Web server log files• All web servers automatically log (record) each http

request

• A server log is a log file (or several files) automatically created and maintained by a server of activity performed by it.

• A typical example is a web server log which maintains a history of page requests.

• Most log file formats can be extended to include “cookie” information

– This allows you to identify a user at the “visitor” level

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Web Server Logging – How Does it Work?Web servers such as Apache or Microsoft IIS

record activity as they receive and fulfill requests.

Web servers provide general-purpose logging at a very detailed level.

To prepare the data for analysis, the web team must clean and organize log records – a big job!

Web Server Logging – A Log Record Example

What log files can record includes:Number of requests to the server (hits)Number of page viewsTotal unique visitors (using “cookies”)The referring web siteNumber of repeat visitsTime spent on a pageRoute through the site (click path)Search terms usedMost/least popular pages

Software for log file analysis (web analytics)

• Market leader is Webtrends

How do you use log files effectively?

1. Identify leading indicators of business success

2. Identify the key performance metrics with which to measure them

3. Establish benchmarks to track changes over time

4. Configure software and use settings consistently

Shortcomings of log file analysisCannot identify individual people. The log file

records the computer IP address and/or the “cookie”, not the user.

Information may be incomplete because of caching.

Assumptions made in defining “user sessions” may be incorrect.

This is why benchmarking is so importanttrends rather than absolute numbers

Log file analysis is a useful tool to:

identify what visitors are looking for what content they find most interestingwhich search and navigation tools they find

most usefulwhether promotions are being successfulidentify normal volatility in usage levelsmeasure growth in site usage as compared to

overall web usage

Enhancing marketing tactics using web analytics - some examples

Identify point of drop-off in registration or purchasing process.Pinpoint problem and concentrate efforts on the apparent

trouble spot to improve conversion rates.Maximize cross-selling opportunities in an on-line

store Identify the top non-purchased products that customers

also looked at before completing the purchasing process.Add these products in as suggestions

Refine search engine placements by implementing keyword strategyUse referrer files to identify commonly used search terms

and the search engine or directory that sent the customer.

Improve web site structure using web analytics - some examplesAnalysis of search logs to improve findability on the

web site.Do people search by “category” rather than “uniquely

identifying” search terms? Redesign home page to enhance visibility of most

commonly used links and therefore promote usability.Demote least used items to “below the fold”

Analyze “click paths”, entry and exit points to trace most common routes around the site. Identify areas where navigation seems unclear or confusing Improve navigation to match demonstrated user

preferences.

Clickstream monitoring and personalizationHow does Amazon.com do that?

This type of personalization is very complex and expensive to achieveExisting customers and order databases must be

mined for buying patterns People who bought a Nora Jones CD also bought a John

Grisham novel Called collaborative filtering

Real-time monitoring of customers on your site needed, so you can make recommendations or special offers at the right time

Becomes even more complex when combined with information actually provided by the customer

Data Analysis and DistributionData collected from all customer touch points are:

Stored in the data warehouse, Available for analysis and distribution to

marketing decision makers.

Analysis for marketing decision making:

Data miningCustomer profilingRFM analysis (recency, frequency, monetary

Data mining = extraction of hidden predictive information in large databases through statistical analysis.

Marketers are looking for patterns in the data such as: Do more people buy in particular months Are there any purchases that tend to be made

after a particular life event

Refine marketing mix strategies, Identify new product opportunities, Predict consumer behavior.

Real-Space Approaches

Real-space primary data collection occurs at offline points of purchase with:Smart card and credit card readers,

interactive point of sale machines (iPOS), and bar code scanners are mechanisms for collecting real-space consumer data.

Offline data, when combined with online data, paint a complete picture of consumer behavior for individual retail firms.

Customer profilingCustomer profiling = uses data warehouse information to help

marketers understand the characteristics and behavior of specific target groups.

Understand who buys particular products,

How customers react to promotional offers and pricing changes,

Select target groups for promotional appeals,

Find and keep customers with a higher lifetime value to the firm,

Understand the important characteristics of heavy product users,

Direct cross-selling activities to appropriate customers;

Reduce direct mailing costs by targeting high-response customers.

RFM analysisRFM analysis (recency, frequency, monetary)

= scans the database for three criteria.

When did the customer last purchase (recency)?

How often has the customer purchased products (frequency)?

How much has the customer spent on product purchases (monetary value)?

=> Allows firms to target offers to the customers who are most responsive, saving promotional costs and increasing sales.