Business Analytics

2. Introduction
A 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.

3. Introduction
Clickstream 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”

4. Methodology
The 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.

5. Data
Data 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 code
URL 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.

6. Data
URL 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.

7. 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 Mining
These 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

8. 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

9. 9
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!

10. Web Server Logging –
A Log Record Example

11. What log files can record includes:
Number of requests to the server (hits)
Number of page views
Total unique visitors (using “cookies”)
The referring web site
Number of repeat visits
Time spent on a page
Route through the site (click path)
Search terms used
Most/least popular pages

12. Software for log file analysis (web analytics)
• Market leader is Webtrends

13. 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

14. Shortcomings of log file
analysis
Cannot 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 important
trends rather than absolute numbers

15. Log file analysis is a useful tool to:
identify what visitors are looking for
what content they find most interesting
which search and navigation tools they find most useful
whether promotions are being successful
identify normal volatility in usage levels
measure growth in site usage as compared to overall
web usage

16. 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 strategy
Use referrer files to identify commonly used search terms and
the search engine or directory that sent the customer.

17. Improve web site structure using web analytics
- some examples
Analysis 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.

18. Clickstream monitoring and
personalization
How does Amazon.com do that?
This type of personalization is very complex and
expensive to achieve
Existing 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

19. Data Analysis and Distribution
Data 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 mining
Customer profiling
RFM analysis (recency, frequency, monetary

20. 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.

21. 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.

22. Customer profiling
Customer 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.

23. RFM analysis
RFM 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.