Customer information: Server log file and clickstream analysis; data mining

21
Customer information: Server log file and clickstream analysis; data mining MARK 430 Week 3

description

Customer information: Server log file and clickstream analysis; data mining. MARK 430 Week 3. During this class we will be looking at:. Technololgy tools for online market researchers Web analytics - server log file analysis and Clickstream analysis static (historical data) - PowerPoint PPT Presentation

Transcript of Customer information: Server log file and clickstream analysis; data mining

Page 1: Customer information: Server log file and clickstream analysis; data mining

Customer information: Server log file and clickstream analysis; data mining

MARK 430Week 3

Page 2: Customer information: Server log file and clickstream analysis; data mining

During this class we will be looking at:

Technololgy tools for online market researchers Web analytics - server log file analysis and

Clickstream analysis static (historical data) realtime analysis personalization

Data mining - including “buzz” research Customer relationship management (CRM)

Page 3: Customer information: Server log file and clickstream analysis; data mining

Technology-Enabled Approaches The Web provides marketers with huge amounts of

information about users This data is collected automatically It is unmediated

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

Page 4: Customer information: Server log file and clickstream analysis; data mining

Web server log files All web servers automatically log (record)

each http request

Log file basics (from Stanford)

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

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

Page 5: Customer information: Server log file and clickstream analysis; data mining

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

Page 6: Customer information: Server log file and clickstream analysis; data mining

Software for log file analysis (web analytics)

Market leader is Webtrends

Many other software packages available often made available by an ASP (outsourced

solution) can purchase and manage the software inhouse

How to select a web metrics package (from Webtrends)

Page 7: Customer information: Server log file and clickstream analysis; data mining

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

Page 8: Customer information: Server log file and clickstream analysis; data mining

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

Page 9: Customer information: Server log file and clickstream analysis; data mining

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

Page 10: Customer information: Server log file and clickstream analysis; data mining

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.

Page 11: Customer information: Server log file and clickstream analysis; data mining

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.

Page 12: Customer information: Server log file and clickstream analysis; data mining

Server log reports Format of reports depends on software used

In lab next week we will look at Webtrends reports

This is a demo from a competitor, showing typical reports

Clicktracks reports demo

Page 13: Customer information: Server log file and clickstream analysis; data mining

Real-time profiling: building relationships with customers

Uses real-time Clickstream Monitoring - page by page tracking of people as they move through a website

Uses server log files, plus additional data from cookies, plus sometimes information supplied by user

Real time profiling entails monitoring the moves of a visitor on a website starting immediately after he/she entered it.

By analyzing their “online behavior” the potential customer can be classified into a pre-defined profiles. eg. stylish bargain-hunter etc

Page 14: Customer information: Server log file and clickstream analysis; data mining

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

Page 15: Customer information: Server log file and clickstream analysis; data mining

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

Page 16: Customer information: Server log file and clickstream analysis; data mining

Data mining 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.

Page 17: Customer information: Server log file and clickstream analysis; data mining

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.

Page 18: Customer information: Server log file and clickstream analysis; data mining

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.

Page 19: Customer information: Server log file and clickstream analysis; data mining

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.

Page 20: Customer information: Server log file and clickstream analysis; data mining

Data mining - including “internet buzz” research

“deploying technology that mines data for insights—nuggets of consumer opinion and real-time trends to aid and sharpen market research, advertising campaigns, product development, product testing, launch timetables, promotional outreach, target marketing and more”. (Intelliseek Marketing)

Intelliseek and firms like it use a variety of tools for data mining

A typical site that might be scanned for marketing intelligence is Planet Feedback

Page 21: Customer information: Server log file and clickstream analysis; data mining

Customer relationship management (CRM)

Traditionally marketers have focused on acquiring new customers

CRM reflects a change in focus toward building one-to-one relationships with existing customers to increase retention Significant benefits in terms of cost effectiveness and

efficiency - it costs 5 times more to acquire a new customer than to retain one

Move toward a customer-centric focus However, just implementing CRM software cannot change

the nature of an organization to be customer facing Selling CRM software is big business - one Canadian

example is OnPath