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

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

2. 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)

3. 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

4. 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

5. 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

6. 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)

7. How do you use log files effectively? • Identify leading indicators of business success • Identify the key performance metrics with which to measure them • Establish benchmarks to track changes over time • Configure software and use settings consistently

8. 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

9. 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

10. 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.

11. 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.

12. 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

13. 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

14. 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

15. 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

16. 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.

17. 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.

18. 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.

19. 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.

20. 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

21. 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