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)  realtime analysis  personalization  Data mining - including “buzz” research  Customer relationship management (CRM)

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

Web server log files  All web servers automatically log (record) each http request  Log file basics (from Stanford) Log file basics  Most log file formats can be extended to include “cookie” information  This allows you to identify a user at the “visitor” level

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

Software for log file analysis (web analytics)  Market leader is WebtrendsWebtrends  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) How to select a web metrics package

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 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

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

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.

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.

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 Clicktracks

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

Clickstream monitoring and personalization  How does Amazon.com do that?Amazon.com  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

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

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.

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 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.

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.

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

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 OnPathOnPath