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

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Customer information Server log file and
clickstream analysis data mining

• MARK 430
• Week 3

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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)

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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

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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

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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

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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)

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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

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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

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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

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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.

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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.

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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

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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

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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

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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

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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.

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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.

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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.

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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)?
• gt Allows firms to target offers to the customers
  who are most responsive, saving promotional costs
  and increasing sales.

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Data mining - including internet buzz research

• deploying technology that mines data for
  insightsnuggets 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

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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