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

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The Web provides marketers with huge amounts of information about users ... Use referrer files to identify commonly used search terms and the search engine ... – PowerPoint PPT presentation

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Title: 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?
  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

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)?
  • gt 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
    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

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
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