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Leveraging Customer Information through Technology

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Leveraging Customer Information through Technology Powered By Docstoc
					  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)

 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 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)
      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
        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?

 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

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

				
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