E-Marketing (Marketing Research) Rethinking Marketing Research for the Digital Environment Arvind...
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Transcript of E-Marketing (Marketing Research) Rethinking Marketing Research for the Digital Environment Arvind...
e-Marketing (Marketing Research)
Rethinking Marketing Researchfor the Digital Environment
Arvind Rangaswamy
e-Marketing (Marketing Research)
Outline
Traditional research migrating online
Pushing the research envelope online
e-Marketing (Marketing Research)
Types of Marketing Research on the Internet
Secondary data research• Search engines• Content sites (e.g., myplant.com)
Primary data research• Surveys (e.g., e-mail, web site)• Panels (e.g., focus groups, continuing panels, chat groups)• Experiments• Observations
Web site statistics• Standard Log files• Enhanced Log files (e.g., Peapod)
e-Marketing (Marketing Research)
Primary Data
Online Surveys• New medium for traditional surveys
• Enhanced surveys Web-based surveys (e.g., www.insightexpress;
www.harrisinteractive.com)
e-mail surveys
Web site evaluation surveys
Online Focus Groups• Focus group videoconferencing
• Focus group chat windows
e-Marketing (Marketing Research)
Primary Data
Experiments
• Simulated test markets
Continuing Panels• Longitudinal tracking studies
• Custom studies
e-Marketing (Marketing Research)
1010101010 101010101010
Survey is programmed and
activated - invitation to take
survey is sent
Client accesses data and creates reports online
and/or receives automated reports
Respondent connects to survey site and begins
survey
System dynamically generates screens for respondent - data is stored on site server
Typical Web Survey Method
e-Marketing (Marketing Research)
Web Site Evaluation Surveys
Content and Structure (Examples of items)• Graphics
• Visual Attractiveness
• Selling Messages
• Links
• Chat rooms
• Registration Forms
• Audio
Ease of navigation (e.g., Site search engine)
e-Marketing (Marketing Research)
Site Evaluation Surveys (Contd..)
Experience during visit • How much did respondents enjoy their visit?
• Did visitors feel confused while using the site?
• Were visitors frustrated in any way with their experience?
• Did visitors find their visit exciting or boring?
• Did the site meet, exceed or fall short of visitors' expectations?
• What was visitors' overall level of satisfaction visiting the site?
Likelihood of repeat visit
e-Marketing (Marketing Research)
Polls Apart...
Non-representative samples
• Self-selection bias. Respondents are heavier users of computers, Internet and e-mail than non-respondents
• Matching a sample to population on observable characteristics will not make it representative (e.g., propensity weighted scores won’t work!)
Low response rates (e.g., banner clicks average around 0.30%)
Problems of respondent authenticity
Difficulties associated with incentives
Difficulty in gauging response accuracy
e-Marketing (Marketing Research)
Some Online Research Suppliers
www.modalis.com: Web-based surveys, e-mail surveys. Respondents recruited on the web.
www.comscore.com: Generates company-specific panels and monitors their web behavior.
www.greenfield.com: 2.2 million panelists who have volunteered to be members. Participates in drawing to win cash prizes.
www.harrisinteractive.com: 7 million panelists worldwide.
e-Marketing (Marketing Research)
Potential Benefits
Cheaper (Typically about one-third the cost of mail surveys)
Faster than mail surveys
Flexible (Multiple paths in surveys)
Completion rates are higher
Can reach elusive groups (e.g., CIO’s)
Richer content and context than mail and telephone surveys
Interactive (useful for pre-test)
e-Marketing (Marketing Research)
Improving Representativenessof Internet Samples
Select samples from panels
Pre-qualify and profile respondents
Put banners/links to survey at popular web sites
Offer incentives for participation
Focus on products of general usage (e.g., supplies) and segments that match the Internet population
e-Marketing (Marketing Research)
Online Focus Groups(Synchronous and Asynchronous)
Potential Benefits
• Reach difficult to recruit respondents (e.g., geographically dispersed, low-incidence, high currency)
• Broader geographic representation in focus group
• Reduce travel costs
• Useful for discussing sensitive issues (requiring some anonymity)
• Quick turnaround (e.g., transcripts and keywords)
Challenges
• Changes the dynamics of the communication
• Handling emotive issues
e-Marketing (Marketing Research)
Focus Groups via Videoconferencing
Source: Prof. Burke
e-Marketing (Marketing Research)
Focus Groups via Chat Windows
e-Marketing (Marketing Research)
Test Market Experiments -Electronic Shelf Labels
Source: Prof. Burke
e-Marketing (Marketing Research)
Computer-simulated test markets
Source: Prof. Burke
e-Marketing (Marketing Research)
Computer-simulated test markets
Source: Prof. Burke
e-Marketing (Marketing Research)
Test Market Experiments -Promotional Kiosks
Source: Prof. Burke
e-Marketing (Marketing Research)
Customer Tracking -POS Linked to Infrared Sensors
Source: Prof. Burke
e-Marketing (Marketing Research)
Web Site Statistics
How many users visit our site daily? Is that number growing?
What paths do visitors take when they browse our web site?
Which pages are the most popular?
What kind of information is accessed on our server? How many pages are accessed in each directory?
From what countries do users connect? What cities? What states?
From what departments do users connect to the Intranet Server?
Which is the most active day of the week? The most active hour?
What browsers are used to access our web server? What operating systems?
Which sites offer the best referrals to our pages?
e-Marketing (Marketing Research)
Web Server Logs
Transfer Log (records each request to web server)
Error Log (e.g., broken links, mid-process breaks)
Referrer Log (e.g., source web addresses from which a user comes to a specific page)
Agent Log (e.g., browser version of user)
e-Marketing (Marketing Research)
Transfer Log (Common Log Format)
Host name or IP address of the computer making the request
User name of the user on the computer making the request (seldom used)
User name on the local web site making the request (if the reader logs into a secure area of the web site)
Time stamp - the date and time of the request
Request - the text of the actual HTTP request, including the path and file names of the file requested
Status code - the code for the resulting success or failure of the request
Transfer volume - the number of bytes sent to the reader's browser as a result of the request.
e-Marketing (Marketing Research)
Sample Transfer Log
pc18.abcd.com - - [21/Oct/1997:11:02:34 -0700] "GET /HTTP/1.0" 200 2412
pc18.abcd.com - - [21/Oct/1997:11:02:35 -0700] "GET /art/star1.gif HTTP/1.0" 200 678
pc18.abcd.com - - [21/Oct/1997:11:02:35 -0700] "GET /art/star2.gif HTTP/1.0" 200 650
pc18.abcd.com - - [21/Oct/1997:11:02:36 -0700] "GET /art/nav.jpg HTTP/1.0" 200 12781
pc18.abcd.com - - [21/Oct/1997:11:02:39 -0700] "GET /art/logo.gif HTTP/1.0" 200 15633
pc18.abcd.com - - [21/Oct/1997:11:02:40 -0700] "GET /art/storefront.jpg HTTP/1.0" 200 17447
pc18.abcd.com - - [21/Oct/1997:11:03:22 -0700] "GET /information/HTTP/1.0" 200 1971
pc18.abcd.com - - [21/Oct/1997:11:03:23 -0700] "GET /art/bullet.gif HTTP/1.0" 200 920
pc18.abcd.com - - [21/Oct/1997:11:03:55 -0700] "GET /products/HTTP/1.0" 200 2667
pc18.abcd.com - - [21/Oct/1997:11:03:55 -0700] "GET /art/logo2.gif HTTP/1.0" 200 4288
pc18.abcd.com - - [21/Oct/1997:11:04:16 -0700] "GET /products/fudge.html HTTP/1.0" 200 1875
pc18.abcd.com - - [21/Oct/1997:11:04:17 -0700] "GET /products/fudge.gif HTTP/1.0" 200 15645
pc18.abcd.com - - [21/Oct/1997:11:05:32 -0700] "GET /ordering/HTTP/1.0" 200 5139
pc18.abcd.com - - [21/Oct/1997:11:05:33 -0700] "GET /ordering/form.html HTTP/1.0" 200 22791
pc18.abcd.com - - [21/Oct/1997:11:07:08 -0700] "POST /cgi-bin/orderConfirm.cgi HTTP/1.0" 200 3896
pc18.abcd.com - - [21/Oct/1997:11:07:33 -0700] "POST /cgi-bin/orderPost.cgi HTTP/1.0" 200 1388
e-Marketing (Marketing Research)
Statistics from Common Log Format
Number of requests
Number/percentage of successful/failed requests
Number/percentage of cached requests
Top pages or files (most requested documents)
Number of page-transfers by day
Top downloaded files by type (all files)
Top submitted forms and scripts
Bottom pages or files
Top pages by directory
Top directories accessed
Source: Rick Stout (rlsnet.com)
e-Marketing (Marketing Research)
Statistics from Common Log Format (Contd ..)
Average number of requests per week
Average number of requests per day
Total bytes transferred
Average bytes transferred by day
Average bytes transferred by hour of day
Average number of hits on weekdays/weekends
Most/least active day of the week (and number of hits)
Most/least active day ever (and number of hits)
Activity level by day of week/hour of day
Source: Rick Stout (rlsnet.com)
e-Marketing (Marketing Research)
Enhancements to Common Log Format
Difficult to link information across log files
Combined Log Format (Combines Transfer log, Referrer log, and Agent log).
Difficult to identify “unique visits”
Cookies (Stored in browser with expiration time)
CGI Session ID (appended to URL)
User-registration
(Append ID information from any of these methods to log files)
e-Marketing (Marketing Research)
Statistics from Enhanced Log Format
Number of visits
Average number of requests (and page views) per visit
Average duration of a visit
Sequence of user activities at the site
Average number of visits per day or week
Number of visits by hour of the day
Visits from organizations (most active organizations)
Visits by organization type (root domain)
Visits from countries (most active countries)
Top visit entry pages
Top-page durations
Source: Rick Stout (rlsnet.com)
e-Marketing (Marketing Research)
Statistics fromEnhanced Log Format (Contd ..)
Top exit pages
Average number of users on weekdays/weekends
Visit level by day of week/hour of day
Top U.S. geographic regions
Percentage of visits from inside/outside the U.S.
Top cities
Top referring organizations
Top referring URLs
Top browsers
Top user operating systems
Source: Rick Stout (rlsnet.com)
e-Marketing (Marketing Research)
Customer Decision Stages Measures Data sources
Awareness and Search Total pages deliveredCumulative number of visitsUnique visitorsVisitor profilesAided/Unaided recallClickthroughs (referrals from othersites)
Enhanced log fileEnhanced log fileEnhanced log file (e.g., with cookies)RegistrationOnline intercepts/panel surveysLog files/data from affiliates
Interest and Evaluation Incoming links, user sites/groupsVisit duration and depthInter-visit durationRequests for informationLeads generatedSite search usageBrand attitude and knowledgeE-mail activity
Enhanced log fileEnhanced log fileEnhanced log file + registrationLog file/customer databaseCustomer databaseEnhanced log fileOnline intercept surveys, panelse-mail server database
Desire and Trial Requests for informationDownloadsSimulator usagePreferencesConsideration set inclusion
Qualified leadsParticipation in promotions
Log file/Customer databaseLog files+registrationLog files/Activity monitoringActivity monitoring/registrationActivity monitoring, Online surveys,panelsCustomer DatabaseRegistration/Database, surveys
Action Online orderingCoupon redemptionCross sell/Up sellStore visits (e.g., competing stores)Automated replenishment
Log file/DatabaseLog file/DatabaseEnhanced log file/registrationSurveys/Channel partner databaseTransactions database
Post-purchase relationship Customer satisfactionRepeat purchase intentRepeat purchase rate and amountFAQ usageIncoming callsShare of customer requirements
Surveys/Resource usage at siteSurveysEnhanced log file+ registrationEnhanced log file/DatabaseCustomer database+unique IDSurveys/offline database
Deriving Marketing Insights from Web Site Statistics
e-Marketing (Marketing Research)
Summary Benefits of Online Research
Access to volumes of secondary research
Potential for inexpensive, instantaneous, interactive, and global communication with customers
More realistic marketing stimuli and decision contexts
Ability to dynamically change marketing programs and measure consumer response
Individual-level data on search and choice
e-Marketing (Marketing Research)
Pushing the Research Envelope
Integrate marketing research with marketing planning and implementation
• Real-time research and analysis• Link research to implementation (e.g., segmentation study)
Make customers an integral part of the marketing planning process
Establish data sharing learning communities for benchmarking
Do online experiments
Develop methods and models for deriving insights from large data sets
Create customized marketing research bots
e-Marketing (Marketing Research)
An ExamplePushing the Research Envelope
Real-time research and analysis
e-Marketing (Marketing Research)
The Real-Time Decentralized Research Model
BrowserClient
(Research User)
ApplicationServer
(Analysis Store)
DataServer(DataStore)
WebServer
(Research AccessPoint)
http: Response A Java Applet (user
interface)
http: Request for an analysis/model
Java RMI Java RMI
Java RMI
e-Marketing (Marketing Research)
Quote for the Day
Anyone can count the number of seeds in an apple. Who can count the number of apples in a seed?
Anon.