E-Marketing (Marketing Research) Rethinking Marketing Research for the Digital Environment Arvind Rangaswamy

e-Marketing (Marketing Research)

Rethinking Marketing Researchfor the Digital Environment

Arvind Rangaswamy


Outline

Traditional research migrating online

Pushing the research envelope online


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)


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

http://www.harrisinteractive.com/


Primary Data

Experiments

• Simulated test markets

Continuing Panels• Longitudinal tracking studies

• Custom studies


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


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)


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


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


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.

http://www.modalis.com/

http://www.comscore.com/

http://www.greenfield.com/

http://www.harrisinteractive.com/


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)


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


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


Focus Groups via Videoconferencing

Source: Prof. Burke


Focus Groups via Chat Windows


Test Market Experiments -Electronic Shelf Labels

Source: Prof. Burke


Computer-simulated test markets

Source: Prof. Burke


Computer-simulated test markets

Source: Prof. Burke


Test Market Experiments -Promotional Kiosks

Source: Prof. Burke


Customer Tracking -POS Linked to Infrared Sensors

Source: Prof. Burke


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?


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)


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.


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


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)


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



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)


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



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



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


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


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


An ExamplePushing the Research Envelope

Real-time research and analysis


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


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.

Documents

E-Marketing (Marketing Research) Rethinking Marketing Research for the Digital Environment Arvind Rangaswamy