Index_C

C

Caching of Web pages

defined, 404–405

institutional, 405–406

log analysis and, 405–406

personal, 405

Card sorting, 192–199

analyzing output informally, 195

analyzing output using cluster analysis, 195–198

benefits, 199

creating cards, 193–194

described, 192

for information architecture development, 47

preparation, 193

for prioritization, 198–199

process of, 193–194

the sort, 194

timing for, 192–193

Cardiod microphones, 225

Causation, correlation vs., 354

CCCORP (Computer Consultants Corporation) user advisory board charter, 388–389

Characteristic survey questions and subcategories, 308

Charters for user advisory boards, 388–391

Checklist survey questions, 310–311

Chi-square test, 354

Chief Experience Officer (CXO), 515

Click-throughs, 22

Clickstream analysis

average path, 413

content clustering, 414

cookies used for, 408

defined, 413

diary studies vs., 370

"next" pages, 413

overview, 413–414

purchase path, 413

shopping cart abandonment, 413–414

See also log analysis

Client domain statistics in log analysis, 410

Close-ended questions

open-ended vs., 121, 124–125

for surveys, 310, 311

Cluster analysis

analyzing card sorting output using, 196–198

defined, 195–196

diagrams, 197–198

EZSort software for, 196–198

Clustering, as audience attributes, 143–145

c|net

differentiation by, 24–25

navigation inconsistency in sites, 432–433

visual theme of, 50–51

Coding data

customer support comments, 400–401

defined, 400

diaries, 383

from focus groups, 242–243

software for, 401

Combined techniques

focus groups and diaries, 469–471

log analysis and usability tests, 473–474

observational interviews and usability tests, 471–472

surveys and focus groups, 472–473

task analysis and usability tests, 474–475

Comments

coding for focus groups, 242–243

leaving space in surveys for, 319

See also customer feedback analysis

Commercial recruiters. See professional recruiters

Committees, user. See user advisory boards

Communications of the ACM (June 1993), 468

Companies

conflicting agendas in, 501–502

culture for iterative development, 34–35

independent analysis specialists, 441

success criteria for, 23–27

traffic/demographic specialists, 442

as Web site stakeholders, 17

See also user-centered corporate culture

Comparing survey variables, 345–349

Competitive advantage from usercentered processes, 517

Competitive profiles, 423–426

audience profile, 424

features and attributes, 424–426

product description, 423–424

Competitive research acting on, 433–434

analyzing the data, 431–433

benchmarking, 433

competitive analysis techniques, 426–431

contextual inquiry, 427

extracting from focus group data, 246

feature audit, 425

focus groups for, 67, 206, 428

identifying the competition, 421–423

identity design and, 51, 52

invitations for, 427

profiling the competition, 423–426

recruiting for, 426–427

sorting competitors into categories, 422–423

surveys, 308, 430–431

usability testing, 67, 68, 428–430

user experience research, 419

uses for, 420

ZDNet example, 434–437

Computer Consultants Corporation (CCCORP) user advisory board charter, 388–389

ComScore, 442

Conclusion section of reports, 492

Confidence interval, 351–352

Confidentiality, reports and, 488

Confirming participant appointments, 106–108

Conflicting agendas in companies, 501–502

Conglomerate statistics in log analysis, 409–410

Consistency

in survey questions, 316

of user interfaces, 49

Consultants

contacting by email and phone, 454

finding, 449–454

guidelines for managing, 456–457

hiring, 447–457

reasons for using, 439

as resources after the initial research, 457

RFPs (request for proposals) for, 450–454

savings from using, 439

setting expectations, 454–457

timing for using, 448–449

See also specialists

Contact information for surveys, 320, 321, 322

Content clustering, clickstream analysis of, 414

Context

development over time, 369

presentations to engineers and, 496–497

Contextual Design, 57, 160, 176, 511

Contextual inquiry, 160–182

affinity diagrams for analyzing data, 176–179

after release, 67

analyzing data, 174–182

artifacts, 172

in atypical situations, 166

authenticity issues, 170

benefits and pitfalls, 69

big brother model, avoiding, 169

building models, 180–181

competitive, 427

conducting the inquiry, 167–169

defined, 160

developing scenarios, 166

expert/novice model, avoiding, 168

follow-up interview, 164, 170–171

frequency vs. importance and, 181

goals and, 176

guest model, avoiding, 168

for information architecture development, 47

information to collect, 171–173

interviewer/interviewee model, avoiding, 168

introduction and warm up, 169

in iterative development cycle, 36

learning the domain, 165

limitations, 395

main observation period, 169–170

master/apprentice model, 167

mental models and, 175

methods and, 171–172, 175–176

organizing questions into projects, 72

overview, 69, 160–161

partnership model, 167–168

practical preparations for, 166–167

privacy and, 170

process of, 162–173

recruiting for, 163–164

for requirement gathering, 67, 68

results, 181–182

schedule for, 162

scheduling participants, 164–165

scheduling service example, 37

structure of the inquiry, 169–171

as survey follow-up, 357–358

target audience, 163

task analysis vs., 182, 184

terminology and, 175

time requirements, 76

tools and, 171, 175

uses for, 161–162

values and, 176

videotaping, 173–174

warm-up, 169

wrap-up, 170

Contractors. See specialists

Conversion rate, 412

Cookies

clickstreams using, 408

expiration dates, 407–408

log analysis and, 406–408

sampling and tracking cookies, 330

session vs. identity, 407–408

turning on session tracking, 407

Cooper, Alan, 130

Corporate culture. See user-centered corporate culture

Corporate edict, iterative development vs., 29, 30

Corporations. See companies

Correlation, causation vs., 354

Costs

equipment, 76, 530–531

incentives, 76, 108–109, 164

professional recruiters, 116–117

See also budgets

Counting survey results. See tabulating survey results

Creation step in iterative development

overview, 32

scheduling service example, 38, 39, 40, 41

CRM (customer relationship management), 416, 418

Cross-tabulation, 345–349

Cultural models, 181

Culture, corporate. See user-centered corporate culture

Cummings, e. e., 498

Current users, identity design and, 52

Customer feedback analysis

analyzing comments, 401–402

benefits and pitfalls, 71

coding comments, 400–401

collecting comments, 397–398

customer support process, 396–397

described, 71

negative nature of comments, 396

organizing comments by subject and severity, 400

reading comments, 398–399

scheduling service example, 42

"stock" answers to questions, 397

tabulating comments, 401

tips, 398–399

uses for, 395, 396

Customer relationship management (CRM), 416, 418

Customer support. See customer feedback analysis

Customers, users vs., 134

CXO (Chief Experience Officer), 515