A

abstract concepts, 31–37

acuity, visual, 16–17

affinities and psychological profiles, 141–143

AI Winter, 196

Alexa (Amazon), 28–29, 198

Amazon Alexa, 28–29, 198

analyzing dreams, 55, 59–60, 131–133

analyzing user research. See Post-It note categorization method

analyzing word frequency, 96–97

appeal (form of emotion), 130–131, 161–163, 175

Apple Siri, 28, 198–199

Ariely, Dan, 57

artifacts (what researchers notice), 68

artificial intelligence

artificial neural networks, 197–199

background information, 196–197

recommendations for, 202

statistical learning, 197–199

Assistant app (Google), 198

assumptions

challenging internal, 155–156

contextual interviews and, 71, 81

empathy research and, 65–66

attention. See vision, attention, and automaticity

audience segmentation

case study, Millennial money, 152–154

case study, trust in credit, 154

challenging internal assumptions, 155–156

creating, 77–78, 113, 141, 152–154

emotion and, 147–148

empathy research and, 155–158

finding the dimensions, 152–155

identifying, 117

language and, 143–146

psychographic profiles, 131–132, 141–143

recommendations for, 160

wayfinding and, 149–151

augmented reality (AR), 24

automaticity. See vision, attention, and automaticity

awaken (form of emotion), 130–131, 165–166, 175

B

behaviors, unconscious, 12–14, 64, 70

blockers (problems), 52–53

boundary extension, 35–36

brain

artificial intelligence and, 196–198

spatial information and, 19–21

too much information, 56–57

what information/pathway, 9–10, 69–70

where information/pathway, 9–10, 21–22, 28–29

Buxton, Bill, 178

C

Cancer.gov website, 45

case studies

adventure race, 133–135

auction website, 90–91

builders, 167–169

coupons, 127

credit card theft, 131

distracted movie watching, 109–110

ecommerce payment, 122–123

high-net-worth individuals, 169–170

Institute of Museum and Library Services, 101

let’s just build it!, 178–179

Mad Men and women, 135–136

medical terms, 97–98

Millennial money, 152–154

producing products vs. managing business, 112

psychographic profile, 131–132

search terms, 105

security department, 87–88

shopping mall, 104–105

tax code, 113

teacher timeline, 123–124

timeline of researcher’s story, 118–119

trust in credit, 154

website hierarchy, 89–90

chess game, 48–49

Chipchase, Jan, 65

“Cognition and Emotion” (LeDoux), 57

communication (what researchers notice), 68

comparable testing, 182–183

competitors, testing with, 182–183

contextual inquiry

about, 62, 179–180

case study, adventure, 133–135

case study, Mad Men and women, 135–136

case study, timeline of a researcher’s story, 118–119

clean slate mentality and, 155

empathy research and, 186–189

prototyping and testing, 179–183

wayfinding and, 106–107

contextual interviews. See also Post-It note categorization method

common questions, 74–75

empathy research and, 63, 67–68

identifying nuanced behaviors, 64, 70–71

memory and “in the moment”, 64, 81

reasons for choosing, 63–65

recommended approach for, 70–74

recording, 96

uncovering semantic representations in, 46

understanding user needs, 65–70

watching superusers, 64

what researchers notice, 68

contrast, visual popout and, 14

convergent thinking, 173–174

Cool Hunting website, 161

Cortana (Microsoft), 28, 198

current state (problem solving), 48, 122

customer journey, 122, 124–125, 127, 191–193

customers. See user research

D

data-to-insights process. See Post-It note categorization method

decision making

about, 5, 7–8, 78

additional information, 47

appeal form of emotion and, 162–163

awaken form of emotion and, 166

case study, coupons, 127

case study, ecommerce payment, 122–123

case study, teacher timeline, 123–124

customer journey and, 122, 124–125, 127, 191–193

drivers for, 58–59

emotion and, 55–60

empathy in design decisions, 189

enhance form of emotion and, 164

evidence-based, 178, 189–190

irrationality in, 57–58

just-in-time needs, 122–124

machine learning/artificial intelligence and, 201–202

Post-It note categorization method, 75, 79–81, 124–127, 153

problem solving, 47–54

questions regarding customers, 61, 121

recommendations for, 71, 127

See/Feel/Say/Do chart and, 158

too much information and, 56–57

Define phase (Double Diamond process), 174–175

Deliver phase (Double Diamond process), 175

design thinking

decision making in, 189–190

empathy research and, 63, 65, 164, 186

learning while making, 177–179

desires (customer), 55, 59–60

Develop phase (Double Diamond process), 175

dimensions (Six Minds). See Six Minds of Experience

Discover phase (Double Diamond process), 174–175

divergent thinking, 173–174

diversity of mental models, 39

Double Diamond process, 173–177

dreams, analyzing, 55, 59–60, 131–133

E

emotion

about, 6–8, 78

appeal form of, 130–131, 161–163, 175

audience segmentation and, 147–148

awaken form of, 130–131, 165–166, 175

case study, adventure race, 133–135

case study, builders, 168

case study, credit card theft, 131

case study, high-net-worth individuals, 169–170

case study, Mad Men and women, 135–136

case study, psychographic profile, 131–132

customer desires, goals, fears, 55, 59–60, 131–133

decision making drivers, 58–59

empathy in design decisions, 189

enhance form of, 130–131, 163–165, 175

getting the zeitgeist, 133–135

machine learning/artificial intelligence and, 202

portraits of, 55–56

Post-It note categorization method, 76, 79–81, 136–138, 147–148, 153

questions regarding customers, 62, 129

recommendations for, 71, 138

satisficing and, 55–56, 58–59, 135–136

See/Feel/Say/Do chart and, 158

too much information and, 56–57

empathy research

audience segmentation and, 155–158

contextual inquiry and, 186–189

contextual interviews and, 63, 67–68

design thinking and, 63, 65, 164, 186

discovering what vs. why, 69–70

leaving assumptions at the door, 65–66

on multiple levels, 186–189

what researchers notice, 68

enhance (form of emotion), 130–131, 163–165, 175

Escape Room adventure game, 48, 52

evidence-based decision making, 178, 189–190

experience. See Six Minds of Experience

experts

language and, 44–45, 96–98, 143–146

problem solving and, 49–53

eye movement and tracking (saccades)

eye tracking devices, 84–89, 89–90

null result, 15

unconscious behaviors, 12–14

virtual space response delays, 27–28

visual acuity, 16–17

visual popout, 14–15

F

Facebook Torch, 197

“fail fast, fail often” approach, 173

fast thinking (automatic processes), 11–12, 24–25, 56

fears (customers), 55, 59–60, 131–133

focus groups, 63

framing problems differently, 49–52

F-shaped eye search pattern, 13–14

G

Gallistel, Randy, 19

goals (customer), 55, 59–60, 122, 131–133

goal state (problem solving), 48, 122

GOOB (Get Out Of Building), 186

Google products

Assistant app, 198

Home assistant, 28

TensorFlow software library, 197

H

“happy hour” concept, 37

heat maps, 13, 84, 88–90

high-fidelity prototypes, 180–181, 190

Home assistant (Google), 28

Hound app (SoundHound), 198

Human-Centered Design Toolkit (IDEO), 65

I

icons, 16

IDEO design studio, 65, 177

innovation, 176–177

in-person investigations. See contextual interviews

in-situ prototyping, 181

Instagram social media platform, 16

interfaces

observing user interactions, 25–28, 104–106

voice-activated, 28–29, 198–200

interruptions (what researchers notice), 68

interviews. See contextual interviews

“in the moment” and memory, 64, 81

irrationality in decision making, 57–58

J

journey map, 127, 191

K

Kahneman, Daniel, 11, 47, 56

Kaplan, Craig, 50

L

language

about, 4–5, 7–8, 78

appeal form of emotion and, 162

audience segmentation and, 143–146

case study, builders, 168

case study, high-net-worth individuals, 170

case study, Institute of Museum and Library Services, 101

case study, medical terms, 97–98

communication difficulties, 42–44, 49

Double Diamond process and, 176

empathy in design decisions, 188

machine learning/artificial intelligence and, 201

Post-It note categorization method, 75, 79–81, 98–101, 143–146, 153

questions regarding customers, 61

reading between the lines, 96–98

recommendations for, 71, 102

recording interviews, 96

revealing level of expertise, 44–45, 96–98, 143–146

See/Feel/Say/Do chart and, 158–159

semantic concepts and, 41

uncovering semantic representations in interviews, 46

word frequency analysis, 96–97

Lean Startup methodology, 186

learning-while-making phase, 177–179

LeDoux, Joseph, 57

LifeHacker website, 163

M

machine learning (ML), 200–202

market research. See user research

McClelland, James, 197

MedlinePlus website, 97–98

memory

about, 5, 7–8, 78

abstract concepts, 31–34

awaken form of emotion and, 166

boundary extension, 35–36

case study, producing products vs. managing business, 112

case study, tax code, 113

case study, timeline of researcher’s story, 118–119

context triggering, 64, 81

Double Diamond process and, 176

empathy in design decisions, 188

enhance form of emotion and, 164–165

expectations and, 5, 36–37, 39, 106–107

machine learning/artificial intelligence and, 201

meanings in the mind, 112–113

Post-It note categorization method, 75, 79–81, 114–116, 154

questions regarding customers, 61, 111

recommendations for, 71, 119

See/Feel/Say/Do chart and, 159–160

stereotypes, 31–32, 36–37

trash talk experiment, 33–36

understanding mental models, 38–39

mental models, 38–39

Microsoft Cortana, 28, 198

Minsky, Marvin, 196

mock-ups, 189–190

motion, visual popout and, 14

Mural tool, 75

mutilated checkerboard problem, 50–52

N

navigational cues

observing user interactions, 25–28, 104–106

physical vs. virtual space, 24–25

voice interfaces and, 28–29

neural networks, artificial, 197–199

Newell, Alan, 196–197

novices

language and, 44–45, 96–98, 143–146

problem solving and, 49–53

null result in eye tracking, 15

O

The Organization of Learning (Gallistel), 19

P

parallel distributed process (PDP), 197

PayPal (company), 186–191

physical space, wayfinding in, 21–29, 104–107

Pinterest social media platform, 24

popout, visual, 14–15

Post-It note categorization method

affinities and psychological profiles, 141–143

analysis exercise, 78–82

creating audience segmentation, 77–78, 152–154

decision making category, 75, 79–81, 124–127, 153

emotion category, 76, 79–81, 136–138, 147–148, 153

language category, 75, 79–81, 98–101, 143–146, 153

looking for trends across participants, 77–78

memory category, 75, 79–81, 114–116, 154

organizing participant findings, 76–77

reviewing and writing down observations, 75–76

vision category, 75, 79–81, 91–94

wayfinding category, 75, 79–81, 107–109, 149–151, 154

Predictably Irrational (Ariely), 57

problem solving

building subgoals, 48–49, 52–53, 122

case study, builders, 167

defining problem, 48–49

Double Diamond process and, 177

framing problems differently, 49–52

machine learning/artificial intelligence and, 201–202

matching audience perception of virtual space, 28

mutilated checkerboard problem, 50–52

resolving problems, 52

prototyping, 178–183, 190

psychographic profiles, 131–132, 141–143

Q

questions regarding customers

contextual interviews, 74–75

decision making, 61, 121

emotion, 62, 129

language, 61, 95

memory, 61, 111

questioning existing assumptions, 71, 81

vision, attention, and automaticity, 61, 83–84, 90

wayfinding, 61, 103

R

RealTimeBoard tool, 75

redefining the problem space, 49–52

risk aversity, 57

Rumelhart, David, 197

S

saccades. See eye movement and tracking

satisficing, 55–56, 58–59, 135–136

See/Feel/Say/Do chart, 156–159

semantics. See also memory

about, 41

considering customer associations, 111–119

mental models and, 38–39

semantic map, 42

stereotypes and, 36

uncovering representations in interviews, 46

Simon, Herbert, 50, 56, 177, 196–197

Siri (Apple), 28, 198–199

Six Million Dollar Man (TV show), 195

Six Minds of Experience

about, 6–7, 78

affinities and psychological profiles, 141–143

data-to-insights process. See Post-It note categorization method

decision making. See decision making

Double Diamond process and, 173–177

emotion. See emotion

finding the dimensions, 152–155

language. See language

learning-while-making phase, 177–179

memory. See memory

recommendations, 70–74, 183

user research considerations, 61–62

vision, attention, and automaticity. See vision, attention, and automaticity

wayfinding. See wayfinding

Sketching User Experiences (Buxton), 178

slow thinking (conscious processes), 11–12, 56

Snapchat social media platform, 23

sophistication of language usage, 96–98

“stalking with permission”. See contextual interviews

statistical learning, 197–199

stereotypes, 31–32, 36–37, 112–113

sticky notes. See Post-It note categorization method

subgoals in problem solving, 48–49, 52–53, 122

“succeed fast, succeed often” approach

about, 173

design thinking and, 177–179

Double Diamond process, 173–177

prototyping and testing, 179–183

recommendations, 183

superusers, 64

surveys, 63, 69–70

T

TensorFlow software library (Google), 197

testing

interfaces to reveal metaphors for interaction, 25–28

prototyping and, 179–183

visual elements for correct identification, 16

Thinking, Fast and Slow (Kahneman), 11, 56

Tobii eye tracking technology, 13

too much information, 56–57

Torch (Facebook), 197

touchscreen tests, 25–28

Tunisian ants in the desert, 19–21

Turing, Alan, 196–197

Turing test, 196

Tversky, Amos, 47

U

unconscious behaviors, 12–14, 64, 70

usability test findings, 69–70

user research

analyzing. See Post-It note categorization method

appeal form of emotion and, 130–131, 161–163

awaken form of emotion and, 130–131, 165–166

case study, builders, 167–169

case study, high-net-worth individuals, 169–170

case study, let’s just build it!, 178–179

choosing contextual interviews, 63–65

common questions, 74–75

empathy research, 65–70

enhance form of emotion and, 130–131, 163–165

getting to deep desires, goals, and fears, 55, 59–60, 131–133

recommendations for, 70–74, 171

Six Minds considerations, 61–62

US penny, 31, 39

V

virtual reality (VR), 24

virtual space, wayfinding in, 21–29, 104–107

vision, attention, and automaticity

about, 4, 7, 9–12, 78

appeal form of emotion and, 162

case study, auction website, 90–91

case study, builders, 167

case study, high-net-worth individuals, 169

case study, security department, 87–88

case study, website hierarchy, 89–90

Double Diamond process and, 175

empathy in design decisions, 187

eye tracking. See eye movement and tracking

machine learning/artificial intelligence and, 200–201

null result, 15

Post-It note categorization method, 75, 79–81, 91–94

questions regarding customers, 61, 83–84, 90

recommendations for, 70, 94

See/Feel/Say/Do chart and, 158

testing visual elements, 16

unconscious behaviors, 12–14

visual acuity, 16–17

visual popout, 14–15

voice-activated interfaces, 28–29, 198–200

Voltaire (writer), 41

W

wayfinding

about, 4, 7, 78

audience segmentation and, 149–151

case study, distracted movie watching, 109–110

case study, search terms, 105

case study, shopping mall, 104–105

Double Diamond process and, 176

empathy in design decisions, 188

expectations and, 24–25, 106–107

machine learning/artificial intelligence and, 201

“not good with directions” and, 21

observing user interactions, 25–28, 104–106

physical versus virtual space, 21–29

Post-It note categorization method, 75, 79–81, 107–109, 149–151, 154

questions regarding customers, 61, 103

recommendations for, 71, 110

See/Feel/Say/Do chart and, 159

Tunisian ants in the desert, 19–21

voice-activated interfaces, 28–29

where information and, 21–22, 28–29, 104–105

“weekend” case study, 38–39

what information/pathway (brain), 9–10, 69–70

where information/pathway (brain), 9–10, 21–22, 28–29, 104–105

why information, 69–70

Williams Sonoma (company), 50

Wizard of Oz prototype, 181

word frequency analysis, 96–97

Z

zeitgeist, 133–135