A/B tests for KontoSensor

, 208

Accuracy

, 195

Active method development

, 15

Active personalization

, 61

AdaBoost

, 112

Adobe Experience Manager (AEM)

, 55

Adversarial attack

, 89

Advertising

cutting down 60-second Ad

, 42

facial coding application in

, 41–42

optimizing vignette style ad and making culturally relevant

, 42

Airbnb

, 133

new user bookings

, 190–192

AlexNet

, 95

Alibaba

, 38–39

Allianz Global & Specialty (AGCS)

, 166

Amazon

, 38–39, 131, 215

Amazon’s Alexa

, 43

Anticipation

, 219

APIs

, 11

Apple credit card

, 89

Apple’s Siri

, 43

Application programming interfaces (APIs)

, 135–136

Arc de Triomphe

, 219–220

Artificial intelligence (AI)

, 8, 20–21, 38–39, 80, 92–93, 130, 184, 200

AI and future of sales

, 34

AI-based voice assistance

, 33

algorithms

, 15

applications in modern sales organization

, 23–34

to creating structure to voice assistant generated data

, 43–44

marketing

, 25–27

sales and management

, 30–34

sales development

, 27–30

salesforce achieves scalable

, 21–23

Artificial neural networks

, 86–87

Association rules

, 87

Attention mechanism

, 99

Autoencoders

, 97

Automated machine learning (AutoML)

, 21

algorithms

, 26–28

Automated speech

, 92

Automatic text

, 12

Automation

, 8

Automotive customer insights, changing capabilities in

, 11

Automotive industry, transformation of

, 9–11

B2B

, 70–71

B2C

, 70–71

Backpropagation

, 94

Bagging technique

, 109–112

Beat Cinematch

, 104

BellKor’s Pragmatic Chaos

, 104

Bias

, 84

Big Data

, 184

analytics

, 8

era

, 79–80

“Black box” methods

, 86–87, 89

Boolean labels

, 62–63

Boosting technique

, 87, 112

Branches

, 108

Business implementation, considerations for

, 99

Business intelligence (BI). See also Artificial intelligence (AI)

, 178

deploying

, 178–179

Business-friendly products

, 177

Campaigns

, 25–26

Car clinics

, 11

Cascaded style sheets (CSS)

, 136, 139

Central processing unit (CPU)

, 83

Chatbot technology

, 43–44, 46, 53

developing chatbot persona “Serena”

, 45–46

Climax scene

, 214

Cloud

, 172

Cloudera

, 54–55

Clustering approach

, 87–88, 206

Codalab

, 185

Cold data, leveraging

, 174–175

Cold storage

, 174

Comma-separated values (CSV file)

, 141

Commodity

, 160

Communicate risks relevant to users

, 151–152

Competencies

, 163–164

Competitive advantage

data protection

, 149

designing for privacy in age of digital customer insight

, 149–154

individual privacy management

, 148

privacy

, 149, 154, 156

Competitive data science platform

, 185–186

Complementary phenomena

, 13

Consent management

, 152

Constant learning. See also Deep learning

, 15

Consumers’ emotions

, 39

Content creators, guidance for

, 57

data and feature engineering

, 57–59

model and performance

, 59–60

prediction and feedback

, 60–61

Content Health Panel (CHP)

, 55

Content Management System

, 55

Content Marketing. See also Machine-driven content marketing

, 52–53

Credit Suisse content marketing business challenge

, 53

data science solutions for

, 53–63

through time and at Credit Suisse

, 52–53

Content Marketing Institute (CMI)

, 52–53

Content success prediction tool

, 57–61

Contextualize data collection

, 150–151

Convolutional Neural Networks (CNN)

, 92, 94–95, 114

Correlation One

, 185

Cost function

, 94

COVID-19 pandemic

, 130

COVID19 Global Forecasting

, 185–186

Credit Suisse

content marketing business challenge

, 53

content marketing through time and at

, 52–53

Cross-industry standard process for data mining (CRISP-DM)

, 188–189

CrowdAI

, 185

CrowdANALYTIX

, 185

Crowdsourcing Data science

, 184, 195

Crowdspring

, 133

Culture

, 164

Customer

centricity

, 5–7

communication

, 202–204

feedback channels

, 10–11

monitor data disclosure

, 153–154

service

, 125–126

Customer experience management (CEM)

, 155

marketing perspective

, 154–156

Customer insights

on “transparency”-myth

, 150–152

changing capabilities in automotive customer insights

, 11

constant learning

, 15

customer centricity as driver for growing importance of

, 5–7

decision support through meaningful controls

, 152–153

deep learning and

, 97–99

designing for privacy in age of digital

, 149–154

dynamic capabilities as necessity and opportunity

, 8–9

helping customers monitor data disclosure

, 153–154

individual privacy management in machine age of

, 148

network competencies

, 14–15

new data sources

, 11–12

new methods

, 12–13

new technologies

, 12

synthesizing competencies

, 13–14

transformation from market research to

, 7–8

transformation of automotive industry

, 9–11

value generation of customer insights

, 15–16

through voice assistants

, 43–47

Customer relationship management (CRM)

, 21–22, 162

Cutting down 60-second Ad

, 42

Data

analytics

, 178

disconnect

, 170–171

generation capabilities

, 11

leakage

, 193

literacy

, 153–154

management

, 73, 172

models

, 21

sources

, 11–12

synthesis

, 13

visualizations

, 178

warehouses

, 176

Data “superpower,” realizing

, 180–181

Data competitions

challenges of

, 188–189

Kaggle competition

, 190–192

metrics

, 193–195

opportunities of

, 186–188

Data growth

amount of data

, 173–175

data disconnect

, 170–171

data value equation

, 171

deploying business intelligence

, 178–179

quality of data

, 176–177

realizing data “superpower”

, 180–181

unlocking value of data

, 171–172

usage of data

, 178–181

Data protection

, 2, 54

as global driver for data-driven innovation

, 149

Data revolution, story creates

, 211–212

Data science

, 89–90

Airbnb’s new user bookings

, 190–192

competitions procedure in nutshell

, 190

crowdsourcing

, 183–185

data protection

, 54

guidance for content creators

, 57–61

Kaggle

, 185–186

monitor and optimization

, 55–56

personalizing content

, 61–63

relevant data

, 54–55

solutions for content marketing

, 53–63

Data Scraping

, 2, 130–131

check legal aspects of scraping data source

, 135–136

defining business problem, research question and required data

, 132–133

defining scraping logic

, 136

emergence of

, 129–131

locating and analyzing data source

, 133–135

scraping data

, 136–141

six-step process

, 132

storing and retrieving data

, 141

Data value equation

, 2, 171–172

Data-driven innovation

, 149

Database

, 80

models

, 11

science

, 80

Datathon

, 204–205

DBSCAN method

, 207

Decision support through meaningful controls

, 152–153

Decision tree ensembles

bagging

, 109–112

boosting technique

, 112

empirical illustration of three decision tree ensembles

, 112–114

growing single decision tree

, 105–109

leveraging ensembles to win $1 Million Netflix prize

, 103–105

real-world case

, 105

seeing forest for trees

, 114

from tree to forest

, 109

Deep Blue

, 20

Deep learning

, 87

and customer insights

, 97–99

neural networks

, 89

recommender systems

, 98–99

Deep neural networks (DNNs)

, 2, 92–93

Deep reinforcement learning

, 99

Deepfake Detection Challenge

, 186

Demography

, 41–42

Denial-of-service attack (DoS attack)

, 135

DenseNet

, 95

Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

, 206

Design thinking methods

, 6

DesignCrowd

, 133

99Designs

, 133

Deutsche Bank

, 200, 204

Digital services

, 43

Digital technology

, 74

Digital transformation

, 5–6, 170

Digitalization

, 5–6

Disambiguation

, 126–127

Doordash

, 89

Dramatic arc

, 219–220

DrivenData

, 185

Dynamic capabilities

, 16

as necessity and opportunity

, 8–9

E-health

, 68

Education

, 68

Electronic resource planning (ERP)

, 162

Email Sentiment Analysis

, 30

Emotional arousal

, 40

Emotional response

benefits of

, 40

facial coding application in advertising

, 41–42

measuring through facial coding

, 39–43

outlook on further applications of facial coding

, 42–43

types of emotions to measure

, 39–40

validation of facial coding

, 40–41

Ensemble, The

, 104

Enterprise applications for communities advancement

, 69

Entertainment

, 70

Error function

, 94

Excel

, 215

Experience Data (X-data)

, 162

collecting

, 163

Experience economy

, 160–161

activating experience management across organization

, 163–164

collecting X-Data

, 163

enter age of experience management

, 162–163

experience drives economics offering

, 160

operational data

, 161–162

understanding experiences of stakeholders

, 161

Experience management (XM). See also Customer experience management (CEM)

, 162–163

activating experience management across organization

, 163–164

AGCS

, 166

applying XM to close experience gap

, 165–167

Under Armour

, 166–167

competencies

, 164

integrating XM into operating cadence of organization

, 164–165

JetBlue

, 165–166

Experience Management Platform™

, 164

Expressiveness

, 41

Face-to-face interview techniques

, 43

Facebook

, 38–39, 87

Facebook Messenger app

, 44–46

Facial coding

, 2, 11–12, 38–39

application in advertising

, 41–42

benefits of

, 40

measuring emotional response through

, 39–43

Minority Report become reality

, 38–39

outlook on further applications of

, 42–43

systems

, 40

types of emotions to measure

, 39–40

validation of

, 40–41

Facial data

, 11–12

Facial expression

, 40

Feedback

, 162

Feedforward networks (FNNs)

, 93–94

File-based data storage

, 141

Financial crisis (2007–2008)

, 105

FinTechs

, 200

5G

end of line

, 74

mean for collecting customer data

, 71–74

new aspects associated with

, 66

starting era of

, 65–67

state of the art

, 67–68

use cases for

, 68–71

10-fold cross-validation technique (10-fold CV technique)

, 85–86

4G

, 65

Fourth Industrial Revolution

, 21

Framing

, 219

Fraud detection

, 92

Functional magnetic resonance imaging (fMRI)

, 212

Future of sales

, 34

Gaming

, 70

General Data Protection Regulation (GDPR)

, 135, 148, 201

Generalization

, 84

Generative Adversarial Networks (GANs)

, 95

Genius

, 21–22

Global School in Empirical Research Methods (GSERM)

, 15

Google

, 16, 20, 38–39, 87

Google AI system

, 130

Google Analytics

, 54–55

Google Assistant

, 43

Google bot

, 130

Google Duplex

, 38

Google Inception

, 130–131

Google Search Console APIs

, 54–55

GoogLeNet

, 95

Graphics processing units (GPUs)

, 88, 92–93

GreenBook Research Industry Trends Report (GRIT Report)

, 8

Group Method of Data Handling

, 92

GrubHub

, 89

Gut instinct

, 84

Hierarchical clustering algorithm

, 87–88

Hold competitions

, 184

Hopfield networks

, 92

Host competitions

, 184

Hosts

, 184

Hot data, leveraging

, 174–175

“Hovr” technology

, 167

Human machine interfaces (HMI)

, 11, 16

Human resources

, 122–125

Hyperbolic tangent

, 94

Hypertext markup language (HTML)

, 134

Hypertext transfer protocol (HTTP)

, 136

Image

classification

, 94–95

files

, 140–141

recognition

, 12, 130–131

scraping

, 130

Inciting Incident scene

, 214

Individual privacy management

, 148

Informed consent

, 150

Innocentive

, 185

Innovative firms

, 2

International Data Corporation

, 126

Internet

, 130

Internet of things (IoT)

, 21, 66, 151, 170

Javascript

, 140–141

JetBlue

, 165–166

JSON

, 140–141

Kaggle

, 185–186, 188

competition

, 190–192

Kantar’s facial coding system

, 41

KontoSensor

, 2, 200–201

activation and configuration

, 201

customer communication

, 202–204

Datathon

, 204–205

enhancing activation of

, 207–208

integrated use cases/functionalities

, 201–202

predictive overdraft

, 205–207

sample emails sent out by

, 203

working on

, 208

Language translation

, 92

Lead nurturing

, 26–27

Lead qualification

, 26–27

Lead scoring and prioritization

, 27–28

Learning algorithm

, 84

evaluating success of

, 84–86

Leaves

, 108

Lexical diversity

, 126

Line charts

, 217–218

LinkedIn

, 130–131

Logistic Regression

, 22

Logistics function

, 94

Long Short-term Memory Networks (LSTMs)

, 96

Long Term Evolution (LTE). See 4G

Longitude Problem

, 183–184

“Machine age” for customer insights

, 6–7

Machine learning (ML)

, 8, 13, 20–21, 38–39, 63, 80–83, 170, 184

age of

, 222

Big Data era

, 79–80

call to action

, 89–90

ethics

, 88–89

evaluating success of learning algorithm

, 84–86

stages in learning process

, 83–84

types of machine learning algorithms

, 86–88

at work

, 81–82

Machine-driven content marketing. See also Telemarketing

, 2

added value of

, 63

Maintenance

, 69

Malicious attacks

, 89

Market

basket analysis

, 87

research to customer insights

, 7–8

Marketers

, 27

Marketing. See also Content Marketing

, 25

campaigns

, 25–26

lead nurturing and lead qualification

, 26–27

perspective customer experience management

, 154–156

Measurement error

, 84

Median method

, 207

Medical diagnosis

, 92

Microsoft’s Cortana

, 43

Minimal viable products (MVPs)

, 10

Minority Report

, 43

Minority Report become reality

, 38–39

MobileNet

, 95

Modern sales organization

AI and future of sales

, 34

AI and machine learning

, 20–21

AI applications in

, 23–34

sales process of

, 24

salesforce achieves scalable AI for businesses with data

, 21–23

Moneyball (movie)

, 212–213, 216

Moneyball phenomenon

, 81

Music

, 218

MySQL server

, 141

Naive Bayes

, 22

National Academies of Sciences, Engineering, and Medicine (NASEM)

, 185–186

Natural language analytics

, 122–126

customer service

, 125–126

human resources

, 122–125

Natural language processing (NLP)

, 2, 22, 96, 120–121

emergence of

, 119–120

Natural language understanding (NLU)

, 120–121

emergence of

, 119–120

Net Promoter Score^®

, 162

Netflix Prize

, 103–104

Network competencies

, 14–15

Networking

, 14

Neural networks

, 92–94

architectures and applications

, 94–97

autoencoders

, 97

CNN and image classification

, 94–95

GANs

, 95

LSTMs

, 96

reinforcement learning

, 97

RNNs and NLP

, 96

transformers

, 97

Neuroscience

, 218

News aggregation

, 92

Next Best Actions

, 30–32

Nodes

, 93, 108

Numerai

, 185

Objective function

, 94

Office of Science and Technology Policy (OSTP)

, 185–186

Operational data (O-data)

, 161–162

experience data

, 162

Organizers

, 184

Over-the-air updates (OTA updates)

, 10

Oxytocin

, 212

Pace Productivity Inc

, 24

Pacing

, 219

Pandora’s Box

, 80–81

Passive personalization

, 61

Pattern discovery

, 87

Peloton

, 161

Personalizing content

, 61–63

data and features

, 62–63

model and applications

, 63

recommender systems

, 61–62

Pipeline generation

, 29–30

“Plug-and-play” technology

, 163

Poetics

, 219

Porsche Case

, 9–11

Porsche Passion Report

, 14

Predictable Revenue

, 24

Prediction models

, 53–54

Predictive analytics

, 200–201

Predictive forecasting for sales leaders

, 32–34

Predictive models

, 22

Predictive overdraft

, 205–207

Privacy

, 148

designing for privacy in age of digital customer insight

, 149–154

as global driver for data-driven innovation

, 149

marketing perspective CEM

, 154–156

privacy-sensitive information systems

, 148

Programmatic advertising

, 13

Protecting privacy

, 148

Python

, 206

Quality of data

, 176–177

breaking down data silos

, 177

overcoming lack of access

, 176–177

Quantitative model

, 13

Quantum computing

, 12

R programming language

, 136–137

Random Forest

, 22, 60, 88, 92–93, 109, 112, 114, 207

Random sampling

, 85–86

Random-access memory (RAM)

, 83

Readability

, 58

Reading difficulty

, 59

Real business applications of natural language analytics

, 122–126

Real-time navigation

, 10–11

Recommender systems

, 61–62, 98–99

Recurrent neural networks (RNNs)

, 92, 96

Recurring behavior

, 206

Regular expressions

, 136

Reinforcement learning

, 97

Relational database

, 133

ReLU activation function

, 95

Request for proposal (RFP)

, 31

ResNet

, 95

Revocation

, 153

RMySQL

, 141

RSelenium

, 134

rvest

, 134

Sabermetrics

, 212

Sales

AI and future of

, 34

better decision making with opportunity insights and next best actions

, 30–32

development

, 25, 27, 30

lead scoring and prioritization

, 27–28

and management

, 30–34

predictive forecasting for sales leaders using voice assistance

, 32–34

prospecting and pipeline generation

, 29–30

Salesforce achieves scalable AI for businesses

, 21–23

structured data

, 22

unstructured data

, 22–23

Salesforce Einstein

, 22–23

Salesforces AutoML models

, 22

Santander Customer Satisfaction

, 185

SAP Analytics Cloud

, 173, 179

SAP Business Technology platform

, 171, 173

SAP Data Warehouse Cloud

, 173, 177, 180

SAP HANA Cloud

, 173–175

data tiers in

, 175

SAS

, 206

Scalable AI for businesses

, 21–23

Scraping

, 130

Scraping logic

, 136

Screen scraping

, 130

Search engine optimization (SEO)

, 53

Search engines

, 16

Seekers

, 184

Self-driving cars

, 92

Sensors

, 80

Sentiment analysis

, 59, 128

“Serena,” developing chatbot persona

, 45–46

Shallow learning methods

, 114

Sigmoid

, 94

Smart home technology

, 69

Smart logistics

, 69–70

Smart transportation

, 69–70

Soap-operas

, 53

Social media

, 11–12, 20, 55

Sociological models

, 16

Somatic marker hypothesis

, 39

Sponsors

, 184

Stakeholders, experiences of

, 161

Statistical modeling

, 81–82

Storytelling

, 212–213

Amazon

, 215

anticipation

, 219

Arc de Triumph

, 219–220

back to data

, 213

constructing

, 215–217

flip and ramp

, 221–222

framing

, 219

getting help out of weed pile

, 214–215

honing slide and chart

, 215

line charts

, 217–218

music

, 218

neuroscience

, 218

pacing

, 219

from past tense to right now

, 222–223

putting together big moment without designer or Brad Pitt

, 221

starting with key scenes

, 213–214

story creates data revolution

, 211–212

Structured data

, 22

Superpower

, 172

Supervised learning

, 86–87

Support vector machines (SVMs)

, 86–88, 92–93

Synthesizing competencies

, 13–14

Sys. sleep(x) function

, 137

T-statistics

, 15

Technological and organizational measures

, 149

Technology transformations

, 10

Telemarketing. See also Content Marketing; Machine-driven content marketing

conversions, predicting

, 105

decision tree

, 108

Telemedicine

, 68

Text analytics

, 2

Text classification models

, 15

Text Mining

, 120–121

emergence of

, 119–120

project considerations

, 126–128

technologies

, 128

Text processing methods

, 128

The Furrow (agricultural magazine)

, 52

“Time Series” method

, 206

Time-consuming factor analysis

, 5–6

Time-wise fraction of article read

, 57

Transformation process

, 2

Transformer(s)

, 97

transformer-based language models

, 114

TransmogrifAI

, 22, 33

Transmogrification

, 22

“Transparency”-myth

, 150–152

communicate risks relevant to users

, 151–152

contextualize data collection

, 150–151

TripAdvisor

, 131

TunedIT

, 185

Turing, Alan

, 20

Turing machine

, 20

Turning Point scene

, 214

Turning raw sensory information

, 83

Twitter

, 130–131

Uber

, 9–10, 161

Under Armour

, 166–167

Uniform resource locators (URLs)

, 134–135

Units

, 93

Universal Mobile Telecommunications System (UMTS)

, 65

Unstructured data

, 22–23

Use cases for 5G

, 68, 70–71

applications at national or regional scale

, 69

enterprise applications for advancement of communities

, 69

entertainment

, 70

personal, home, and social applications

, 68–69

smart transportation and smart logistics

, 69–70

Use-oriented development processes

, 6

Valence

, 39–41

Validation of facial coding

, 40–41

Value generation of customer insights

, 15–16

Variable cost per contact (VCPC)

, 125

VGG

, 95

Visualization tools

, 13

Vodafone

, 69

Voice assistance

, 32, 34, 39

challenges and benefits of using voice assistants in research

, 44

designing voice driven chatbot

, 44–45

developing chatbot persona “Serena”

, 45–46

Einstein voice assistant smart speaker

, 33

generating customer insights through

, 43–47

outlook on further applications of

, 46–47

utilizing conversational AI to create structure to voice assistant generated data

, 43–44

Voice coding

, 2

Voice driven chatbot, designing

, 44–45

Weather sensors

, 80

Web scraping

, 130

World Health Organization (WHO)

, 185–186

XGBoost

, 60, 63, 112, 114

XML

, 140–141

XML path language (XPath)

, 136, 138

Yelp

, 89

ZFNet

, 95

ZINDI

, 185