- Absorptive capacity
- building
- overcoming
- overview of
- people and
- process and
- tools and
- A/B tests
- complicated
- series of
- simple
- Accelerated learning processes
- cascading campaigns and
- in creating transactions
- micro-segmentation
- in new product design
- for organizations
- overview of
- using data for
- value of
- Accessibility of data
- Accuracy of data
- Acquisition of Big Data
- customer-first culture and
- making good choices
- measurement quality
- overview of
- salespeople and
- truth and
- variety
- velocity
- volume
- Acquisition step in data strategy
- Action
- bias for
- calls to
- moving from strategy to
- Adams, Scott
- Adopting multiple technology innovations
- Adoption diffusion curve
- Advertising
- evaluative
- experiential
- tracking effectiveness of
- Affinity analysis
- After-action reviews
- Airlines
- American
- Continental
- data traps and
- loyalty programs and
- Aligning user metrics and evaluation/reward structures
- Amazon
- American Airlines
- Amgen
- Analysis, common forms of
- Analysis step of data strategy
- Analytics
- overview of
- types of, and types of data
- See also Analytics cycle
- Analytics-based process of organizational learning
- Analytics cycle
- discovery analytics
- production analytics
- reporting analytics
- Apollo astronauts
- Application
- of Dynamic Customer Strategy
- of statistical models
- Application step of data strategy
- Aristotle, model of persuasion of
- Arrogance, as barrier to Big Data and Dynamic Customer Strategy
- Assessment step of data strategy
- Assumptions
- with cohort and incubate approach to CLV
- as data traps
- metrics and
- norms and
- recognizing
- Aster
- Attitudinal loyalty
- Attribution models
- Availability bias
- Averages, assumptions about
- Averaging scores
- Avoiding data traps
-
- B2B, satisfaction in
- Bar charts
- Barksdale, Jim
- Barriers to Big Data and Dynamic Customer Strategy
- BCG Grid
- Becker, Ben
- Behavioral data
- Behavioral loyalty
- Benefits
- of Big Data
- of Dynamic Customer Strategy
- of loyalty
- Best Buy
- Beyond hype about Big Data
- Bias
- for control
- or action
- Big Data
- barriers to
- causality and
- definition of
- dimensions of
- experiments and
- improving adoption of systems for
- leveraging
- spending on
- using effectively
- See also Acquisition of Big Data
- Bluefin Labs
- Brainstorming alternatives
- Budget for discovery analysis
- Buick Enclave
- Burrows, Cathy
- Business cycles, accelerated
- Buying data
- Buzzwords in marketing
- Byrne, Patrick
-
- Cabela's
- basket starters
- conversion metrics
- culture of
- customer experience
- predicting purchases
- RFM and
- rites of passage at
- training of staff
- Calls to action
- Cardinal Health
- Cascading campaigns
- as accelerating learning
- example of
- as ongoing experiments
- overview of
- process of discovery to
- Case-teaching method at Harvard
- Causality
- Big Data and
- establishing through control
- illustration of
- Causal relationships
- CEOs
- distrust of data and
- lack of understanding of
- list of Big Data barriers of
- Ceteris paribus assumption
- Change
- building absorptive capacity for
- as controllable cause
- as culture change
- demographic, as environmental condition
- global implementation and
- learning and mastering
- managing
- Change management, project management compared to
- Cheese making, washing machines for
- Chinese furniture manufacturing
- Choice of data
- Chronology and causality
- Circuit City
- Citibank MasterCard
- CKC (customer knowledge competence)
- Cleaning data
- Clickstream data
- Click-through
- meaning of
- as operational definition of interest
- Cluster analysis
- CLV. See Customer lifetime value (CLV)
- Co-creation
- of experience
- of value
- Coding data
- Cohort and incubate approach to CLV
- Coke (Coca-Cola)
- Commitment
- Common language for organization, importance of
- Community
- in conceptual map for loyalty and CLV
- customer experience and
- Company propensity to relate
- Competitive advantage, sustainable
- Completeness of data
- Concentra
- Concepts
- for conceptual mapping
- operational definitions of
- See also Conceptual mapping
- Conceptual foundation for Dynamic Customer Strategy
- Conceptual mapping
- Big Data and Dynamic Customer Strategy framework
- choice of data and
- concepts for
- conditions for
- of Dynamic Customer Strategy
- establishing causality through control
- of loyalty and customer lifetime value
- Microsoft media center edition advertising plan
- operationalizing
- overview of
- relationships in
- simple versus complex
- Conditions for conceptual map
- Confidence intervals
- Confirmatory validity
- Continental Airlines
- Contribution margin
- Control
- bias for
- establishing causality through
- Conventional displays
- Conversations with customers. See also Cascading campaigns
- Conversions
- metrics for
- monitoring
- Correlational relationships
- Counterfactual variables
- Covin, Jeff
- Creating data strategy
- acquisition
- analysis
- application
- assessment
- avoiding data traps
- no data scenario
- overview of
- steps in
- Cronenberg, David
- Cross-function teams
- Cube (unstructured) display
- Culbert, Bruce
- Culture
- of Cabela's
- customer-first
- leadership and
- Customer experience
- Cabela's and
- management of
- measurement of
- performance and
- responsiveness and
- transparency and
- Customer knowledge competence (CKC)
- Customer-level Dynamic Customer Strategy
- Customer lifetime value (CLV)
- alternative to
- cohort and incubate approach to
- conceptual map for
- factors in
- as forward-thinking
- as metric
- model of
- Customer mindset
- assumptions about
- as center of decision making
- marketing and
- share of wallet and
- Customers
- conversations with
- lead users
- progressive profiling of
- relationships with
- retention of, trapped approach to
- time-starved
- See also Cascading campaigns; Customer lifetime value (CLV); Customer mindset
- Customer satisfaction
- Cycle of analytics
- discovery analytics
- production analytics
- reporting analytics
-
- Data
- accessibility of
- accuracy of
- choice of
- cleaning
- coding
- completeness of
- granularity of
- lifestyle
- machine
- matching to models
- metric
- motivational
- nonmetric
- precision of
- preparing
- psychodemographic
- putting into motion
- quality of
- questions about
- rates of streams of
- sourcing
- structured
- timeliness of
- trusting
- types to acquire
- unstructured
- usage of, and insight matrix
- See also Big Data; Clickstream data; Data strategy; Data traps
- Data assets inventory
- Data dictionary
- Data diva syndrome
- Data reduction, visualization as
- Data sources, orthogonal
- Data strategy
- as key component of Dynamic Customer Strategy
- of Microsoft
- See also Creating data strategy
- Data traps
- airline example
- avoiding
- DCS. See Dynamic Customer Strategy (DCS)
- Decision making
- acquisition of Big Data and
- experiments and
- managing decision risk
- selecting metrics for
- sleep and
- as team-based
- Decision mapping
- Decision tree analysis
- Defined customer value
- Definitions
- of concepts
- operational
- of organizational terminology, importance of
- Deming, W. Edwards
- Demographic change, as environmental condition
- Dependent variables
- Design School of Strategy
- Diffusion of innovations curve
- Digital body language
- Dimensions of Big Data
- Discounting, mindless, avoiding
- Discovery analytics
- Dish Network
- Displays
- dynamic display process
- Rand categorization of
- Disposal, customer activities of
- Diva syndrome
- Dwell time
- Dwyer, Robert F. (Dwyer, Schurr, and Oh matrix)
- Dynamic, definition of
- Dynamic Customer Strategy (DCS)
- applying
- barriers to
- benefits of
- Design School of Strategy compared to
- overview of
- strategy versus opportunity
- See also Accelerated learning processes
- Dynamic display process
- Dynamic offers
-
- EarthLink
- EBay
- Effectiveness, measures of
- Effect size
- Efficiency, measures of
- Egg
- Elimination and causality
- Emotional attachment and loyalty
- Emphasis in displays
- Empowering
- employees
- entrepreneurs
- salespeople
- Engagement in shopper journey
- Enterprise data warehouses
- Entrepreneurs, empowering
- Environmental variables
- Evaluation/reward structures, aligning metrics with
- Evaluative advertising
- Execution of strategy
- Executive fiat, determining tolerance range by
- Executive sponsorship
- Expectancy Theory of Motivation
- Experiential advertising
- Experimental design, multifactorial
- Experiments
- decisions and
- strategic, exploiting
- Explanation in displays
- External factors and performance
-
- Facebook
- Face validity
- Failing fast
- benefits of
- experiments and
- Microsoft case study
- Failure as way of learning
- Field-strategy linkages, importance of
- ForeSee
- Forrester Research Inc.
- Furniture manufacturing
-
- Gains, maximum, for customer-facing Big Data solutions
- Gallery Furniture, progressive profiling of
- Gerstner, Lou
- Global implementation
- Google
- Granularity of data
- Greenberg, Paul
-
- Harrah's
- Harvard, case-teaching method at
- Heat maps
- Hero ethos
- Hilton Honors
- Hospital mortality rates and Facebook likes
- Hotels
- Hype about Big Data
- Hypotheses, testing
-
- IBM
- Idea generation
- Implementation
- in functional area or department
- global
- managing
- separating strategy from
- Individual transactions, value of accelerated learning in creating
- Inferential precision
- Infinity
- Innovations
- accelerated learning and
- diffusion of innovations curve
- near-simultaneous adoption of multiple
- in technology, adopting multiple
- Insight Technology Group
- Inventory of data assets
- Investment risk
-
- JC Penney
- Jeffery, Mark
- Jive
-
- Kaus, Phil
- Konica-Minolta Business Solutions
-
- LaCugna, Joe
- Language, common, for organization
- Lasker, Albert
- Launch strategy, planning
- Leadership
- exploiting strategic experimentation
- movements and
- organizational culture and
- overview of
- Lead scoring
- Learn and act
- Learning processes, accelerated
- cascading campaigns and
- in creating transactions
- micro-segmentation
- in new product design
- for organizations
- overview of
- using data for
- value of
- Lifestyle data
- Lindblom, Charles
- LinkedIn
- Logistic regression
- Lone Star Park case study
- Loss, avoiding
- Loyalty
- airlines and
- attitudinal
- behavioral
- benefits of
- conceptual map for
- correlation between customer experience and
- customer lifetime value and
- making benefits of transparent
- measures of
- Microsoft training program and
- operational definition of
- as past behavior
- production analytics and programs for
- simple model of
-
- Machine data
- Mac's Convenience Stores
- Magic Quadrant score
- Managers, data-capable
- Mapping maturity curves
- Marketing
- Big Data investment and
- buzzwords in
- list of Big Data barriers of
- proximity
- Marketing ploy, Big Data as
- Market level, responsiveness at
- Matching data to models
- Math avoidance, as barrier to Big Data and Dynamic Customer Strategy
- Maturity curves, mapping
- McIngvale, James
- McKinsey Global Institute
- McPhaul, Sam
- Measures
- of customer experience
- of data quality
- of effectiveness
- of efficiency
- of loyalty
- of success
- Meredith Corp.
- Metric data
- Metrics
- aligning with evaluation/reward structures
- application of Big Data to
- assumptions and
- new, creating
- right, creating
- selecting
- tolerance range
- variation and performance
- visualization
- Micro-segmentation
- Microsoft
- Mindless discounting, avoiding
- Mindset, Dynamic Customer Strategy as
- Mintzberg, Henry
- Mission statements
- Mitsubishi
- Model myopia
- Models
- attribution
- behavioral loyalty
- customer lifetime value
- in Dynamic Customer Strategy
- matching data to
- operationalizing
- of persuasion
- predictive, and human insight
- propensity
- statistical, applications of
- See also Conceptual mapping
- Model training
- Monitoring
- conversions
- tolerance range
- See also Tolerance range; Tracking; Visualization
- Motion, putting data into
- Motivational data
- Movement leadership
- Multifactorial experimental design
- Multiple technology innovations, adopting
-
- Near-simultaneous adoption of multiple innovations
- Negotiation
- Net Promoter Score (NPS)
- Newton, Isaac
- Nielsen
- Nike Plus site
- Nonmetric data
- Norms and Dynamic Customer Strategy/Big Data culture
- Not-invented-here (NIH) syndrome
- NPS (Net Promoter Score)
- Numbers myopia
- Numeric values, assigning to variables
-
- Offers
- crafting
- dynamic
- Oh, Seja (Dwyer, Schurr, and Oh matrix)
- Operational analytics
- Operational definitions
- Operationalizing
- conceptual mapping
- models
- Operationalizing strategy
- definitions for
- experiments and decisions
- managing decision risk
- Microsoft case study
- overview of
- using Big Data effectively
- Operational map for Dynamic Customer Strategy
- Operational velocity, increased
- Opportunity risk
- commitment, availability bias, and
- failing fast and
- as Type II error
- Opportunity seeking
- Opportunity versus strategy
- Optimal Design
- Order generation, speed of
- Organizational culture
- Organizational learning
- Organizational transactions
- Orthogonal data sources
- Overstock.com
-
- “Path to purchase”
- Payback, tracking
- Pedowitz Group
- People and building absorptive capacity
- Performance
- concept map of
- in conceptual map for loyalty and CLV
- definition of
- as speed of service
- tolerance range for
- value, propensity to relate, and
- value and
- variation and
- Personal value equation
- Persuasion, Aristotle model of
- Piloting systems
- Pivoting business focus
- Pizza Hut
- Planning launch strategy
- Policies and procedures
- Porter, Michael
- Porter's Five Forces
- Precision of data
- Predictive models and human insight
- Pregnancy campaign of Target
- Preparing data
- Pringles
- Process
- analytics-based, of organizational learning
- building absorptive capacity and
- customer strategy as
- dynamic display
- snowballing
- See also Accelerated learning processes
- Process satisfaction
- Product design, value of accelerated learning in
- Production analytics
- Product satisfaction
- Professional development
- Progressive profiling
- Project management, change management compared to
- Propensity models
- Propensity to relate
- Proximity marketing
- Psychodemographic data
- Purchase/consumption cycle
- Purpose and opportunistic decision making
- $P$-value
-
- Quality of data
- Questions about data
-
- Ramamurthy, Ram
- Rand, Bill
- Rand categorization of displays
- Random effects
- Ranking versus rating
- Raynor, Michael
- Real-time displays
- Recency, frequency, monetary value (RFM) scoring
- Reducing risk, experiments as
- Regression analysis
- Reichheld, Fred
- Relationships
- causal versus correlational
- overview of
- Reporting analytics
- Reporting exchanges
- Resources and absorptive capacity
- Response, accelerated
- Responsiveness
- in conceptual map for loyalty and CLV
- customer experience and
- definition of
- Results, determining causes of
- Retailers
- RFM (recency, frequency, monetary value) scoring
- Risk and decision making See also Opportunity risk
- Rites of passage
- Ritz-Carlton
- Roeper, Richard
- Role of Big Data
- Roosevelt, Teddy
- Royal Bank of Canada
-
- Salesforce.com
- Sales intelligence (SI) companies
- Sales life of customers
- Salespeople
- acquisition of data and
- attribution modeling and
- empowering
- involving in process
- Sample size
- Sampling and data acquisition
- Sandboxes
- SAS Institute
- Satisfaction, operational definition of
- Scenario testing
- Schaars, Theo
- Schultz, Howard. See also Starbucks
- Schurr, Paul (Dwyer, Schurr, and Oh) matrix
- Scouts
- Segmentation and data assets inventory
- Selling the way buyers want to buy
- Sentiment analysis
- Share of wallet
- “Shopper journey,”
- Simplification of displays
- Skewed distribution and tolerance range
- Skunk-works approach
- Sleep and decision making
- Slevin, Dennis
- Snowballing process
- Social media. See also Facebook; Twitter; YouTube
- Social world, leadership in
- Sourcing data
- Speed of service
- Split-half tests
- Starbucks
- Static displays
- Statistical control
- Statistical models, applications of
- Statistical packages
- Statistical power
- Statistics, to reduce risk
- Stories and culture
- Strategy
- absorptive capacity and
- for data acquisition
- execution of
- linkages between field and
- moving to action from
- opportunity versus
- separating from implementation
- theory as
- See also Creating data strategy; Operationalizing strategy; Testing strategies
- Streaming insight
- achieving state of
- analytics cycle
- applications of statistical models
- data usage and insight matrix
- matching data to models
- opportunity to create
- as pipeline issue
- types of data and types of analytics
- Streams of data, rates of
- Structured data
- Structured displays
- Success
- generating quick
- measures of
- See also Failing fast
- Survey approach to CLV
- Sustainable competitive advantage
- Switching costs
- SWOT analysis
- Systematic cause of variance
-
- Taguchi Block Design
- Target
- behavioral loyalty program of
- polling inventory data by
- production analytics of
- Teams, cross-function
- Technology companies
- Technology innovations, adopting multiple
- Teen pregnancy
- Teradata
- Teradata Partners User Group
- Teradata study
- Terminology, definitions of
- Testing strategies
- complicated A/B tests
- hypotheses and
- sample size
- series of A/B tests
- simple A/B or split-half
- Theoretical foundation for Dynamic Customer Strategy
- Theories
- of how things work
- as strategies
- Vroom's Expectancy Theory of Motivation
- Theory of Variation
- Timeliness of data
- Time-starved buyers
- Titus, Varkey
- Tolerance range
- for campaign performance
- creating
- definition of
- monitoring
- Tools for building absorptive capacity
- Tracking
- advertising effectiveness
- campaigns
- payback
- See also Monitoring
- Trade shows
- Transactional data, and data traps
- Transactions, value of accelerated learning in creating
- Transparency
- in conceptual map for loyalty and CLV
- customer experience and
- definition of
- Trapped approach to data acquisition
- Triangulation
- Trusting data
- Truth and Big Data
- Turf, as barrier to Big Data and Dynamic Customer Strategy
- Tweaking/tuning
- Twitter
- Type I and Type II error
- Types of data to acquire
-
- University rankings
- Unstructured data
- Unstructured displays
- Users, involving in process
- US News & World Report's university rankings
-
- Validity
- Value
- of accelerated learning
- of data, assessment of
- of data assets inventory
- performance, propensity to relate, and
- performance and
- of white paper, determining
- Values
- Dynamic Customer Strategy/Big Data culture and
- opportunistic decision making and
- Variables
- assigning numeric values to
- causal versus correlational
- counterfactual
- dependent
- environmental
- Variance, systematic cause of
- Variation and performance
- Variety
- acquisition of Big Data and
- avoiding data traps and
- Big Data, experiments, and
- as dimension of Big Data
- inferential precision and
- types of data to acquire and
- Velocity
- acquisition of Big Data and
- bias for action and
- Big Data, experiments, and
- as dimension of Big Data
- of learning and action
- operational
- Virgil
- Visualization
- benefits of
- dynamic display process
- Rand categorization of displays
- Volume
- acquisition of Big Data and
- Big Data, experiments, and
- as dimension of Big Data
- enterprise data warehouses and
- inferential precision and
- Volvo
- Vroom's Expectancy Theory of Motivation
-
- Walmart
- Wanamaker, John
- Washing machines for cheese making
- Watson, Susan
- Welch, Jack
- White paper, determining value of
- Wireless service providers
- Wood, Steven
- Woolf, Jeff
- Workarounds, eliminating
- Wurtzel, Alan
-
- YouTube
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