Index

  1. Absorptive capacity
    1. building
    2. overcoming
    3. overview of
    4. people and
    5. process and
    6. tools and
  2. A/B tests
    1. complicated
    2. series of
    3. simple
  3. Accelerated learning processes
    1. cascading campaigns and
    2. in creating transactions
    3. micro-segmentation
    4. in new product design
    5. for organizations
    6. overview of
    7. using data for
    8. value of
  4. Accessibility of data
  5. Accuracy of data
  6. Acquisition of Big Data
    1. customer-first culture and
    2. making good choices
    3. measurement quality
    4. overview of
    5. salespeople and
    6. truth and
    7. variety
    8. velocity
    9. volume
  7. Acquisition step in data strategy
  8. Action
    1. bias for
    2. calls to
    3. moving from strategy to
  9. Adams, Scott
  10. Adopting multiple technology innovations
  11. Adoption diffusion curve
  12. Advertising
    1. evaluative
    2. experiential
    3. tracking effectiveness of
  13. Affinity analysis
  14. After-action reviews
  15. Airlines
    1. American
    2. Continental
    3. data traps and
    4. loyalty programs and
  16. Aligning user metrics and evaluation/reward structures
  17. Amazon
  18. American Airlines
  19. Amgen
  20. Analysis, common forms of
  21. Analysis step of data strategy
  22. Analytics
    1. overview of
    2. types of, and types of data
    3. See also Analytics cycle
  23. Analytics-based process of organizational learning
  24. Analytics cycle
    1. discovery analytics
    2. production analytics
    3. reporting analytics
  25. Apollo astronauts
  26. Application
    1. of Dynamic Customer Strategy
    2. of statistical models
  27. Application step of data strategy
  28. Aristotle, model of persuasion of
  29. Arrogance, as barrier to Big Data and Dynamic Customer Strategy
  30. Assessment step of data strategy
  31. Assumptions
    1. with cohort and incubate approach to CLV
    2. as data traps
    3. metrics and
    4. norms and
    5. recognizing
  32. Aster
  33. Attitudinal loyalty
  34. Attribution models
  35. Availability bias
  36. Averages, assumptions about
  37. Averaging scores
  38. Avoiding data traps
  39.  
  40. B2B, satisfaction in
  41. Bar charts
  42. Barksdale, Jim
  43. Barriers to Big Data and Dynamic Customer Strategy
  44. BCG Grid
  45. Becker, Ben
  46. Behavioral data
  47. Behavioral loyalty
  48. Benefits
    1. of Big Data
    2. of Dynamic Customer Strategy
    3. of loyalty
  49. Best Buy
  50. Beyond hype about Big Data
  51. Bias
    1. for control
    2. or action
  52. Big Data
    1. barriers to
    2. causality and
    3. definition of
    4. dimensions of
    5. experiments and
    6. improving adoption of systems for
    7. leveraging
    8. spending on
    9. using effectively
    10. See also Acquisition of Big Data
  53. Bluefin Labs
  54. Brainstorming alternatives
  55. Budget for discovery analysis
  56. Buick Enclave
  57. Burrows, Cathy
  58. Business cycles, accelerated
  59. Buying data
  60. Buzzwords in marketing
  61. Byrne, Patrick
  62.  
  63. Cabela's
    1. basket starters
    2. conversion metrics
    3. culture of
    4. customer experience
    5. predicting purchases
    6. RFM and
    7. rites of passage at
    8. training of staff
  64. Calls to action
  65. Cardinal Health
  66. Cascading campaigns
    1. as accelerating learning
    2. example of
    3. as ongoing experiments
    4. overview of
    5. process of discovery to
  67. Case-teaching method at Harvard
  68. Causality
    1. Big Data and
    2. establishing through control
    3. illustration of
  69. Causal relationships
  70. CEOs
    1. distrust of data and
    2. lack of understanding of
    3. list of Big Data barriers of
  71. Ceteris paribus assumption
  72. Change
    1. building absorptive capacity for
    2. as controllable cause
    3. as culture change
    4. demographic, as environmental condition
    5. global implementation and
    6. learning and mastering
    7. managing
  73. Change management, project management compared to
  74. Cheese making, washing machines for
  75. Chinese furniture manufacturing
  76. Choice of data
  77. Chronology and causality
  78. Circuit City
  79. Citibank MasterCard
  80. CKC (customer knowledge competence)
  81. Cleaning data
  82. Clickstream data
  83. Click-through
    1. meaning of
    2. as operational definition of interest
  84. Cluster analysis
  85. CLV. See Customer lifetime value (CLV)
  86. Co-creation
    1. of experience
    2. of value
  87. Coding data
  88. Cohort and incubate approach to CLV
  89. Coke (Coca-Cola)
  90. Commitment
  91. Common language for organization, importance of
  92. Community
    1. in conceptual map for loyalty and CLV
    2. customer experience and
  93. Company propensity to relate
  94. Competitive advantage, sustainable
  95. Completeness of data
  96. Concentra
  97. Concepts
    1. for conceptual mapping
    2. operational definitions of
    3. See also Conceptual mapping
  98. Conceptual foundation for Dynamic Customer Strategy
  99. Conceptual mapping
    1. Big Data and Dynamic Customer Strategy framework
    2. choice of data and
    3. concepts for
    4. conditions for
    5. of Dynamic Customer Strategy
    6. establishing causality through control
    7. of loyalty and customer lifetime value
    8. Microsoft media center edition advertising plan
    9. operationalizing
    10. overview of
    11. relationships in
    12. simple versus complex
  100. Conditions for conceptual map
  101. Confidence intervals
  102. Confirmatory validity
  103. Continental Airlines
  104. Contribution margin
  105. Control
    1. bias for
    2. establishing causality through
  106. Conventional displays
  107. Conversations with customers. See also Cascading campaigns
  108. Conversions
    1. metrics for
    2. monitoring
  109. Correlational relationships
  110. Counterfactual variables
  111. Covin, Jeff
  112. Creating data strategy
    1. acquisition
    2. analysis
    3. application
    4. assessment
    5. avoiding data traps
    6. no data scenario
    7. overview of
    8. steps in
  113. Cronenberg, David
  114. Cross-function teams
  115. Cube (unstructured) display
  116. Culbert, Bruce
  117. Culture
    1. of Cabela's
    2. customer-first
    3. leadership and
  118. Customer experience
    1. Cabela's and
    2. management of
    3. measurement of
    4. performance and
    5. responsiveness and
    6. transparency and
  119. Customer knowledge competence (CKC)
  120. Customer-level Dynamic Customer Strategy
  121. Customer lifetime value (CLV)
    1. alternative to
    2. cohort and incubate approach to
    3. conceptual map for
    4. factors in
    5. as forward-thinking
    6. as metric
    7. model of
  122. Customer mindset
    1. assumptions about
    2. as center of decision making
    3. marketing and
    4. share of wallet and
  123. Customers
    1. conversations with
    2. lead users
    3. progressive profiling of
    4. relationships with
    5. retention of, trapped approach to
    6. time-starved
    7. See also Cascading campaigns; Customer lifetime value (CLV); Customer mindset
  124. Customer satisfaction
  125. Cycle of analytics
    1. discovery analytics
    2. production analytics
    3. reporting analytics
  126.  
  127. Data
    1. accessibility of
    2. accuracy of
    3. choice of
    4. cleaning
    5. coding
    6. completeness of
    7. granularity of
    8. lifestyle
    9. machine
    10. matching to models
    11. metric
    12. motivational
    13. nonmetric
    14. precision of
    15. preparing
    16. psychodemographic
    17. putting into motion
    18. quality of
    19. questions about
    20. rates of streams of
    21. sourcing
    22. structured
    23. timeliness of
    24. trusting
    25. types to acquire
    26. unstructured
    27. usage of, and insight matrix
    28. See also Big Data; Clickstream data; Data strategy; Data traps
  128. Data assets inventory
  129. Data dictionary
  130. Data diva syndrome
  131. Data reduction, visualization as
  132. Data sources, orthogonal
  133. Data strategy
    1. as key component of Dynamic Customer Strategy
    2. of Microsoft
    3. See also Creating data strategy
  134. Data traps
    1. airline example
    2. avoiding
  135. DCS. See Dynamic Customer Strategy (DCS)
  136. Decision making
    1. acquisition of Big Data and
    2. experiments and
    3. managing decision risk
    4. selecting metrics for
    5. sleep and
    6. as team-based
  137. Decision mapping
  138. Decision tree analysis
  139. Defined customer value
  140. Definitions
    1. of concepts
    2. operational
    3. of organizational terminology, importance of
  141. Deming, W. Edwards
  142. Demographic change, as environmental condition
  143. Dependent variables
  144. Design School of Strategy
  145. Diffusion of innovations curve
  146. Digital body language
  147. Dimensions of Big Data
  148. Discounting, mindless, avoiding
  149. Discovery analytics
  150. Dish Network
  151. Displays
    1. dynamic display process
    2. Rand categorization of
  152. Disposal, customer activities of
  153. Diva syndrome
  154. Dwell time
  155. Dwyer, Robert F. (Dwyer, Schurr, and Oh matrix)
  156. Dynamic, definition of
  157. Dynamic Customer Strategy (DCS)
    1. applying
    2. barriers to
    3. benefits of
    4. Design School of Strategy compared to
    5. overview of
    6. strategy versus opportunity
    7. See also Accelerated learning processes
  158. Dynamic display process
  159. Dynamic offers
  160.  
  161. EarthLink
  162. EBay
  163. Effectiveness, measures of
  164. Effect size
  165. Efficiency, measures of
  166. Egg
  167. Elimination and causality
  168. Emotional attachment and loyalty
  169. Emphasis in displays
  170. Empowering
    1. employees
    2. entrepreneurs
    3. salespeople
  171. Engagement in shopper journey
  172. Enterprise data warehouses
  173. Entrepreneurs, empowering
  174. Environmental variables
  175. Evaluation/reward structures, aligning metrics with
  176. Evaluative advertising
  177. Execution of strategy
  178. Executive fiat, determining tolerance range by
  179. Executive sponsorship
  180. Expectancy Theory of Motivation
  181. Experiential advertising
  182. Experimental design, multifactorial
  183. Experiments
    1. decisions and
    2. strategic, exploiting
  184. Explanation in displays
  185. External factors and performance
  186.  
  187. Facebook
  188. Face validity
  189. Failing fast
    1. benefits of
    2. experiments and
    3. Microsoft case study
  190. Failure as way of learning
  191. Field-strategy linkages, importance of
  192. ForeSee
  193. Forrester Research Inc.
  194. Furniture manufacturing
  195.  
  196. Gains, maximum, for customer-facing Big Data solutions
  197. Gallery Furniture, progressive profiling of
  198. Gerstner, Lou
  199. Global implementation
  200. Google
  201. Granularity of data
  202. Greenberg, Paul
  203.  
  204. Harrah's
  205. Harvard, case-teaching method at
  206. Heat maps
  207. Hero ethos
  208. Hilton Honors
  209. Hospital mortality rates and Facebook likes
  210. Hotels
  211. Hype about Big Data
  212. Hypotheses, testing
  213.  
  214. IBM
  215. Idea generation
  216. Implementation
    1. in functional area or department
    2. global
    3. managing
    4. separating strategy from
  217. Individual transactions, value of accelerated learning in creating
  218. Inferential precision
  219. Infinity
  220. Innovations
    1. accelerated learning and
    2. diffusion of innovations curve
    3. near-simultaneous adoption of multiple
    4. in technology, adopting multiple
  221. Insight Technology Group
  222. Inventory of data assets
  223. Investment risk
  224.  
  225. JC Penney
  226. Jeffery, Mark
  227. Jive
  228.  
  229. Kaus, Phil
  230. Konica-Minolta Business Solutions
  231.  
  232. LaCugna, Joe
  233. Language, common, for organization
  234. Lasker, Albert
  235. Launch strategy, planning
  236. Leadership
    1. exploiting strategic experimentation
    2. movements and
    3. organizational culture and
    4. overview of
  237. Lead scoring
  238. Learn and act
  239. Learning processes, accelerated
    1. cascading campaigns and
    2. in creating transactions
    3. micro-segmentation
    4. in new product design
    5. for organizations
    6. overview of
    7. using data for
    8. value of
  240. Lifestyle data
  241. Lindblom, Charles
  242. LinkedIn
  243. Logistic regression
  244. Lone Star Park case study
  245. Loss, avoiding
  246. Loyalty
    1. airlines and
    2. attitudinal
    3. behavioral
    4. benefits of
    5. conceptual map for
    6. correlation between customer experience and
    7. customer lifetime value and
    8. making benefits of transparent
    9. measures of
    10. Microsoft training program and
    11. operational definition of
    12. as past behavior
    13. production analytics and programs for
    14. simple model of
  247.  
  248. Machine data
  249. Mac's Convenience Stores
  250. Magic Quadrant score
  251. Managers, data-capable
  252. Mapping maturity curves
  253. Marketing
    1. Big Data investment and
    2. buzzwords in
    3. list of Big Data barriers of
    4. proximity
  254. Marketing ploy, Big Data as
  255. Market level, responsiveness at
  256. Matching data to models
  257. Math avoidance, as barrier to Big Data and Dynamic Customer Strategy
  258. Maturity curves, mapping
  259. McIngvale, James
  260. McKinsey Global Institute
  261. McPhaul, Sam
  262. Measures
    1. of customer experience
    2. of data quality
    3. of effectiveness
    4. of efficiency
    5. of loyalty
    6. of success
  263. Meredith Corp.
  264. Metric data
  265. Metrics
    1. aligning with evaluation/reward structures
    2. application of Big Data to
    3. assumptions and
    4. new, creating
    5. right, creating
    6. selecting
    7. tolerance range
    8. variation and performance
    9. visualization
  266. Micro-segmentation
  267. Microsoft
  268. Mindless discounting, avoiding
  269. Mindset, Dynamic Customer Strategy as
  270. Mintzberg, Henry
  271. Mission statements
  272. Mitsubishi
  273. Model myopia
  274. Models
    1. attribution
    2. behavioral loyalty
    3. customer lifetime value
    4. in Dynamic Customer Strategy
    5. matching data to
    6. operationalizing
    7. of persuasion
    8. predictive, and human insight
    9. propensity
    10. statistical, applications of
    11. See also Conceptual mapping
  275. Model training
  276. Monitoring
    1. conversions
    2. tolerance range
    3. See also Tolerance range; Tracking; Visualization
  277. Motion, putting data into
  278. Motivational data
  279. Movement leadership
  280. Multifactorial experimental design
  281. Multiple technology innovations, adopting
  282.  
  283. Near-simultaneous adoption of multiple innovations
  284. Negotiation
  285. Net Promoter Score (NPS)
  286. Newton, Isaac
  287. Nielsen
  288. Nike Plus site
  289. Nonmetric data
  290. Norms and Dynamic Customer Strategy/Big Data culture
  291. Not-invented-here (NIH) syndrome
  292. NPS (Net Promoter Score)
  293. Numbers myopia
  294. Numeric values, assigning to variables
  295.  
  296. Offers
    1. crafting
    2. dynamic
  297. Oh, Seja (Dwyer, Schurr, and Oh matrix)
  298. Operational analytics
  299. Operational definitions
  300. Operationalizing
    1. conceptual mapping
    2. models
  301. Operationalizing strategy
    1. definitions for
    2. experiments and decisions
    3. managing decision risk
    4. Microsoft case study
    5. overview of
    6. using Big Data effectively
  302. Operational map for Dynamic Customer Strategy
  303. Operational velocity, increased
  304. Opportunity risk
    1. commitment, availability bias, and
    2. failing fast and
    3. as Type II error
  305. Opportunity seeking
  306. Opportunity versus strategy
  307. Optimal Design
  308. Order generation, speed of
  309. Organizational culture
  310. Organizational learning
  311. Organizational transactions
  312. Orthogonal data sources
  313. Overstock.com
  314.  
  315. “Path to purchase”
  316. Payback, tracking
  317. Pedowitz Group
  318. People and building absorptive capacity
  319. Performance
    1. concept map of
    2. in conceptual map for loyalty and CLV
    3. definition of
    4. as speed of service
    5. tolerance range for
    6. value, propensity to relate, and
    7. value and
    8. variation and
  320. Personal value equation
  321. Persuasion, Aristotle model of
  322. Piloting systems
  323. Pivoting business focus
  324. Pizza Hut
  325. Planning launch strategy
  326. Policies and procedures
  327. Porter, Michael
  328. Porter's Five Forces
  329. Precision of data
  330. Predictive models and human insight
  331. Pregnancy campaign of Target
  332. Preparing data
  333. Pringles
  334. Process
    1. analytics-based, of organizational learning
    2. building absorptive capacity and
    3. customer strategy as
    4. dynamic display
    5. snowballing
    6. See also Accelerated learning processes
  335. Process satisfaction
  336. Product design, value of accelerated learning in
  337. Production analytics
  338. Product satisfaction
  339. Professional development
  340. Progressive profiling
  341. Project management, change management compared to
  342. Propensity models
  343. Propensity to relate
  344. Proximity marketing
  345. Psychodemographic data
  346. Purchase/consumption cycle
  347. Purpose and opportunistic decision making
  348. $P$-value
  349.  
  350. Quality of data
  351. Questions about data
  352.  
  353. Ramamurthy, Ram
  354. Rand, Bill
  355. Rand categorization of displays
  356. Random effects
  357. Ranking versus rating
  358. Raynor, Michael
  359. Real-time displays
  360. Recency, frequency, monetary value (RFM) scoring
  361. Reducing risk, experiments as
  362. Regression analysis
  363. Reichheld, Fred
  364. Relationships
    1. causal versus correlational
    2. overview of
  365. Reporting analytics
  366. Reporting exchanges
  367. Resources and absorptive capacity
  368. Response, accelerated
  369. Responsiveness
    1. in conceptual map for loyalty and CLV
    2. customer experience and
    3. definition of
  370. Results, determining causes of
  371. Retailers
  372. RFM (recency, frequency, monetary value) scoring
  373. Risk and decision making See also Opportunity risk
  374. Rites of passage
  375. Ritz-Carlton
  376. Roeper, Richard
  377. Role of Big Data
  378. Roosevelt, Teddy
  379. Royal Bank of Canada
  380.  
  381. Salesforce.com
  382. Sales intelligence (SI) companies
  383. Sales life of customers
  384. Salespeople
    1. acquisition of data and
    2. attribution modeling and
    3. empowering
    4. involving in process
  385. Sample size
  386. Sampling and data acquisition
  387. Sandboxes
  388. SAS Institute
  389. Satisfaction, operational definition of
  390. Scenario testing
  391. Schaars, Theo
  392. Schultz, Howard. See also Starbucks
  393. Schurr, Paul (Dwyer, Schurr, and Oh) matrix
  394. Scouts
  395. Segmentation and data assets inventory
  396. Selling the way buyers want to buy
  397. Sentiment analysis
  398. Share of wallet
  399. “Shopper journey,”
  400. Simplification of displays
  401. Skewed distribution and tolerance range
  402. Skunk-works approach
  403. Sleep and decision making
  404. Slevin, Dennis
  405. Snowballing process
  406. Social media. See also Facebook; Twitter; YouTube
  407. Social world, leadership in
  408. Sourcing data
  409. Speed of service
  410. Split-half tests
  411. Starbucks
  412. Static displays
  413. Statistical control
  414. Statistical models, applications of
  415. Statistical packages
  416. Statistical power
  417. Statistics, to reduce risk
  418. Stories and culture
  419. Strategy
    1. absorptive capacity and
    2. for data acquisition
    3. execution of
    4. linkages between field and
    5. moving to action from
    6. opportunity versus
    7. separating from implementation
    8. theory as
    9. See also Creating data strategy; Operationalizing strategy; Testing strategies
  420. Streaming insight
    1. achieving state of
    2. analytics cycle
    3. applications of statistical models
    4. data usage and insight matrix
    5. matching data to models
    6. opportunity to create
    7. as pipeline issue
    8. types of data and types of analytics
  421. Streams of data, rates of
  422. Structured data
  423. Structured displays
  424. Success
    1. generating quick
    2. measures of
    3. See also Failing fast
  425. Survey approach to CLV
  426. Sustainable competitive advantage
  427. Switching costs
  428. SWOT analysis
  429. Systematic cause of variance
  430.  
  431. Taguchi Block Design
  432. Target
    1. behavioral loyalty program of
    2. polling inventory data by
    3. production analytics of
  433. Teams, cross-function
  434. Technology companies
  435. Technology innovations, adopting multiple
  436. Teen pregnancy
  437. Teradata
  438. Teradata Partners User Group
  439. Teradata study
  440. Terminology, definitions of
  441. Testing strategies
    1. complicated A/B tests
    2. hypotheses and
    3. sample size
    4. series of A/B tests
    5. simple A/B or split-half
  442. Theoretical foundation for Dynamic Customer Strategy
  443. Theories
    1. of how things work
    2. as strategies
    3. Vroom's Expectancy Theory of Motivation
  444. Theory of Variation
  445. Timeliness of data
  446. Time-starved buyers
  447. Titus, Varkey
  448. Tolerance range
    1. for campaign performance
    2. creating
    3. definition of
    4. monitoring
  449. Tools for building absorptive capacity
  450. Tracking
    1. advertising effectiveness
    2. campaigns
    3. payback
    4. See also Monitoring
  451. Trade shows
  452. Transactional data, and data traps
  453. Transactions, value of accelerated learning in creating
  454. Transparency
    1. in conceptual map for loyalty and CLV
    2. customer experience and
    3. definition of
  455. Trapped approach to data acquisition
  456. Triangulation
  457. Trusting data
  458. Truth and Big Data
  459. Turf, as barrier to Big Data and Dynamic Customer Strategy
  460. Tweaking/tuning
  461. Twitter
  462. Type I and Type II error
  463. Types of data to acquire
  464.  
  465. University rankings
  466. Unstructured data
  467. Unstructured displays
  468. Users, involving in process
  469. US News & World Report's university rankings
  470.  
  471. Validity
  472. Value
    1. of accelerated learning
    2. of data, assessment of
    3. of data assets inventory
    4. performance, propensity to relate, and
    5. performance and
    6. of white paper, determining
  473. Values
    1. Dynamic Customer Strategy/Big Data culture and
    2. opportunistic decision making and
  474. Variables
    1. assigning numeric values to
    2. causal versus correlational
    3. counterfactual
    4. dependent
    5. environmental
  475. Variance, systematic cause of
  476. Variation and performance
  477. Variety
    1. acquisition of Big Data and
    2. avoiding data traps and
    3. Big Data, experiments, and
    4. as dimension of Big Data
    5. inferential precision and
    6. types of data to acquire and
  478. Velocity
    1. acquisition of Big Data and
    2. bias for action and
    3. Big Data, experiments, and
    4. as dimension of Big Data
    5. of learning and action
    6. operational
  479. Virgil
  480. Visualization
    1. benefits of
    2. dynamic display process
    3. Rand categorization of displays
  481. Volume
    1. acquisition of Big Data and
    2. Big Data, experiments, and
    3. as dimension of Big Data
    4. enterprise data warehouses and
    5. inferential precision and
  482. Volvo
  483. Vroom's Expectancy Theory of Motivation
  484.  
  485. Walmart
  486. Wanamaker, John
  487. Washing machines for cheese making
  488. Watson, Susan
  489. Welch, Jack
  490. White paper, determining value of
  491. Wireless service providers
  492. Wood, Steven
  493. Woolf, Jeff
  494. Workarounds, eliminating
  495. Wurtzel, Alan
  496.  
  497. YouTube
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