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Mastering Marketing Data Science
book

Mastering Marketing Data Science

by Iain Brown
April 2024
Beginner to intermediate
432 pages
12h 45m
English
Wiley
Content preview from Mastering Marketing Data Science

Index

  • 2D space, word embeddings, 181f
  • A
  • A/B test/testing, 4, 12, 95–101, 215, 231, 351
    • analysis, 327
    • experimental design, 97, 97t
    • fractional factorial designs, example, 314–315
    • landing pages usage, 229
    • online A/B testing, challenges/benefits, 321
    • outcomes, comparison, 315t
    • results, 101, 233f
    • scenarios, results (comparison), 96f
    • usage, 117–118, 124–127, 230
    • virtual A/B testing, 369
  • Actionable insights, cross-channel behavior analysis (impact), 288
  • Activation functions, 141
  • Ad campaign optimization, 318
  • Addition rule, 86
  • Ad optimization, fractional factorial design, 328–329
  • Ad spend/sales, correlation, 58f
  • Advanced AI, role, 397
  • Advertising, 245, 245f, 383
  • Advocacy (customer journey stage), 277
  • Agglomerative clustering, 109
  • Aggregation, 34, 37–39, 137
  • Algorithmic approaches, 257, 258
  • Algorithmic attribution model, 253, 253t
  • Algorithmic bias, 394
  • Algorithmic fairness, principles, 393
  • Algorithmic multi-touch attribution models, 258t
  • Algorithms, 210, 316–317, 384, 395
  • Aliasing (fractional factorial design concept), 314
  • Amazon Web Services (AWS), 341, 342
  • Ambiguity, NLP challenge, 177
  • Analysis of variance (ANOVA) (parametric test), 89, 103–104
  • Analytics
    • capabilities, 256
    • types, 51t
  • Analytic techniques, 22
  • Apache Kafka (real-time analytics tool/platform), 344, 346
  • Apache Spark, 337–339, 339f, 340f, 342–343
  • Apache Storm (real-time analytics tool/platform), 344
  • App analytics, journey mapping tool, 277
  • Apple Store, observational study (case study), 24
  • Application programming ...
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Publisher Resources

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