Marketing Automation

Grzegorz Błażewicz

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ISBN/ISSN:

978-83-01-22237-6

DOI:

Wydawnictwo:

Wydawnictwo Naukowe PWN

Rok wydania:

2022

Liczba stron:

196

XML:ISBN/ISSN:

ONIX MARC21

Bibliografia:

Błażewicz, Grzegorz. Marketing Automation. Red. . : Wydawnictwo Naukowe PWN, 2022, 196 s. ISBN 978-83-01-22237-6

Bibliografia refWorks:

RT Book, Whole

SR Electronic(1)

A1 Błażewicz, G.

T1 Marketing Automation

PB Wydawnictwo Naukowe PWN

YR 2022

SN 978-83-01-22237-6

Bibliografia BibTex:

@Book{ 268557, author = "Błażewicz, Grzegorz", editor = "", title = "Marketing Automation", publisher = "Wydawnictwo Naukowe PWN", year = "2022", address = "", isbn = "978-83-01-22237-6" }

Bibliografia endNote:

TY - BOOK

AU - Błażewicz, Grzegorz

ED -

TI - Marketing Automation

PB - Wydawnictwo Naukowe PWN

CY -

PY - 2022

SN - 978-83-01-22237-6

ER -

Netografia (standard APA):

Błażewicz, Grzegorz. Marketing Automation [baza danych online] : Wydawnictwo Naukowe PWN, 2022 [dostęp: 23 07 2024]. Dostęp w Ibuk Libra: https://libra.ibuk.pl/reader/marketing-automation-grzegorz-blazewicz-268557.

marketing, Marketing Automation, artificial intelligence, Hyperpersonalization

During the COVID-19 pandemic, 76 percent of customers permanently changed their buying patterns, McKinsey found in their report. That brings great challenges for marketers, including the need to restore true trusting relationships with each and ev...

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ISBN/ISSN:

978-83-01-22237-6

DOI:

Wydawnictwo:

Wydawnictwo Naukowe PWN

Rok wydania:

2022

Liczba stron:

196

XML:ISBN/ISSN:

ONIX MARC21

Introduction13
Marketing autoMation philosophy15
1. Marketer’s new challenges17
Mobile first20
An even newer consumer23
Hyper-personalization26
GDPR and user tracking28
The end of the cookie era31
Transparency in data collection32
2. Two revolutions in marketing 33
Processes and codification of knowledge34
Big Data, machine learning and artificial intelligence36
3. Who are we and why was this book written?39
4. Artist’s shit – excuse my language, but it actually happened41
Part I Revolution 1.0 a few words about the codification of processes in Marketing43
5. From sales funnel automation to AI – the story of automation platforms45
Automation in B2B and B2C45
When everyone follows #marketingautomation48
Focus on customer data49
Marketing Automation versus Customer Data Platform52
6. The most important functions of the Marketing Automation system, namely the foundations of Revolution 1.058
Anonymous identification and 360-degree profiles58
Automatic contact segmentation62
Events, conditions, actions66
Single channel, multi-channel, omnichannel68
1-1 personalization and dynamic content70
A smarter outbound71
Cross-channel analytics74
7. Marketing automation: facts and myths 77
Marketing automation is an area exclusively for marketers77
Marketing Automation is just software79
You don’t have a customer base – automation is the best solution80
Marketing Automation is only for the largest companies82
If you want a good implementation, hire an agency84
Part II Revolution 2.0 how big data shapes Marketing 87
8. Taming Big Data89
The 7 Vs of Big Data91
The modern Big Data market94
Big Data trends96
9. Why does Google know better? And doesn’t it know too much? 99
10. Information chaos 102
11. Correlation instead of causality 104
12. Everything is data 108
13. The algorithm will choose a series and compose a playlist for you110
14. Data as company capital 113
15. The use of Big Data in marketing 116
How it’s working? How is the data collected?117
What data can I collect and what should I collect?121
Data ethics123
Part III ArtificiAl intelligence in mArketing125
16. A brief history of algorithms and machine learning 127
17. Machine learning or artificial intelligence129
18. What artificial intelligence can do131
19. What artificial intelligence cannot do and will it be able to do?134
20. Should we be afraid of artificial intelligence?138
21. Types of machine learning algorithms and their application in marketing 140
Collaborative filtering141
Neural networks144
Deep learning145
Regression algorithms147
Decision trees148
22. Artificial intelligence in marketing 149
Chatbots150
Better, personalized search results151
Virtual assistant153
Automated e-mail155
Database segmentation157
Welcome and birthday coupons158
Push notifications160
Product recommendations162
Video Body Language Monitoring163
23. Machine learning limitations 166
Part IV A new way of Marketing technologies167
24. Personalization versus hyper-personalization169
25. A new approach to measuring efficiency in e-commerce173
26. Deep Behavioral Profiling 175
27. How to build an offer based on data that is not yet available 177
28. Marketing based on artificial intelligence predictions179
Prediction of the customer’s life value180
Purchase prediction182
Churn prediction183
Prediction of the optimal time and communication channel185
Bibliography187