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Voorbeeld
  2.  
  3. Universiteit Van Amsterdam (UvA)
  4.  
  5. Digital Marketing & Analytics

Samenvatting

Summary Digital Marketing Analytics - Lecture & Article Summaries

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 5 keer verkocht

Please note that while these notes do follow the Lecture & Tutorial Schedule, they have been moved up and down in certain areas to ensure clarity. It is designed to help understand the concept as well as answer questions that may pop up in the exam. Some parts may be more concise than you expect.

Voorbeeld 4 van de 49  pagina's

Document informatie

  • 17 december 2021
  • 49
  • 2021/2022
  • Samenvatting

Onderwerpen

  • digital marketing amp analytics

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Voorbeeld van de inhoud

DIGITAL MARKETING &
ANALYTICS
Course Summary

Note
Please note that while these notes do follow the Lecture &
Tutorial Schedule, they have been moved up and down in certain
areas to ensure clarity. It is designed to help understand the
concept as well as answer questions that may pop up in the exam.
Some parts may be more concise than you expect.




Oscar Wilde

,Table of Contents
DIGITAL LANDSCAPE _____________________________________________________________________________ - 3 -
What makes digital media different? _________________________________________________________________________ - 3 -
CUSTOMER JOURNEYS ____________________________________________________________________________ - 3 -
Touchpoint _____________________________________________________________________________________________ - 3 -
Role of Touchpoint _______________________________________________________________________________________ - 3 -
Paid-Owned-Earned Media (POEM) __________________________________________________________________________ - 3 -
ARTICLE 1.3 - Zaremba. (2020). OPEC Model and Its Consequences _________________________________________________ - 4 -
DIGITAL DATA TYPES _____________________________________________________________________________ - 4 -
Classification 1: Structured vs. Unstructured ___________________________________________________________________ - 4 -
Classification 2: Source of Data _____________________________________________________________________________ - 5 -
Digital Data with Emotions and Behavior ______________________________________________________________________ - 5 -
ARTICLE 1.1 – Matz et al (2017) _____________________________________________________________________________ - 5 -
DIGITAL MARKETING METRICS _____________________________________________________________________ - 6 -
Top 10 Metrics used by marketeers __________________________________________________________________________ - 6 -
What are the best metrics to review and evaluate? _____________________________________________________________ - 6 -
Basic Metrics ____________________________________________________________________________________________ - 7 -
Conversion _____________________________________________________________________________________________ - 8 -
ATTRIBUTION ___________________________________________________________________________________ - 8 -
Attribution Problem ______________________________________________________________________________________ - 8 -
Attribution Methods: Simple, Rule Based, Data Driven ___________________________________________________________ - 9 -
Analyzing Attribution Models ______________________________________________________________________________ - 11 -
Attribution Methods: Which one to use? _____________________________________________________________________ - 15 -
Attribution: Why Do We Pay So Much Attention? ______________________________________________________________ - 15 -
Challenges in Attribution _________________________________________________________________________________ - 15 -
CROSS-CHANNEL MANAGEMENT: Online & Offline Touchpoints _________________________________________ - 17 -
Evolution: from Multi-Channel to Cross/Omnichannel __________________________________________________________ - 17 -
Omnichannel differs from multi-channel as: __________________________________________________________________ - 17 -
Online, Offline and Mixed-Mode Journeys ____________________________________________________________________ - 17 -
Cross Effects in Attribution: Carry-Over & Spill-Over Effects ______________________________________________________ - 17 -
CUSTOMER JOURNEY MANAGEMENT ______________________________________________________________ - 18 -
Research Shopping: ______________________________________________________________________________________ - 18 -
Customer Journeys in Omni-Channel Landscape _______________________________________________________________ - 18 -
Customer Journey Design _________________________________________________________________________________ - 19 -
ARTICLE 2.3 – Li et al. (2020) Impact of Product Types on Customer Journeys ________________________________________ - 19 -
ARTICLE 2.2 – De Haan (2018) – Impact of device switching on Customer Journeys ___________________________________ - 20 -
GOOGLE ANALYTICS: ____________________________________________________________________________ - 21 -
Difference between Google Analytics and Google BigQuery ______________________________________________________ - 21 -
ARTICLE 1.2 – Pakkala et al (2012) - Use of Google Analytics. _____________________________________________________ - 21 -
SCRAPING _____________________________________________________________________________________ - 21 -
Scraping: Tools _________________________________________________________________________________________ - 22 -
Scraping: Basic Tool – how to use? __________________________________________________________________________ - 22 -
SENTIMENT (SEMANTIC) ANALYSIS_________________________________________________________________ - 22 -
Types of Sentiment Classifiers _____________________________________________________________________________ - 22 -
Sentiment Analysis: Subjectivity – Polarity____________________________________________________________________ - 23 -
Sentiment Analysis: Sarcasm Detection ______________________________________________________________________ - 23 -
Beyond Positivity: Sentiment towards Semantics ______________________________________________________________ - 23 -
TOPIC MODELING / TOPIC ANALYSIS _______________________________________________________________ - 23 -
SENTIMENT & TOPIC ANALYSIS: Use in Combination __________________________________________________ - 24 -
Sentiment & Topic Analysis: Tools __________________________________________________________________________ - 24 -

- 1 -

,SOCIAL MEDIA _________________________________________________________________________________ - 25 -
What are people doing on social media? _____________________________________________________________________ - 25 -
Enhancing the impact of social media _______________________________________________________________________ - 26 -
SOCIAL MEDIA: Media Metrics ____________________________________________________________________ - 26 -
Simple Social Media Metrics _______________________________________________________________________________ - 27 -
How can you make posts more popular (on Facebook)? _________________________________________________________ - 27 -
ARTICLE 2.4 – Shahbaznezhad et al (2021) – Elements that impact engagement on social media ________________________ - 28 -
SOCIAL MEDIA: Data Analytics ____________________________________________________________________ - 28 -
Unstructured Data: Text / Image / Video Data_________________________________________________________________ - 29 -
ARTICLE 3.3 – Klostermann et al (2018) – Extracting brand information from social networks: Integrating image, text, social tags _ -
30 -
ARTICLE 3.4 – Li & Xie (2020) – Is a picture worth a thousand words? An empirical study of image content and social media
engagement ___________________________________________________________________________________________ - 30 -
ARTICLE 3.2 – Aleti et al (2019) – Effect of celebrity social media posts on Word of Mouth (through text analysis) __________ - 31 -
ONLINE RATINGS & REVIEWS _____________________________________________________________________ - 31 -
Online Review / Rating Analytics ___________________________________________________________________________ - 31 -
Online Review components _______________________________________________________________________________ - 31 -
Online Review / Rating platforms ___________________________________________________________________________ - 32 -
Text Reviews: Value and Use ______________________________________________________________________________ - 32 -
Impact of Reviews _______________________________________________________________________________________ - 32 -
ARTICLE 4.1 - Vana & Lambrecht (2021) – Online reviews on Purchase likelihood _____________________________________ - 33 -
ARTICLE 4.2 – Grewal & Stephen (2019) – Effects of Mobile vs. Non-Mobile reviews on Consumer purchase intentions. ______ - 33 -
ARTICLE 4.3 – Srivastava & Kalro (2019) – Enhancing the helpfulness of Online customer reviews _______________________ - 33 -
What other factors make the review perceived as more helpful? _________________________________________________ - 34 -
Should Firms respond to Reviews / Does it pay off? ____________________________________________________________ - 34 -
SEARCH BEHAVIOR ______________________________________________________________________________ - 34 -
Search Intent Funnel _____________________________________________________________________________________ - 34 -
Search Marketing _______________________________________________________________________________________ - 35 -
SEO & SEA: Complementary Tools __________________________________________________________________________ - 35 -
Search Engine Optimization (SEO) __________________________________________________________________________ - 35 -
Search Engine Advertising (SEA) – paid search / sponsored search ________________________________________________ - 36 -
ARTICLE 4.4 - Klapdor, S. et al. (2014). Influence of keyword characteristics on SEA ___________________________________ - 38 -
DIGITAL ADVERTISING: PRICING MODELS ___________________________________________________________ - 39 -
Calculating Costs ________________________________________________________________________________________ - 40 -
PERSONALIZATION & TARGETING __________________________________________________________________ - 41 -
Levels of Personalization__________________________________________________________________________________ - 41 -
ARTICLE 5.1 – Impact of ECR _______________________________________________________________________________ - 42 -
Personalized Targeting: Recommendation Engines _____________________________________________________________ - 43 -
Types of Recommendation Engines _________________________________________________________________________ - 43 -
ARTICLE 5.2 - Gai & Klesse (2019). Making recommendations more effective through framings _________________________ - 44 -
ARTICLE 5.3 – Lee & Hosanager (2019) – How do Recommender Systems affect sales diversity? _________________________ - 44 -
MOBILE: New Dominating Platform ________________________________________________________________ - 44 -
Mobile: What makes mobile different (than desktop/laptop)? ____________________________________________________ - 45 -
Mobile: Effect of Device __________________________________________________________________________________ - 45 -
Mobile: Targeting _______________________________________________________________________________________ - 46 -
MOBILE APPS __________________________________________________________________________________ - 47 -
ARTICLE 6.2 – Gokgoz et al (2021) – Drivers of Mobile App Downloads _____________________________________________ - 47 -
ARTICLE 6.1 – Liu et al. (2019) – To Have or Not to Have an App? _________________________________________________ - 48 -
ARTICLE 6.3 - Lee, Zhang, & Wedel (2021) – Free or Paid? _______________________________________________________ - 48 -




- 2 -

, DIGITAL LANDSCAPE

Online data getting more and more complex and challenging now. But the data is
also getting rich, comprehensive, and diverse to better understand, predict
and manage customers’ behavior.

What makes digital media different?
• Personalization (Individualization) and UCG (User Generated Content) is what
makes today’s digital platforms substantially different than traditional
offline marketing.
o Traditional channels like TV, Radio, Newspaper send the same message to
all customers. Digital media gives you the possibility to create unique-
personalized messages.
o Digital media enables a two-way communication between the customer and
company at an individual level that has not been quite as easy or efficient
earlier.
o Digital media allows for information exchange between customers as well
and customers actively generate content and impact/shape-up other
customers and firms’ behavior.


CUSTOMER JOURNEYS
These describe the path of sequential steps and interactions that a customer
goes through with a company, product and/or service. Journeys are typically
broken down into stages and sub-stages and journey maps can be drawn. This
helps business to plan, manage and measure experiences across the customer
journey.

Touchpoint
A point of contact or interaction, especially between a business and its
customers or consumers.
Role of Touchpoint
• Introduce: To introduce / build awareness for the brand / product
• Assist: To assist / enables a transaction
• Convert: To convert a transaction.
PAID
• Paid Search
• Digital Ads

Paid-Owned-Earned Media (POEM)
• Affiliate Marketing
• Social Media Ads
• Email Ads

• Paid Media
where you pay including > advertising, PAID
MEDIA
search engine advertising and promoted
social media content.
OWNED
o To interact with strangers •
•
Website / App
Social Presence
•
EARNED
SEO - Relevant
• Blogs / Forums OWNED EARNED • Social Networks
• Blogs / Forums
• Managed MEDIA MEDIA • Word of Mouth
communities.




- 3 -

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