University of Amsterdam
MSc Business Administration/Digital Marketing
Academic Year 2023/2024
Digital Marketing & Analytics Summary 2023/2024
Content:
➢ Lecture 1: Platforms, Data Types, Metrics, & Conversion-Attribution
o Tutorial 1: Introduction to Data Analysis, Variable Scale Types,
Regression Models & Probabilistic Attribution
➢ Lecture 2: Challenges & Alternatives to Attribution/Marketing Mix
Models: Omnichannel Management
o Tutorial 2 / Part 1: Scraping – Collecting Web Data for Marketing
Analytics
o Tutorial 2 / Part 2: NLP & Text Analytics – Sentiment and Topic
Analysis
➢ Lecture 3: Social Media Marketing & Analytics
➢ Lecture 4: Online Review-Rating Platforms & Analytics – Search
Marketing: SEA & Optimization
➢ Lecture 5: Digital Advertising & Pricing Models – Personalization &
Personalized Targeting - Generative AI & Chat GPT – Practical Overview
➢ Lecture 6: Recommendation Systems – Mobile Marketing: Mobile-Apps
& Geo-Targeting
,Lecture 1: Platforms, Data Types, Metrics, & Conversion-Attribution
Digital landscape:
• Digital marketing and analytics: “half the money I spend on advertising is wasted; the
trouble is, I don’t know which half.”
o Main challenge: Measuring the real IMPACT- no speculating, no wishful-
thinking-we need to know what really matters.
Expanding:
• Online data is getting more and more complex and challenging now.
• The data is also getting rich and diverse to have a better understanding and then,
predict and manage customers’ behavior.
Personalization & User Generated Content:
• Personalization (individualization) and UGC make today’s digital platforms
substantially different from traditional offline marketing.
o TV, radio, newspaper: (non)digital traditional media (same message to all);
o New digital media (unique-personalized messages and more information
exchange between customers).
o Customers actively generate content and impact/shape-up other customers and
firms’ behavior.
Customer journeys and touchpoints:
• All these goals and benefits can be achieved through designing and managing
seamless customer journeys on digital platforms- by using right digital touchpoints
synchronized with offline platforms & touchpoints.
,Touchpoints: Paid-Owned-Earned Media
• Paid-owned earned touchpoints: most widely used classification in marketing &
advertising practice.
• Known and used by most marketeers.
o This is not a must-use & universal recipe, but easy to understand and
implement.
Owned media: i.e. your web site, your app, your social media platforms: where you can post
your own information for ‘free’.
• To interact with customers
• Examples: websites, email marketing, social network pages, communities, SEO,
content.
Earned media: the most difficult, yet a valuable type of media. Word of mouth,
conversations, comments, likes and shares.
• Fans: CRM database, SEO, facebook, youtube, twitter, google, blogs, forums, yelp.
Paid media: where do you pay? Including advertising, search engine and promoted social
media content.
• Used to connect with strangers
• Examples: paid search, display banners, remarketing, purchased email list,
advertorials.
Why do we need such classifications or why don’t we handle each touchpoint in a
granular way?
• POE helps decisions whether the firm should focus on advertising (paid), content &
service (owned), or engagement (earned).
, Paid-Owned-Earned Media: Extension > Category Media
• Category media: relates to the category that do not mention the advertiser’s brand nor
published by the focal brand.
• Captures (i) competitors’ own, paid, and earned media and (ii) independent
publications related to the product category.
• Motivation: control level on earned (individuals) and category (publications,
competitors) different. Control: earned > category.
Digital Data Types & Metrics:
Digital data types: classification 1- Structure
• Data could be structures in nature. The management and analysis of the data depends
on this structure accordingly.
• Structured Data: Mostly Quantitative
(numeric). Examples: sales in euro, click/no
click (0-1), session time, number of likes
etc.
• Unstructured data: mostly qualitative
(textual or visual data): photos/videos,
examples: Instagram videos, online,
reviews, social media posts.
• The importance of unstructured data is
rapidly increasing.
o Recent projections
indicate that
unstructured data is
over 80% of all
business-marketing
data.
Digital data types: classification 2-
Source of the Data:
• On-site data: what you can
see on website: available and
visible to anyone. Extracted
through data/web scraping.
• Clickstream/session data: traffic on website-app: what, when and how people do
online- not visible to visitor (i.e: google analytics).
