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Summary DSMS including articles
- Instelling
- Tilburg University (UVT)
Summary of digital and social media strategies including the required readings of each lecture. Passed my examen using this summary only.
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Voorbeeld 3 van de 20 pagina's
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Voorbeeld van de inhoud
DIGITAL AND SOCIAL MEDIASTRATEGIES
Tilburg University – 2023/2024
MARKETING MANAGEMENT MINOR
,Lecture 1
Digital marke,ng = Marke-ng of products or services using digital technologies, mainly on
the internet, but also including mobile phones, display adver-sing and any other digital
medium
Social media marke,ng = Process of crea-ng (tailored) content to drive engagement,
promote a business and generate revenue
Marke,ng strategy = Refers to a firm’s overall game plan for reaching and engaging with
customers
Digital and social media strategies = Understanding how to reach and engage with
consumers via digital technologies and social media
Managers propose strategic ques,ons that need to be answered:
- Need to be answered -> Can be quan-fied with a number
- Number needs to be credibly constructed
- From correct sta-s-cal analysis of the right data
Tradi,onal data driven marke,ng Gold standard data driven marke,ng
Anchor on data that is available Anchor on decision that needs to be made
Finds a purpose for data Starts from what is unknown
Empowers data analysis / scien-sts Empowers decision making
3 methods to analyze data:
1. Descrip,ve analysis (= summarize characteris-cs of dataset)
2. Causal analysis (= Does A lead to B)
è Hard to evaluate
è Need to compute counterfactuals
è Challenge: Same person cannot both get treatment and not get treatment
3. Predic,ve analysis (=how can I best predict an outcome)
è When A occurs, so does B
Ar-cle 1: DeLangePuntoni
o Discusses the importance of shiYing from a data-driven approach to a decision-
driven approach in data analy-cs
o Data-driven decision making fail to obtain ac-onable insights and desirable outcomes
-> Leads to asking the wrong ques-ons
o ShiY to a decision-driven approach and emphasize the role of managerial judgement
and highlight the unknowns
o Iden-fy the data that is required to support that decision, start with the decision that
needs to be made
, Ar-cle 2: Chintsgunta, Hanssens & Hauser
o Discusses the evolu-on of marke-ng science, adap-on to big data revolu-on and
synergic rela-onship between marke-ng science and computer science
o Highlights the importance of combining marke-ng science with data science and
machine learning -> Provide valuable insights to complex marke-ng challenges in the
age of the big data
Lecture 2
Goal of any marke,ng campaign: Increase sales
𝐼𝑛𝑐𝑟𝑒𝑚𝑒𝑛𝑡𝑎𝑙 𝑝𝑟𝑜𝑓𝑖𝑡 𝑑𝑢𝑒 𝑡𝑜 𝑎𝑑𝑣𝑒𝑟𝑡𝑖𝑠𝑖𝑛𝑔 − 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑎𝑑𝑣𝑒𝑟𝑡𝑖𝑠𝑖𝑛𝑔
𝑅𝑂𝐼 =
𝐶𝑜𝑠𝑡 𝑜𝑓 𝑎𝑑𝑣𝑒𝑟𝑡𝑖𝑠𝑖𝑛𝑔
Incremental sales = Addi-onal sales made due to adver-sing over and above what would
have been sold without adver-sing
Incremental profit = Addi-onal profit made due to adver-sing over and above what would
have been sold without adver-sing
è Typically a func-on of incremental sales
Marke,ng a@ribu,on = Process for determining which marke-ng touchpoints led to a
conversion
A@ribu,on models = Rule / set of rules that determines how conversion credit is assigned to
different marke-ng touchpoints
Rule-based models:
- Last-touch (Last click) = A_ribu-on looks backward from each conversion to find the
last ad that the user saw (or clicked on) prior to the conversion
è Limita,ons of last touch:
1. Other ads may have influenced customer and contributed to the sale
2. All sales are treated incremental
3. Unfairly favors channels that tend to show ads towards the end of the path to
purchase
è When does last touch work?
1. All sales are incremental
2. Effect of ads on behavior is short-lived and ad exposures are spaced out over
-me
- First touch (first click) = A_ribu-on looks backward from each conversion to find the
first ad that the user saw (or clicked on) prior to the conversion
- Linear a@ribu,on = A_ribu-on looks backward from each conversion to find each ad
that the user saw (or clicked on) prior to the conversion, assigning them equal weight
- Posi,on based a@ribu,on = A_ribu-on looks backward from each conversion to find
each ad that the user saw (or clicked on) prior to the conversion, assigning higher
weight to the first and last
- Time decay = Higher weight to more recent ads
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