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Digital and Social Media Strategies: INTENSIVE Lecture notes
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Extensive lecture notes that helped me pass the exam!
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Lecture 1:Digital Marketing: Marketing of products and services using digital technologies, mainly on the
internet, but also includes mobile phones, display advertisement and other digital medium.
Social media marketing: Process of creating tailored content to
● Drive engagement
● Promote business
● Generate revenue
Marketing Strategy: overall game plan for reaching prospective consumers and turning them
into customers.
DSMS: game plan to reach out to prospects and convert them into customers via digital
technologies and social media.
Strategic Questions:
Can be answered > quantified with a number > number credibly quantified > from correct
statistical analysis of right data
Methods to Analyse Data
● Descriptive analytics (+ Exploratory analysis)
○ Summarises characteristics of data set
○ Results are stylised facts
○ Eg: mean, median, mode, SD
● Causal analysis: Does A lead to B?
○ Think about mechanism of how it happens
○ Might also care about the mechanism of how it happens
■ Causal data driven mkt: What is the impact of a mkt intervention (X) on
outcome (Y)
● Hard to evaluated
● Need to compare counterfactuals
● Challenge: The same person cannot both get treatment and not
get treatment (has to divided target population into groups (A/B
testing)
● Predictive analysis: How can I best predict an outcome?
○ When A occurs → So does B (this is not the focus of this class)
Benefits of using R
● Purpose built statistical software
● Wide variety of statistical and graphical techniques
● Free and open source
,Lecture 2: Marketing Attribution Models - Ad Attribution
● The goals of any marketing campaign is to increase sales, either if the campaign is short
or long term
● To evaluate marketing performance:
1. Incremental sales
- Additional sales made due to
advertising over and above what would
have been sold without advertising
- Incremental profit: Additional profit mad
dude to advertising over and above
what would have been sold without
advertising
2. Marketing Attribution Models
a. Conversion journey
i. Marketing attributions: the
process for determining which marketing touchpoints led to a conversion
ii. Attribution model: rules, or set of rules that determines how conversion
credit is assigned to different marketing touchpoints
b. Food for thoughts: How can I attribute credit to multiple campaigns that contribute
to generating a conversion? Attribution problem assumes data on individual
consumer journeys
i. Approach:
1. Rule based attributions
2. Data driven models
c. What KPIs can we attribute to ads?
i. Brand awareness
ii. Brand knowledge
iii. Brand emotions
iv. Brand positioning
v. Brand attitude
vi. Brand relationship
3. Rule based models
,1. Last touch attribution: looks backward from each conversion to find that last ad
user saw prior to the conversion (Last-clicked)
● Since consumers don't click on ads that much, attributed sales is lower when
using last click attribution
● Simple and ease of use
● Best used when really focused on driving conversions
Limitations:
● Other ads may have influenced customer and contributed to the sale
● All sales are treated as incremental
● Unfairly favours channels that tend to show ads towards the end of path to
purchase
● Unable to track engagement that initially lead consumers to brand
2. First touch attribution: Looks backwards from each conversion to find the first ad
users saw prior to conversion (First-clicked)
○ Easy to implement, simple to understand
○ Does not provide visibility into lower funnel touchpoints
○ Best when focused on demand generations and lead forms
3. Linear Attribution: looks backwards from each conversion to find every ads users
saw prior to conversion, and assigning them equal weight
○ Gives full picture of customer interaction
○ Not all interactions are created equal
○ Best use when touchpoints have relatively equal importance in relation to overall
goal.
, 4. Position based attribution: Look backwards from each conversion to find every ad
users saw prior to conversion and assign higher weight from first AND last.
○ Need to decide on what the higher weight is.
○ Common: 30/40% for both first and last
○ Aka U shape attribution
○ Best to use when curious about specific touchpoints, but want to see full picture
too
5. Time decay attribution: looks backwards from each conversion to find every ad
users saw prior to conversion, assigning higher weight to more recent ads.
○ Assumes later touchpoints has bigger impacts
○ Best to use for lengthy sales cycles eg B2B
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