Digital Technologies and Marketing Flashcards
What is web scraping
Using application programming interfaces (APIs) to collect data from the internet
Benefits of web data (3)
- Enormous size
- Publicly available
- Cheap to access
Addressing validity struggles of web data (2)
Addressing both technical and legal/ ethical concerns
Matrix of for types of data (4)
Data format: structured/ unstructured
Data Source: external/ internal
Big data (3)
- Volume
- Velocity
- Variety
The internet of things (IoT)
Physical objects that connect and exchange data with other devices through a network.
Benefits of web scraping (4)
- Study new phenomena: new areas of research and faster turnaround
- Ecological value: more controlled real-life data without external involvement
- Methodological advancement: new types of data, new ways to process them.
- Improving measurement: new or more detailed variables.
4 Dimensions of online customer experience
- Informativeness
- Entertainment
- Social presence
- Sensory appeal (stimulates sight and sound)
-> Ultimately impacting purchase decision
Moderators of online customer experience (2)
Product (search vs. experience)
Brand (trustworthiness)
Web design A/B testing
Comparing two versions to see which one works
Letting multiple ads run and see which one performs best
Multivariate testing
Tests multiple features at the same time
Useful for relative/ interaction effects
Full factorial design
Tests all possible combinations, useful to test for interactions
Multi-touch attribution (MTA)
- Consumers can have many (MTA) before making a purchase
- Touch points are very different and their efficiency is difficult to determine
First touch/ click
The purchase happens with the first contact.
Last touch/ click
The purchase happens with the last time they are in touch
Linear attribution
The probability of purchase is the same with every interaction.
Time decay attribution
The more touchpoints happened the higher the probability.
Macro-influencers: and high-arousal
-> Arousal for macro-influencers decreases engagement (monetary interests)
-> Informative goal for macro-influencers leads to more engagement; even more so when arousal is high (monetary interests are less apparent)
Use of high-arousal language by macro-influencers can lead to a decrease in engagement, it is perceived as being overly commercial and decreases trustworthiness. It is preferred to provide informative content rather than solely promoting.
Micro-influencers:
-> Arousal for micro-influencers increases engagement (genuine excitement)
Benefit from the use of high-arousal language (“its totally AMAZING”). IT contributes to a higher level of engagement from the audience. It provokes trustworthiness and authenticity. It is perceived as being more genuine, which in turn fosters a stronger connection and encourages more interaction with the content. This effect highlights the importance of perceived sincerity and enthusiasm in micro-influencer marketing strategies.
Bass model: (3)
- Differentiations between innovation (p) and imitation (q).
- Innovation signals consumers willing to test new things
- Imitation signals social contagion
- Driving imitation can greatly increase diffusion
- Social media can great increase social contagion
Advantages of user generated content to gain insights into market structures and consumer perceptions (3)
- no traditional consumer surveys needed
- low cost
- high resolution
Challanges of user generated content (3)
- unstructured nature
- qualitative
- noisy
Lift (based on occurrence & co-occurrence) Text analysis models
- Lift between words A&B: lift (A,B) = P(A,B)/ P(A)*P(B)
how often they appear vs. how often they appear together.
TF-IDF (term frequency – inverse doc frequency) Text analysis models
- For word i in document j: TFIDF ij = TFij * IDFj
- Useful for identifying which words are meaningful
- Often used for search engines (google)
- Can be used as weighting for other methods
Cosine similarity; Text analysis models
- Can be used to check similarity of reviews
- Can be sentences, reviewers, or word pairings over reviews
Sentiment analyses (dictionary, basis); Text analysis models
- Uses other methods and sees how often the brand name is used with the word “good”
- it can also be used directly on your won specific reviews
- Positive words get +1 and negative -1
- Control the length of the review
–> Can also use AI applications
Explicit recommendations
approval for OTHERS
- using words like: recommend/suggest
Implicit recommendations
declaration for your SELF
- using words like: I like/ I enjoy/ my favorite novel
What influences the effect of word of mouth(3)
- the language chosen (implicit/ explicit)
- liking/credibility of the sender
- the mediumof WOM
Manager responsing to negative reviews
Positive effect
- Tailored responses to negative reviews amplify positive impact.
Manager responses to positive reviews
Negative effect
- Responses to personalized positive reviews seem fake/ too promoting
Social proof (copying actions from others) (3)
- Psychological phenomenon where people copy actions from others
- Social proof is powerful and omnipresent (widespread)
- Many examples in both offline and online setting
Influence of responding to reviews (3)
- Might create a connection with the reviewer
- Might signal something to other customers
- Can decrease negative reviews (more cost), or increase (expect response)
Preferred algorithms
when it is more facts based.
Preferred human judgment (vs. algorithms)
when it is subjective, emotional or about personal preference.
Disadvantages of using algorithms (2)
- reduces human creativity
- reinforces human inequalities
Transparency vs. ad effectiveness (in terms of targeting of customer)
- transparency regarding acceptable information use increases likelihood of engagement with ads.
privacy concerns and the desire for personalization
- higher likelihood to click on ads when information was gathered within the website
Key findings (Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases)
- Chatbot Disclosure’s Impact: sales 80% when the customer knows they are talking to a chatbot
- Human Perception Plays a Role: Sales drop because people think that bots are less knowledgeable and emphatic
Strategies to Mitigate Negative Impact (Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases)
- delaying disclosure of Bot as long as possible
- Enhancing AI experience before, people with AI experience like chat bots more.
Step 1: Item-based recommendation
Demeaning - calculate the mean of the user and then take every value minus the mean
Step 2: Item-based recommendation
v are all value from the top
r are all demanded values
-> How important the features are for the participant
Step 3: Item-based recommendation
- consider all attributes in one calculation
- Be careful and consider the multiplication in the square root
Step 1: user-based recommendations
- demean all the value based on the participant
A. Lift (based on occurrence & co-occurrence)
-> how often they appear vs. how often they appear together.
TF ij (def)
How many times a word appears in this specific comment (indicating prminence)
IDF𝑖 = log(𝑁/𝑛𝑖) (2)
𝑁: the total number of comments
𝑛𝑖: in how many comments the word appears, it does not matter if that happens more than one time, it is just about the total number of comments with that word (indicating rarity).
Interpretation Lift
Lift > 1 -> positive association
Lift < 1 -> no association
TF-IDF Score interpretation
A measure of the importance of a word in a document relative to a collection of documents.
Three stages collecting web data
- Selecting data sources
- Designing collection
- Extracting data
Verbal Elements - Webpage Design Elements (4)
- Linguistic style
- Descriptive product detail
- Bulleted product features
- Return policy information
Visual elements - Webpage Design Elements (4)
- Product feature crop
- Lifestyle photo
- Photo size
- Product video
Verbal/ Visual Elements - Webpage Design Elements (4)
- Customer star ratings
- Expert endorsement
- Comparison matrix
- Recommendation agent
- Content filter
Search products (2)
- attributes can be easily be evaluated before the purchase
- informativeness is more important
Experience products (2)
- Quality or satisfaction level can only be assessed after use
- Socia experience is more important
Brand (trustworthiness) characteristics (2)
- Entertainment and customer customized content is more important if trustworthiness is low.
- Information is more important if trustworthiness is high.
Characteristics of big influencers (4)
- Reaches larger audience
- Generate more sales
- Benefit from promoting a limited number of products
- Trustworthiness decreases the more influencers promote the same product
Characteristics of small influencers (3)
- More effective at increasing the audiences conversation rate
- Small-influencer-only strategy is dominant
- Seem more relatable and approachable to normal people
Using small and big influencers (2)
- Strong negative effect on big influencers due to decreased trust
- When using both influencers, the advertisement campaign does not seems genuine anymore.
Influencer goal (4)
- Creating awareness
- Encouraging trial/ purchase
- Informative motives are less clearly linked to advertising
- Commercial motives are more clearly linked to advertising
How much does a big influencer increases sales
+ 259.7%
How much does a small influencer increases sales
+18.5%
Content-based filtering
recommends content to users on the basis of topic they already subscribed to
Social filtering algorithm
Recommends content with which users’ social connections are already engaged
Effects of shifing from content-based to social-based algorithm
shifted online activity from content-oriented to social-oriented, resulting in a rich-get-richer effect.
Moved from content-based filtering to social filtering
Main effects (2)
- Reduced question subscription (subscribed to get answer to a topic) by 20% and reduced answer contribution (subscribed to give answers) by 23%
- Socia interactions increased by 15%, more social relationships
