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Laurin > Digital Technologies and Marketing > Flashcards

Digital Technologies and Marketing Flashcards

1
Q

What is web scraping

A

Using application programming interfaces (APIs) to collect data from the internet

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2
Q

Benefits of web data (3)

A
  • Enormous size
  • Publicly available
  • Cheap to access
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3
Q

Addressing validity struggles of web data (2)

A

Addressing both technical and legal/ ethical concerns

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4
Q

Matrix of for types of data (4)

A

Data format: structured/ unstructured
Data Source: external/ internal

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5
Q

Big data (3)

A
  1. Volume
  2. Velocity
  3. Variety
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6
Q

The internet of things (IoT)

A

Physical objects that connect and exchange data with other devices through a network.

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7
Q

Benefits of web scraping (4)

A
  1. Study new phenomena: new areas of research and faster turnaround
  2. Ecological value: more controlled real-life data without external involvement
  3. Methodological advancement: new types of data, new ways to process them.
  4. Improving measurement: new or more detailed variables.
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8
Q

4 Dimensions of online customer experience

A
  1. Informativeness
  2. Entertainment
  3. Social presence
  4. Sensory appeal (stimulates sight and sound)
    -> Ultimately impacting purchase decision
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9
Q

Multidimensional customer experience

A

It goes beyond these four dimensions

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10
Q

Moderators of online customer experience (2)

A

Product (search vs. experience)
Brand (trustworthiness)

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11
Q

Web design A/B testing

A

Comparing two versions to see which one works

Letting multiple ads run and see which one performs best

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12
Q

Multivariate testing

A

Tests multiple features at the same time

Useful for relative/ interaction effects

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13
Q

Full factorial design

A

Tests all possible combinations, useful to test for interactions

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14
Q

Multi-touch attribution (MTA)

A
  • Consumers can have many (MTA) before making a purchase
  • Touch points are very different and their efficiency is difficult to determine
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15
Q

First touch/ click

A

The purchase happens with the first contact.

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16
Q

Last touch/ click

A

The purchase happens with the last time they are in touch

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17
Q

Linear attribution

A

The probability of purchase is the same with every interaction.

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18
Q

Time decay attribution

A

The more touchpoints happened the higher the probability.

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19
Q

Macro-influencers: and high-arousal

A

-> 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.

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20
Q

Micro-influencers:

A

-> 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.

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21
Q

User-based framing: similarities among customers (increasing factors) (algorithmic recommender systems)

A

-> Works better when experience in a product is high.
-> When users are dissimilar

Framing recommendations as user-based can significantly increase recommendation click-through rates. Recommendation do not only match the product but also matches the tastes of similar customers.

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22
Q

Item-based framing: similarities between products (algorithmic recommender systems)

A

-> Works better when attractiveness is low (or average)

The study finds that item-based framing is less effective in increasing click-through rates copmraed to user-based framing.

23
Q

Mediation effects of user-based vs. item-based (algorithmic recommender systems)

A

Mediation effects
- The recipient’s consumption experience
- The attractiveness of the product
- Suggesting similarity or dissimilarity with other users.

24
Q

Bass model: (3)

A
  • 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
25
Q

Advantages of user generated content to gain insights into market structures and consumer perceptions (3)

A
  • no traditional consumer surveys needed
  • low cost
  • high resolution
26
Q

Challanges of user generated content (3)

A
  • unstructured nature
  • qualitative
  • noisy
27
Q
A
28
Q

Lift (based on occurrence & co-occurrence) Text analysis models

A
  • 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.
29
Q

TF-IDF (term frequency – inverse doc frequency) Text analysis models

A
  • 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
30
Q

Cosine similarity; Text analysis models

A
  • Can be used to check similarity of reviews
  • Can be sentences, reviewers, or word pairings over reviews
31
Q

Sentiment analyses (dictionary, basis); Text analysis models

A
  • 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
32
Q

Explicit recommendations

A

approval for OTHERS

  • using words like: recommend/suggest
33
Q

Implicit recommendations

A

declaration for your SELF

  • using words like: I like/ I enjoy/ my favorite novel
34
Q

What influences the effect of word of mouth(3)

A
  • the language chosen (implicit/ explicit)
  • liking/credibility of the sender
  • the mediumof WOM
35
Q

Manager responsing to negative reviews

A

Positive effect

  • Tailored responses to negative reviews amplify positive impact.
36
Q

Manager responses to positive reviews

A

Negative effect

  • Responses to personalized positive reviews seem fake/ too promoting
37
Q

Social proof (copying actions from others) (3)

A
  • Psychological phenomenon where people copy actions from others
  • Social proof is powerful and omnipresent (widespread)
  • Many examples in both offline and online setting
38
Q

Influence of responding to reviews (3)

A
  • Might create a connection with the reviewer
  • Might signal something to other customers
  • Can decrease negative reviews (more cost), or increase (expect response)
39
Q

Preferred algorithms

A

when it is more facts based.

40
Q

Preferred human judgment (vs. algorithms)

A

when it is subjective, emotional or about personal preference.

41
Q

Disadvantages of using algorithms (2)

A
  • reduces human creativity
  • reinforces human inequalities
42
Q

Transparency vs. ad effectiveness (in terms of targeting of customer)

A
  • transparency regarding acceptable information use increases likelihood of engagement with ads.
43
Q

privacy concerns and the desire for personalization

A
  • higher likelihood to click on ads when information was gathered within the website
44
Q

Key findings (Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases)

A
  1. Chatbot Disclosure’s Impact: sales 80% when the customer knows they are talking to a chatbot
  2. Human Perception Plays a Role: Sales drop because people think that bots are less knowledgeable and emphatic
45
Q

Strategies to Mitigate Negative Impact (Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases)

A
  • delaying disclosure of Bot as long as possible
  • Enhancing AI experience before, people with AI experience like chat bots more.
46
Q

Step 1: Item-based recommendation

A

Demeaning - calculate the mean of the user and then take every value minus the mean

47
Q

Step 2: Item-based recommendation

A

v are all value from the top

r are all demanded values

-> How important the features are for the participant

48
Q

Step 3: Item-based recommendation

A
  • consider all attributes in one calculation
  • Be careful and consider the multiplication in the square root
49
Q

Step 1: user-based recommendations

A
  • demean all the value based on the participant
50
Q

A. Lift (based on occurrence & co-occurrence)

A

-> how often they appear vs. how often they appear together.

51
Q

TF ij (def)

A

How many times a word appears in this specific comment (indicating prminence)

52
Q

IDF𝑖 = log(𝑁/𝑛𝑖) (2)

A

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).

53
Q

Interpretation Lift

A

Lift > 1 -> positive association
Lift < 1 -> no association

54
Q

TF-IDF Score interpretation

A

A measure of the importance of a word in a document relative to a collection of documents.

Laurin (5 decks)

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