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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.

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.

21
Q

User-based framing: similarities among customers (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.

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

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.

Laurin (3 decks)

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