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MARK 302 > CH10 Marketing Analysis > Flashcards

CH10 Marketing Analysis Flashcards

1
Q

define structured data

A

data that is highly organized and can be represented as tables made up of rows and columns
- rows are observations for a single unit of the population
- columns are variables like age, gender, and income

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2
Q

define unstructured data

A

data or information that is not neatly structured
eg. emails, social media posts, online reviews, photos, and text

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3
Q

what is marketing analytics?

A

marketing analytics: the discovery, interpretation, and communication of meaningful patterns in data

the insights gained from marketing analytics enable marketing managers to better understand their customers and markets

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4
Q

the development of marketing analytics has rapidly accelerated based on what 3 things?

A
  • the emergence of big data
  • advances in analytics tools
  • improvements in information technology (IT)
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5
Q

what is the marketing analytics process?

A
  1. get the data
  2. organize and merge the data
  3. analyze the data
  4. take action based on the data
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6
Q

analyzing data consists of what 3 analytics?

A
  1. descriptive analytics
  2. predictive analytics
  3. prescriptive analytics
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7
Q

define descriptive analytics

A

descriptive analytics: data summarized in basic formats to identify patterns and trends

this is the essence of old school marketing research analytics
- cross tabulation tables are common

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8
Q

define predictive analytics

A

predictive analytics: a more advanced form of analytics that predicts what customers or potential customers will do in response to various marketing programs or classify them into market segments or other subgroups

the data comes from primary, secondary, and big data sources

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9
Q

what are some examples of problems to address when talking about predictive analytics?

A
  • predicting the impact of improving customer service through our call centers on overall customer experience
  • estimating the likelihood that an individual will buy or not buy our new product or service
  • classifying people into market segments
  • predicting which product different consumers are most likely to buy
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10
Q

define prescriptive analytics

A

prescriptive analytics: tells us the best course of action in a given situation

it employs the results of descriptive and predictive analytics to guide decision-making in more specific ways

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11
Q

what are the steps in prescriptive analytics?

A
  1. determine what quantity you want to maximize
  2. list the decision levers available to you
  3. build and calibrate a model that it robust under a wide range of ways of adjusting the decision levers
  4. embed the simulator inside an optimization tool that evaluates a variety of different ways of setting the decision levers and identifies which combination of lever settings maximizes your objectives
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12
Q

what are example of the uses of prescriptive analytics?

A
  • a company could automatically adjust pricing in response to various factors
  • doctors and hospitals could use prescriptive analytics to choose the best treatments for patients
  • airlines can adjust fares for different destinations based on multiple factors
  • fire departments can evaluate where to order evacuations
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13
Q

what is big data?

A

big data: the accumulation and analysis of massive quantities of information that is especially related to human behavior and interactions

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14
Q

what are the 3 Vs associated with big data?

A
  1. volume, referring to the amount of data
  2. variety, concerning putting together data from different sources
  3. velocity, concerning the fact that the data is arriving continuously in data streams
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15
Q

fil in the blank:
big data are more about “___” than “___”

A

big data are more about “what” than “why”

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16
Q

define data mining

A

data mining: an umbrella term for analytic techniques that facilitate fast pattern discovery and model building, particularly with large datasets

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17
Q

define backpropagation

A

backpropagation: a process where AI systems learn from mistakes

18
Q

define neural network

A

neural network: a modeling technique that mimics the processes of the human brain and is capable of learning from experience to find patterns in data

19
Q

define CRISP-DM Framework (Cross-Industry Standard Process for Data Mining)

A

CRISP-DM Framework (Cross-Industry Standard Process for Data Mining): a standardized methodology for conducting a data mining project based on using six steps

20
Q

what are the 6 steps of CRISP-DM Framework?

A
  1. business understanding
  2. data understanding
  3. data preparation
  4. data modeling
  5. evaluation of results and
  6. deployment of a plan
21
Q

what is behavioral targeting?

A

behavioral targeting: the use of online and offline data to understand a consumer’s habits, demographics, and social networks in order to increase the effectiveness of online advertising

22
Q

what is machine learning?

A

machine learning: machines can learn by experience and acquire skills without human involvement, aka neural networks learning by processing vast amounts of data

23
Q

what is deep learning?

A

deep learning: a subset of machine learning where artificial neural networks learn from large amounts of data backpropagation added

24
Q

define artificial intelligence (AI)

A

artificial intelligence (AI): a machine that perceives its environment and takes actions that maximize its chance of successfully achieving its goal

25
Q

give examples of AI

A
  • virtual assistants such as Alexa and Hey Google
  • translation apps; eg. Google Translate
  • vision for cars and other vehicles such as Tesla’s almost self-driving car
  • image colorization; transforming B&W images into color
  • facial recognition
  • medicine and pharmaceuticals; personalized medicines created for an individual’s genome according to their disease or tumor
  • personalized shopping and entertainment; Netflix’s entertainment suggestions or Amazon’s suggestions on what you should buy next
26
Q

define surge/dynamic pricing

A

surge/dynamic pricing: continuously adjusting pricing in relation to demand levels

27
Q

give an example of surge pricing

A

Uber: During periods of high demand for rides, such as during bad weather, rush hour, or special events, Uber may face a shortage of available cars. To address this, Uber implements surge pricing, where prices increase to ensure that those in need of a ride can still get one. This surge pricing system, communicated through the Uber app, gives riders the option to either pay the higher rates for an immediate ride or wait for a potential decrease in prices. This approach allows Uber to maintain its reliability as a transportation option while providing riders with the choice to pay more for immediate service or exercise patience and wait for rates to normalize.

28
Q

define data visualization

A

data visualization: graphic tools that make data understandable to a wider audience than just analysts and data scientists
eg. charts, graphs, photos, videos, and infographics

29
Q

what is an infographic?

A

infographic: a collection of imagery, charts, and minimal text that gives an easy-to-understand overview of a topic

30
Q

what is a marketing dashboard?

A

marketing dashboard: a reporting tool that provides a comprehensive snapshot of performance-based analytics, key performance indicators (KPIs) and other metrics

31
Q

name the 3 types of marketing dashboards

A

3 types of marketing dashboards:
- strategic (monitor performance towards high-level goals
- tactical (use past data to identify trends that can impact future plans, more specific than strategic dashboards)
- operational (the most strategic dashboard, is often used to track the performance of a department–say, manufacturing or sales–within a company)

32
Q

what is a downside associated with big data?

A

downside = loss of privacy

32
Q

what are the 3 Vs associated with big data?

A
  1. volume, referring to the amount of data
  2. variety, concerning putting together data from different sources
  3. velocity, concerning the fact that the data is arriving continuously in data streams
33
Q

define scraping

A

scraping: an emerging technique that it upsetting many privacy advocates where firms harvest online conversations and collect personal details from social networking sites

34
Q

define the “right to privacy”

A

the “right to privacy: is the right to have information that was never made public to remain private

35
Q

define the “right ot be forgotten”

A

the “right to be forgotten” allows individuals to have information, videos, or photos, about themselves deleted from internet records

36
Q

what does PIPEDA stand for?

A

PIPEDA: Personal information Protection and Electronic Documents Act

37
Q

what are marketing research aggregators?

A

marketing research aggregators: companies that acquire, catalogue, reformat, segment, and resell reports already published by large and small marketing research firms

38
Q

what are geographic information systems (GIS)?

A

geographic information systems: computer-based systems that use secondary and/or primary data to generate maps and visually display various types of data geographically

39
Q

what are decision support systems (DSS)?

A

decision support systems (DSS): interactive, personalized information systems designed to be initiated and controlled by individual decision-makers. it views company information as you wish to see it and it can also ask “what if …” questions

MARK 302 (14 decks)

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