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GHL 3353: Hospitality Metrics and Data Analytics > Week 1: Business Analytics > Flashcards

Week 1: Business Analytics Flashcards

(58 cards)

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1
Q

Data analytics (definition)

A

Cleaning, processing,
and analyzing data to tell stories,
help decision-making, improve
business operation, performance,
customer satisfaction/experience, etc.

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2
Q

Business analytics (definition)

A

Use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models

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3
Q

Goals of business analytics

A
  • Uncover patterns, relationships, and insights
  • Enable better business decision-making
  • Solve business problems, monitor their
    business fundamentals, identify new growth
    opportunities
  • Enhance customer experience and satisfaction
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4
Q

Five stages of business analytics (maturity toward business value)

A
  1. Data Wrangling
  2. Descriptive Analytics
  3. Predictive Analytics
  4. Prescriptive Analytics
  5. Storytelling
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5
Q

1st stage of business analytics

A

Data wrangling

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6
Q

Data wrangling (definition)

A

Preparing data for analytics.

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7
Q

Examples of data wrangling

A

Data transformation, data structuring, and SQL

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8
Q

2nd stage of business analytics

A

Descriptive analytics

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9
Q

Descriptive analytics (definition)

A

Describing what has happened; identifying trends/patterns in historical data

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10
Q

Examples of descriptive analytics

A

Data mining, web analytics, and IoT analytics

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11
Q

3rd stage of business analytics

A

Predictive analytics

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12
Q

Predictive analytics (definition)

A

Predicting future outcomes (demand forecasting)

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13
Q

Examples of predictive analytics

A

A/B testing and forecasting

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14
Q

4th stage of business analytics

A

Prescriptive analytics

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15
Q

Prescriptive analytics (definition)

A

Deciding what we should do

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16
Q

Example of prescriptive analytics

A

Optimization

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17
Q

5th stage of business analytics

A

Storytelling

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18
Q

Storytelling (definition)

A

Communicating analytics for decision-making

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19
Q

Example of storytelling

A

Visualization

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20
Q

How business analytics affects firms

A

It provides data and informs actions

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21
Q

How do the firm’s actions affect business analytics

A

It provides market data

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22
Q

Data (definition)

A

Facts, numbers, words, observations, or other useful information

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23
Q

Quantitative data (definition)

A

Data that can be quantified or measured

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24
Q

Qualitative data (definition)

Study These Flashcards
A

Descriptive information related to concepts and characteristics rather than numbers

25
Structured data (definition)
Data residing in a fixed field within a file or record
26
Unstructured (definition)
Data not in a specific format
27
Firm-generated data [FGD] (definition)
Information created and collected by the company itself
28
Consumer-generated data [CGD] (definition)
Data that is created and shared voluntarily by customers
29
Big data (definition)
Large, hard-to-manage volumes of structured/unstructured that flood businesses on a day-to-day basis
30
5 Vs of Big Data
Volume, velocity, veracity, value, and variety
31
Data volume (definition)
Size of the data
32
Data velocity (definition)
The speed data appears and disappears
33
Data veracity (definition)
Reliability of the data
34
Data value (definition)
Relevance of the data
35
Data variety (definition)
Types of data
36
Sources of data
-Operational data - Social media - Review sites - Customer data - Payment information - Mobile apps
37
Guest customer journey in Digital Transformation Era steps
1. Pre-travel 2. Research 3. Booking 4. On-site experience 5. Post-travel
38
Pre-Travel Technology Digitalization examples
Social media marketing
39
Pre-Travel Data Digitalization examples
Social medial KPI's
40
Research Technology digitalization examples
Website, search engine marketing, and meta search reviews
41
Research Data digitization examples
Website KPI's and online reviews
42
Booking technology digitization examples
Website and mobile app
43
Booking data digitization examples
Guest and transaction data
44
On-site experience technology digitization examples
Mobile app, in-room technology, and AI assistants
45
On-site experience data digitization examples
Guest behavioral data and transaction data
46
Post-travel technology digitization examples
Social media and mobile app
47
Post-travel data digitization examples
Direct feedback and online reviews
48
Innovative data collection technologies
- Facial recognition - Robotics - Smart assistant - Virtual reality - Mobile applications
49
Data types we can collect
- Guest Info - Expenditure/payment - Room preferences and usage - Interaction data with AI - Booking and transaction - Internet usage - Movement - Energy consumption - Social media and online interaction
50
Simulation algorithms (definitions)
Recommended actions/strategies for desired outcomes
51
Diagnostic analytics (definitions)
Causes of observed patterns
52
CRISP-DM acronym
Cross-industry standard process for data mining
53
Tools for data & text analysis
Rapid Miner, XLMiner, Nvivo, LIWC, Sentiment Analysis, SAS Enterprise Miner, SAS Enterprise Guide
54
Tools for data collection and programming
Java, Excel VBA, ASP, SQL, Python
55
Tools for statistical analysis
R, STATA, SPSS, SAS
56
Tools for data visualization
Tableau, Power BI
57
4 reasons why business analytics are relevant
- Addressing industry challenges - Changing and growing competition - More data, better tools - Smarter decisions for everyone
58
How business analytics address industry challenges
Forecasting demand, optimizing staffing, improving pricing, and resolving guest dissatisfaction

GHL 3353: Hospitality Metrics and Data Analytics (4 decks)

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