DAHI 311 Reviewer: Data Analysis Cycle & Hospitality Analytics

Comprehensive vocabulary flashcards covering data analysis cycle stages, statistics, text analysis, classification, regression, machine learning, and prescriptive analytics in modern hospitality management.

Property Management Systems (PMS)

Systems that capture core transactional inputs including room rates, booking dates, and precise guest demographics.

User-Generated Content (UGC)

Unstructured, qualitative data extracted from social media and online reviews to gauge destination preferences and guest satisfaction.

Internet of Things (loT)

Technology that harvests real-time machine data from connected physical devices such as smart thermostats and digital room keys.

Data Provenance

The process of documenting and tidying datasets in real-world environments to prevent costly errors and ensure data reliability.

Deduplication

A data cleaning filter (Filter A) used to remove double-counted bookings resulting from multi-channel distributions.

Normalization

A data cleaning filter (Filter B) that aligns time zones and currency metrics to allow accurate, apples-to-apples comparisons.

Descriptive Analytics

Analytics that summarize past events through standard arithmetic and aggregation to identify operational bottlenecks and market shifts after they occur.

Predictive Modeling

The application of machine learning algorithms to historical datasets to forecast future demand and support proactive decision-making.

Factual Judgment

The apex of the data analysis cycle, reached by synthesizing processed data to understand why an event occurred and justify executive action.

Text Analysis

The application of Natural Language Processing (NLP) to extract meaning from unstructured guest feedback and convert reviews into quantifiable datasets.

Sentiment Analysis

A technique used to sort text into positive, negative, or neutral categories to gauge overall guest satisfaction and brand reputation.

Entity Extraction

The process of pulling specific keywords (e.g., "cleanliness", "breakfast") from guest feedback to identify priority amenities.

Emotion Detection

The use of advanced AI models to classify specific underlying emotions, such as excitement, frustration, or disappointment, from written text.

Diagnostic Analysis

Analytics that move beyond descriptive analytics to answer "Why did this happen?" by revealing underlying causes of trends and anomalies.

Descriptive Statistics

Statistical methods used to summarize and organize data to describe its main features.

Mean ($\bar{x}$)

The average value central to establishing baseline performance across seasons, such as Average Daily Rate (ADR), occupancy percentage, or median guest age.

Variance ($\sigma^2$)

A statistical measure of data spread indicating booking volatility and service delivery consistency.

Inferential Statistics

Statistical techniques that use a random sample of data to make inferences or predictions about a larger population.

Demographic Analysis

Identifying target audiences based on age, income, and booking preferences through statistical distribution.

Customer Satisfaction Index (CSI)

A mathematically calculated Key Performance Indicator (KPI) used to track growth in guest satisfaction and service quality.

Confidence Intervals

Statistical ranges used to verify that an operational change's success is not a statistical fluke before full-scale implementation.

Risk Assessment (Probability)

The application of probability models to calculate optimal overbooking ratios for hotel rooms and flights.

Predictive Analytics

Analytics addressing "What might happen?" by using statistical models and machine learning to anticipate demand based on historical data.

Binary Classification

A supervised machine learning model predicting between exactly two outcomes, such as "Cancellation" (Yes) versus "Stay" (No).

Multi-Class Classification

A machine learning model predicting among three or more categories, such as categorizing review sentiment into "Positive," "Neutral," or "Negative."

Regression Models

Predictive models designed to output exact, continuous numerical values, such as room prices or total visitor counts.

Simple Linear Regression

A continuous predictive model defined by $y = \beta_0 + \beta_1 x + \theta$, where $y$ is the predicted variable (Room Demand), $x$ is the independent variable (Marketing Spend), $\beta_0$ is the intercept, $\beta_1$ is the coefficient, and $\theta$ is the error term.

K-Means Clustering

An unsupervised learning method that discovers hidden groupings in customer data without predefined labels based on spending patterns.

Customer / KPI Dashboards

Pre-built, visual interfaces providing real-time metric updates (such as daily RevPAR, Occupancy %, and CSI) for operational roles.

Ad-Hoc Analysis

Custom, one-off reports generated sporadically by data analysts or strategy directors to answer specific operational questions.

Prescriptive Analytics

Analytics addressing "What should we do next?" by leveraging complex AI algorithms, heuristics, and machine learning to optimize decision-making.

Dynamic Pricing

An automated system that updates room rates in real-time according to demand and competitor pricing to maximize RevPAR.

Data Preparation

The phase in the data process involving cleaning, merging, and formatting data from PMS, POS, and CRMs prior to modeling.

Modeling

The step in the data lifecycle where machine learning algorithms are applied to identify patterns and generate predictions.

Deployment

The integration of a completed machine learning model into operational hotel software for daily workflow usage.

Implementation Deliverables

Tangible end-products of a data project, including dynamic pricing engines, cleaned databases, or automated reporting scripts.

Segmentation Models

Customer analytics models that group guests by shared characteristics to allocate marketing spend efficiently.

RFM Analysis

A evaluation model that quantifies guest value based on Recency (last stay), Frequency (stay count), and Monetary Value (total spend).

Recommender System

An algorithmic tool designed to drive ancillary revenue via targeted upselling and cross-selling suggestions.

Collaborative Filtering

A recommendation algorithm that suggests items to a user based on the purchasing preferences of similar guests.

Content-Based Filtering

A recommendation algorithm that suggests items to a user based on their own past choices and interactions.

Data Analysis Cycle Pyramid

A four-tier pyramid structure detailing the workflow from Collection at the base, through Preparation and Analysis, to Factual Judgment at the top.

The Operational Engine Framework

A diagram mapping back-end extraction to front-end decision-making across Data Collection, Preparation & Cleaning, Analysis & Processing, and Factual Judgment.

Executive Action Workflow

A three-stage decision path connecting an initial Observation to Synthesis of processed data and resulting Executive Action.