Complete Guide to BA (Business Analytics) Terminology - Essential Concepts for Data Analysts

“Understanding terminology illuminates the path” - In the field of Business Analytics, proper understanding of terminology is the first step toward effective communication and successful projects.

Business Analytics is a rapidly evolving field where new terms and concepts continue to emerge. This post systematically organizes essential terminology that BA professionals must know.

📚 Fundamental Concepts

Business Analytics vs Business Intelligence

Aspect	Business Analytics	Business Intelligence
Purpose	Future prediction and optimization	Understanding past/current situations
Approach	Predictive, prescriptive	Descriptive, explanatory
Data	Both structured and unstructured	Primarily structured data
Tools	Machine learning, statistical models	Dashboards, reports
Outcome	Insights, recommendations	KPIs, trend analysis

Three Stages of Analytics

1. Descriptive Analytics

Definition: Analyzes past and present data to explain “what happened?”
Examples: Sales reports, customer segmentation, trend analysis
Tools: Excel, Tableau, Power BI

2. Predictive Analytics

Definition: Predicts “what will happen?” based on historical data
Examples: Customer churn prediction, sales forecasting, inventory optimization
Tools: Python, R, machine learning models

3. Prescriptive Analytics

Definition: Suggests “how to act?” based on predictions
Examples: Optimal pricing, marketing strategy development, operational optimization
Tools: Optimization algorithms, simulation

Data Storage Comparison

Storage	Characteristics	Advantages	Disadvantages	Use Cases
Data Lake	Raw data storage	Flexibility, cost efficiency	Complex queries	Big data, machine learning
Data Warehouse	Structured data	Fast queries, ACID	Fixed schema	BI, reporting
Data Lakehouse	Lake + warehouse	Best of both worlds	Complexity	Modern analytics platform

🔬 Analytics Techniques

Customer Analytics Techniques

Cohort Analysis

Definition: Tracks and analyzes customer groups with common characteristics from specific time periods
Purpose: Understanding customer behavior patterns, retention analysis
Applications: Subscription services, e-commerce, mobile apps

RFM Analysis

Recency: Days since last purchase
Frequency: Purchase frequency
Monetary: Purchase amount
Applications: Customer segmentation, marketing targeting

Customer Lifetime Value (CLV)

Definition: Expected revenue a customer will bring to a business over their lifetime
Calculation: CLV = (Average Order Value × Purchase Frequency × Customer Lifespan) - Customer Acquisition Cost
Applications: Marketing budget allocation, customer priority decisions

Churn Analysis

Definition: Analysis of patterns and causes of customer service departure
Metric: Churn Rate = (Customers Lost in Period / Total Customers) × 100
Applications: Retention strategy development, churn prevention programs

Experimentation and Testing

A/B Testing

Definition: Compares two versions to measure better performance
Process: Hypothesis setting → Experiment design → Data collection → Result analysis
Considerations: Statistical significance, sample size, experiment duration

Statistical Significance Testing Methods

1. Basic Testing Procedure

Hypothesis Setting: Set null hypothesis (H₀) and alternative hypothesis (H₁)
Significance Level: α = 0.05 (5% significance level)
Test Statistic Calculation: Calculate test statistic based on data
P-value Calculation: Calculate probability that null hypothesis is true
Conclusion: Reject null hypothesis if P-value < 0.05

2. Testing Methods by Data Type

Categorical Data (conversion rate, click rate): Chi-square test or Z-test
Continuous Data (revenue, session duration): t-test or Mann-Whitney U test
Non-normal distribution: Non-parametric tests (Wilcoxon, Mann-Whitney U)
Normal distribution assumption: t-test or ANOVA

3. Sample Size Calculation

Effect Size: Measured by Cohen’s d, Cramér’s V, etc.
Statistical Power: 80% or higher recommended
Formula: n = (Zα/2 + Zβ)² × 2σ² / δ²
Tools: G*Power, R’s pwr package

4. Result Interpretation Criteria

P-value < 0.05: Statistically significant difference exists
Confidence Interval: Significant if 95% CI does not include 0
Practical Effect Size: Statistical significance ≠ practical significance
Power Analysis: Larger effect sizes detectable with smaller samples

Multi-Armed Bandit (MAB)

Definition: Adaptive experimentation method that tests multiple options simultaneously while allocating more traffic to better-performing options
Advantages: Faster convergence compared to traditional A/B testing, minimizes opportunity cost
Algorithms: ε-greedy, UCB (Upper Confidence Bound), Thompson Sampling
Applications: Website optimization, advertising campaigns, recommendation systems

Multivariate Testing

Definition: Tests multiple elements simultaneously to find optimal combinations
vs A/B: More complex but can identify interaction effects
Applications: Website optimization, marketing campaigns

Advanced Analytics Techniques

Market Basket Analysis

Definition: Analysis of patterns of products purchased together
Metrics: Support, Confidence, Lift
Applications: Product recommendations, shelf optimization, bundle product development

Time Series Analysis

Definition: Analysis of data arranged in chronological order
Techniques: ARIMA, Prophet, LSTM
Applications: Sales forecasting, inventory management, trend analysis

⚙️ Data Processing

ETL vs ELT

Aspect	ETL	ELT
Extract	Extract raw data	Extract raw data
Transform	Transform data (intermediate storage)	Transform data (target storage)
Load	Load to final storage	-
Advantages	Data quality assurance	Fast processing, flexibility
Disadvantages	Complexity, delay	Need for data quality management

Data Quality Management

Data Quality Dimensions

Accuracy: Correctness (Are the values correct?)
Completeness: Completeness (Are there missing values?)
Consistency: Consistency (Is the format consistent?)
Timeliness: Timeliness (Is it timely?)
Validity: Validity (Does it follow the rules?)

Data Governance

Definition: Policies, processes, and role definitions for effective data asset management
Components: Data policies, data standards, data ownership, data quality management
Purpose: Data reliability, compliance, improved decision-making quality

Data Lineage

Definition: Process of tracking where data comes from and where it goes
Applications: Impact analysis, data quality issue tracking, compliance
Tools: Apache Atlas, DataHub, Collibra

Data Integration

Master Data Management (MDM)

Definition: System for integrated management of enterprise core data
Master Data: Customer, product, supplier, employee information
Purpose: Data consistency, deduplication, single source of truth

Data Virtualization

Definition: Logical integration of physically distributed data for access
Advantages: Real-time access, no physical copying required
Applications: Real-time dashboards, ad-hoc analysis

📊 Business Metrics

Key Performance Indicators

KPI vs OKR

Aspect	KPI	OKR
Purpose	Performance measurement	Goal achievement
Characteristics	Continuous, quantitative	Periodic, challenging
Examples	Monthly revenue, customer satisfaction	30% increase in new customers in 3 months
Applications	Performance monitoring	Strategic goal setting

User Metrics

DAU (Daily Active Users): Daily active users
WAU (Weekly Active Users): Weekly active users
MAU (Monthly Active Users): Monthly active users
Stickiness: DAU/MAU ratio (user engagement)

Web Service Metrics

Web Traffic Metrics

PV (Page View): Page view count
UV (Unique Visitor): Unique visitor count
Visit (Session): Visit session count
Duration: Duration (average time spent on site)

Web Usability Metrics

Bounce Rate: Bounce rate = (Single-page sessions / Total sessions) × 100
Exit Rate: Exit rate = (Sessions that ended on specific page / Sessions that viewed that page) × 100
Average Session Duration: Average session duration
Pages per Session: Pages per session

Mobile App Metrics

App Store Rating: App store rating
Download Rate: Download rate
Install Rate: Install rate
App Open Rate: App open rate

Engagement Rate: Engagement rate = (Likes + Comments + Shares) / Reach × 100
Reach: Reach (unique users who saw content)
Impressions: Impressions (total times content was displayed)
Share Rate: Share rate = (Shares / Impressions) × 100

User Identification Methods

Aspect	Cookie-based	Login Session
Definition	User identification via browser cookies	User identification via account login
Accuracy	Medium (per device/browser)	High (per actual user)
Tracking Scope	Within same device/browser	Integrated across all devices/browsers
Advantages	Immediate tracking, no login required	Accurate user identification, cross-device tracking
Disadvantages	Tracking stops when cookies deleted, duplicate accounts	Login required, difficult to track anonymous users
Applications	General web analytics, ad targeting	Personalization, customer journey analysis, CRM integration

Characteristics by User Identification Method

Technology: Browser cookies, pixel tracking, device fingerprinting
Tracking Scope: Valid only within same browser/device
Data Quality: Inaccurate when cookies deleted, incognito mode, multiple device usage
Use Cases: Google Analytics default settings, ad retargeting

Technology: User account ID, session management, SSO
Tracking Scope: Integrated tracking across all devices and browsers
Data Quality: Based on actual users, high accuracy
Use Cases: Personalized services, customer journey analysis, CRM systems

Hybrid Method

Technology: Combination of cookies + login + device ID
Tracking Scope: Maximum user tracking across broad range
Data Quality: High accuracy and comprehensiveness
Use Cases: Large-scale platforms, services requiring sophisticated analysis

Mobile App Advertising Identifiers

Identifier	Platform	Description	Characteristics
IDFA	iOS	Identifier for Advertisers	For ad tracking, user can block
IDFV	iOS	Identifier for Vendor	Per app developer, changes when app deleted
GAID	Android	Google Advertising ID	For ad tracking, user can reset
Android ID	Android	Android system ID	Unique per device, difficult to change
OpenUDID	iOS/Android	Open source UDID	Unofficial identifier, violates app store policy

Characteristics by Advertising Identifier

IDFA (Identifier for Advertisers)

Definition: iOS advertising tracking identifier
Characteristics: User can block in settings (iOS 14.5+)
Applications: Ad targeting, attribution analysis
Limitations: ATT (App Tracking Transparency) framework required

IDFV (Identifier for Vendor)

Definition: iOS per-app-developer identifier
Characteristics: Changes when app is deleted, user cannot block
Applications: In-app analytics, developer-level user tracking
Limitations: Not unique per app (shared among same developer’s apps)

GAID (Google Advertising ID)

Definition: Android advertising tracking identifier
Characteristics: User can reset, ad personalization settings available
Applications: Ad targeting, install attribution
Limitations: Stricter restrictions on Android 12+

Android ID

Definition: Android system-level unique identifier
Characteristics: Unique per device, difficult for users to change
Applications: Device identification, security authentication
Limitations: Different values per app, changes on factory reset

Business Metrics

ARR (Annual Recurring Revenue): Annual recurring revenue
MRR (Monthly Recurring Revenue): Monthly recurring revenue
CAC (Customer Acquisition Cost): Customer acquisition cost
LTV (Lifetime Value): Customer lifetime value
LTV/CAC Ratio: Profitability metric (3:1 or higher recommended)

Conversion and Retention Metrics

Conversion Funnel

Stages: Awareness → Interest → Consideration → Purchase → Loyalty
Metrics: Conversion rate by stage, drop-off analysis
Applications: Marketing optimization, user experience improvement

Types of Funnel Calculations

Linear Funnel: Linear funnel (sequential step progression)
Branching Funnel: Branching funnel (multiple path divergences)
Parallel Funnel: Parallel funnel (simultaneously possible steps)
Loop Funnel: Loop funnel (repeatable steps)
Reverse Funnel: Reverse funnel (backtracking from the end)
Cohort Funnel: Cohort funnel (analysis by specific groups)
Time-based Funnel: Time-based funnel (progression within specific periods)
Goal-based Funnel: Goal-based funnel (focused on final goal achievement)

Types of Funnels

Sales Funnel: Sales funnel (Prospect → Lead → Proposal → Contract)
Marketing Funnel: Marketing funnel (Awareness → Interest → Consideration → Purchase)
User Onboarding Funnel: User onboarding funnel (Sign-up → Profile completion → First use → Retention)
E-commerce Funnel: E-commerce funnel (Product view → Cart → Checkout → Purchase completion)
Lead Generation Funnel: Lead generation funnel (Visit → Content download → Contact input → Sales connection)
App Install Funnel: App install funnel (App store visit → App download → Install → Launch → Registration)

Retention Metrics

Retention Rate: Return rate = (Returning users / Total users) × 100
Churn Rate: Churn rate = (Lost users / Total users) × 100
NPS (Net Promoter Score): Customer recommendation intention measurement

Types of Retention Calculations

Classic Retention: Classic retention (return rate based on specific date signups)
Rolling Retention: Rolling retention (percentage of users active after N days)
Return Retention: Return retention (return rate within specific period after signup)
Unbounded Retention: Unbounded retention (users who returned at any time after signup)
Bracket Retention: Bracket retention (users active within specific time range)
Day N Retention: Day N retention (percentage of users active on Nth day after signup)
Cohort Retention: Cohort retention (based on same-period signup groups)

Types of Retention Strategies

Onboarding Optimization: Onboarding optimization (improving new user experience)
Gamification: Gamification (points, badges, level systems)
Personalization: Personalization (customized content, recommendation systems)
Email Marketing: Email marketing (newsletters, remarketing campaigns)
Push Notification: Push notifications (app re-engagement)
Loyalty Program: Loyalty programs (point accumulation, discount benefits)
Customer Support: Customer support (24/7 chat, FAQ, tutorials)
Content Strategy: Content strategy (regular updates, new features)
Social Features: Social features (community, sharing, reviews)
Win-back Campaign: Win-back campaigns (special offers for churned customers)

Marketing and Advertising Metrics

ROI (Return on Investment)

Definition: Return on investment ratio
Calculation: ROI = (Investment Return - Investment Cost) / Investment Cost × 100
Applications: Marketing campaign effectiveness measurement, investment decision-making

ROAS (Return on Ad Spend)

Definition: Return on advertising spend
Calculation: ROAS = Revenue from ads / Advertising cost
Applications: Digital marketing performance measurement, ad budget optimization

CPM (Cost Per Mille)

Definition: Cost per thousand impressions
Calculation: CPM = (Advertising cost / Impressions) × 1,000
Applications: Ad impression cost comparison, brand awareness campaigns

CPC (Cost Per Click)

Definition: Cost per click
Calculation: CPC = Advertising cost / Clicks
Applications: Search ads, display ads performance measurement

CPA (Cost Per Acquisition)

Definition: Cost per customer acquisition
Calculation: CPA = Advertising cost / Acquired customers
Applications: Marketing efficiency measurement, channel performance comparison

CTR (Click-Through Rate)

Definition: Click-through rate
Calculation: CTR = (Clicks / Impressions) × 100
Applications: Ad creative effectiveness measurement

Conversion Rate

Definition: Conversion rate
Calculation: Conversion Rate = (Conversions / Visitors) × 100
Applications: Website optimization, marketing campaign performance measurement

Attribution Modeling

Definition: Method for measuring the contribution of each touchpoint in the customer journey
Models: First-touch, Last-touch, Linear, Time-decay, Position-based
Applications: Marketing channel effectiveness analysis, budget allocation optimization

Customer Journey

Definition: The entire process a customer goes through from brand awareness to purchase
Stages: Awareness → Interest → Consideration → Purchase → Retention
Applications: Marketing strategy development, customer experience optimization

Omnichannel

Definition: Marketing strategy that provides consistent customer experience by integrating all channels
Channels: Online, offline, mobile, social media
Applications: Brand consistency, customer satisfaction improvement

🛠️ Tools & Platforms

Visualization Tools

Tableau

Features: Drag and drop, powerful visualization
Advantages: Ease of use, interactive dashboards
Applications: Data exploration, business dashboards

Power BI

Features: Microsoft ecosystem integration
Advantages: Cost efficiency, cloud integration
Applications: Enterprise BI, self-service analytics

Looker

Features: LookML-based modeling
Advantages: Reusable models, automatic documentation
Applications: Data team-centered analytics, embedded analytics

Analytics Platforms

Google Analytics

Features: Website traffic analysis
Metrics: Page views, sessions, bounce rate, conversion rate
Applications: Digital marketing, website optimization

Adobe Analytics

Features: Enterprise-grade web analytics
Advantages: Advanced segmentation, multi-channel analysis
Applications: Large-scale e-commerce, multi-brand

Data Platforms

Snowflake

Features: Cloud-native data warehouse
Advantages: Auto-scaling, SQL-based
Applications: Large-scale data analytics, real-time analytics

Google BigQuery

Features: Serverless data warehouse
Advantages: Cost efficiency, ML integration
Applications: Big data analytics, ML pipelines

Amazon Redshift

Features: Cluster-based data warehouse
Advantages: AWS ecosystem integration, performance
Applications: Enterprise data warehouse

Data Processing Tools

dbt (Data Build Tool)

Features: SQL-based data transformation
Advantages: Version control, automated testing
Applications: Data modeling, data quality management

Apache Airflow

Features: Workflow orchestration
Advantages: Scheduling, monitoring, scalability
Applications: ETL pipelines, batch jobs

📊 Google Analytics Terminology (Appendix)

Basic Metrics

Sessions: Sessions (time users spent active on site)
Users: Users (unique visitor count)
Pageviews: Page views (total number of pages viewed)
Bounce Rate: Bounce rate (percentage of single-page sessions)
Average Session Duration: Average session duration
Pages per Session: Pages per session

Engagement Metrics

Engagement Rate: Engagement rate (percentage of interactive sessions)
Engaged Sessions: Engaged sessions (10+ seconds or event occurred)
Engagement Time: Engagement time (time users actually interacted)
Scroll Depth: Scroll depth (degree of page scrolling)
Click-through Rate (CTR): Click-through rate (percentage of users who clicked)
Time on Page: Time on page (time spent on specific page)
Exit Rate: Exit rate (percentage of sessions ending on specific page)

Traffic Sources

Organic Search: Organic search (traffic from search engines)
Paid Search: Paid search (traffic from ads)
Direct: Direct (direct URL input, bookmarks)
Referral: Referral (traffic from other site links)
Social: Social media (Facebook, Twitter, etc.)
Email: Email (traffic from email links)
Display: Display advertising (banner ads, etc.)

Advanced Analytics Terms

Goals: Goals (conversion point settings)
Conversions: Conversions (goal achievement count)
Conversion Rate: Conversion rate (conversion ratio to sessions)
Funnel Visualization: Funnel visualization (step-by-step conversion process)
Flow Visualization: Flow visualization (user behavior flow)
Cohort Analysis: Cohort analysis (analysis of specific period signup groups)

Google Analytics 4 (GA4) Terms

Events: Events (user behavior tracking units)
Parameters: Parameters (detailed information of events)
Custom Dimensions: Custom dimensions (additional classification criteria)
Custom Metrics: Custom metrics (additional measurement criteria)
Audiences: Audiences (user groups with specific conditions)
Attribution: Attribution (analysis of channels contributing to conversions)
Data Streams: Data streams (data collection settings)

Measurement and Tracking

Tracking Code: Tracking code (JavaScript inserted into website)
Google Tag Manager: Google Tag Manager (tag management system)
Enhanced Ecommerce: Enhanced ecommerce (detailed purchase analysis)
Cross-domain Tracking: Cross-domain tracking (integrated analysis across multiple domains)
User ID: User ID (logged-in user identification)
Client ID: Client ID (unique identifier per browser)

💼 Practical Application Guide

Terminology Usage Scenarios

Executive Reporting

Use: KPIs, ROI, strategic objectives
Avoid: Technical details, complex algorithm names

Technical Team Collaboration

Use: Data pipelines, ETL, schema, API
Avoid: Business jargon, marketing terms

Marketing Team Collaboration

Use: Segmentation, conversion rate, campaign performance
Avoid: Database structure, technical implementation

Effective Communication

Importance of Terminology Definition

Build common terminology within teams
Provide education when introducing new terms
Regular terminology review and updates

Appropriate Terminology Selection by Situation

Consider audience background
Use terms appropriate for purpose
Add explanations when necessary

Learning Directions for Growth

Beginner

Organize basic concepts (BA vs BI, analysis stages)
Learn tool usage (Excel, Tableau)
Understand business metrics

Intermediate

Advanced analytics techniques (cohort, RFM, CLV)
Data processing tools (dbt, Airflow)
Experiment design and A/B testing

Advanced

Build machine learning models
Design data architecture
Team leadership and strategy development

📚 Learning Summary

Key Points

Accurate Understanding of Terminology
- Clearly distinguish between BA vs BI
- Organize terminology by analysis stage
- Understand characteristics of data storage
Appropriate Terminology Usage by Situation
- Choose terminology based on audience
- Communication appropriate for purpose
- Build common language within teams
Continuous Learning and Updates
- Learn new tools and techniques
- Stay updated with industry trends
- Understand terminology through practical experience

Next Steps

Practical Projects: Apply analytics techniques with real data
Tool Proficiency: Learn professional tool usage
Networking: Communicate and learn with industry experts

“Using correct terminology is the first step in demonstrating professionalism.”

Business Analytics is a continuously evolving field. Use this guide as a reference for accurate terminology usage and continue learning to enhance your expertise. When you understand and use terminology correctly, more effective analysis and communication become possible!