Business Analytics Cheatsheet Series
Mục đích: Ghi chú cá nhân + chia sẻ kiến thức về nền tảng phân tích và xử lý dữ liệu của Data science trong các bài toán kinh doanh.
Định dạng: Intuition → Công thức → Code Python thực tế.
Đối tượng: Data science/engineer, BA.
Topics:
business-analyticscrisp-dmglossarykpiseries-index
How to use this series
Each file is a lookup table, not a tutorial. Three columns throughout:
| Column | What it holds |
|---|---|
| Term | The technique, method, theorem, or definition. |
| Type | Where it sits in the data science toolkit — Framework, Process, Metric, Test, Algorithm, Technique, Diagnostic, Artifact, Concept, Anti-pattern, Governance. |
| Definition / Usage | The concept, formula, role, and do/don’t. Optimized for recall, not first-time learning. |
Series index
| # | Bài viết | Covers |
|---|---|---|
| 00 | Business Analytics Cheatsheet Series | This page: reading guide + shared glossary. |
| 01 | Business Analytics Foundations & Types of Analytics | What BA is, BA vs DA vs DS, evolution, 6-step BA process, four analytics types. |
| 02 | CRISP-DM, KPI Tree & Problem Framing | CRISP-DM six phases, KPI Tree, problem framing, A/B basics, templates. |
| 03 | Data Quality & Feature Engineering | Quality dimensions, missingness, leakage, encoding, scaling, feature selection, pipelines. |
| 04 | EDA & Visualization for Decision-Making | Univariate/bivariate/multivariate EDA, correlation vs causation, chart choice, chartjunk. |
| 05 | Linear Regression (Simple & Multiple) | SLR/MLR, least squares, r², LINE assumptions, residual analysis, inference, MRA. |
| 06 | Classification Evaluation: ROC/PR, Thresholding, Cost-Sensitive, Calibration | Confusion matrix, ROC/PR, thresholding, cost-sensitive decisions, calibration. |
| 07 | Segmentation & Dimensionality Reduction | k-means, GMM/EM, PCA, RFM, hybrid segmentation pipeline. |
Reading order
- New to BA → 01 → 02 → 04 → 03 → 05 → 06 → 07.
- Scoping a project → 02 (framing + KPI Tree) → 03 (leakage) → 06 (evaluation contract).
- Reviewing someone’s analysis → 04 (anti-patterns) → 03 (leakage) → 06 (metric choice).
Shared glossary
Terms used across multiple decks. Deck-specific terms live in their own file.
Business & product metrics
| Term | Type | Definition / Usage |
|---|---|---|
| AOV — Average Order Value | Metric (output) | Σ(price × qty) / #orders. Revenue lever independent of traffic. Levers: bundles, upsell, free-shipping threshold. Watch: returns and order edits change the denominator. |
| CR — Conversion Rate | Metric (output) | #conversions / #sessions (or users). Decompose into stage-wise conditional probabilities before optimizing. Always report the window and the unit (session vs user). |
| CAC — Customer Acquisition Cost | Metric (input) | Total acquisition spend / new customers. Compare against LTV, not against revenue. |
| LTV — Lifetime Value | Metric (output) | Expected discounted margin over a customer’s lifetime. Sensitive to churn assumptions — state the horizon. |
| MRR / ARR | Metric (output) | Monthly/Annual Recurring Revenue. MRR = #Customers × ARPA × (1 − Churn). |
| ARPA | Metric (output) | Average Revenue Per Account. Pricing/packaging lever in the MRR tree. |
| Churn | Metric (output) | Share of customers lost in a period. Define the event precisely (contract end vs inactivity threshold) — the definition is the model’s label. |
| Retention | Metric (output) | 1 − churn for the cohort. Report as a curve by cohort age, not a single number. |
| Cohort | Concept | Group sharing a start event (signup month, first purchase). Cohorting isolates mix-shift from real behavior change. |
| North Star | Metric (framework) | Single metric that best proxies delivered customer value; the root of the KPI Tree. |
| Vanity metric | Anti-pattern | Metric that moves without informing any decision (raw pageviews, cumulative signups). Drop unless tied to a lever. |
Metric design
| Term | Type | Definition / Usage |
|---|---|---|
| Leading KPI | Metric class | Moves before the outcome; usable for steering (add-to-cart rate, activation rate). Noisier but actionable. |
| Lagging KPI | Metric class | Confirms the outcome after the fact (revenue, churn). Authoritative but too slow to steer. Pair one leading with one lagging. |
| Input vs Output metric | Metric class | Input = what the team controls (emails sent, latency). Output = what the business feels (revenue). Set targets on outputs, run sprints on inputs. |
| Health vs Performance metric | Metric class | Health = system doesn’t break (latency, payment error rate). Performance = system produces value. Ship neither alone. |
| Guardrail | Metric class | Hard constraint that a winning variant must not violate (e.g., “overall CR must not drop >1pp”). Defined before the experiment, not after. |
| Funnel | Framework | Ordered stage sequence (session → view → add_to_cart → checkout → purchase). Multiply stage rates to get end-to-end CR; the smallest stage rate is rarely the biggest absolute loss — compute absolute drop-off. |
| Uplift | Metric | Incremental effect of treatment vs control. Not the same as post-treatment level; requires a counterfactual. |
| Drift | Diagnostic | Distribution shift over time in inputs (data drift) or in the input→output relation (concept drift). Triggers retraining/recalibration. |
Delivery & MLOps
| Term | Type | Definition / Usage |
|---|---|---|
| Canary release | Deployment pattern | Route a small traffic slice to the new version, monitor, then expand. Limits blast radius; needs an automatic rollback trigger. |
| Blue-Green deployment | Deployment pattern | Two full environments; switch traffic atomically. Fast rollback, double the infrastructure cost. |
| Feature Store | Artifact / infrastructure | Central registry of computed features with lineage and versioning. Guarantees train/serve consistency — the top cause of silent production degradation. |
| Model Card | Artifact | Documentation of intended use, training data, metrics by segment, limitations, fairness checks. Required for handover and audit. |
| Runbook | Artifact | Operational procedure: alerts, thresholds, on-call actions, rollback steps. |
| RACI | Governance | Responsible / Accountable / Consulted / Informed. Attach to every KPI and every deliverable; unassigned KPIs do not move. |
| Data lineage | Governance | Traceable path from source system to metric. Prerequisite for trusting any number in a board deck. |
| Data contract | Governance | Agreement between producer and consumer on schema, semantics, and SLA. Versioned; breaking changes require backfill plans. |
Evidence hierarchy
| Term | Type | Definition / Usage |
|---|---|---|
| Observation | Evidence level | What the data shows. Safe to state. “Mobile CR is 1.2pp below desktop.” |
| Hypothesis | Evidence level | Proposed explanation, falsifiable. “Mobile CR is lower because the checkout form requires re-entry.” Must name the test that would kill it. |
| Causation | Evidence level | Established only by randomization or a credible quasi-experiment (DID, IV, RDD). Never claim it from correlation alone. |
| A/B test | Method | Randomized controlled experiment. Fix randomization unit, primary metric, guardrails, and duration before launch. Do not peek. |
| Holdout | Method | Untreated group kept aside long-term to measure cumulative program effect, not a single change. |
| Quasi-experiment | Method | DID (difference-in-differences), IV (instrumental variables), RDD (regression discontinuity). Use when randomization is impossible; assumptions are strong and must be stated. |
Definition of Done for this series
- Every term you use in a stakeholder document appears in one of these files, or you add it.
- Every KPI you cite has: a formula, a window, a unit of analysis, an owner.
- Every causal claim in your writing names its identification strategy.
- Every model deliverable ships with a model card and a monitoring plan.
Next step: start with CRISP-DM, KPI Tree & Problem Framing — it is the spine the other files hang from.
🔗 Full series
Bài 1: Business Analytics Foundations & Types of Analytics
Bài 2: CRISP-DM, KPI Tree & Problem Framing
Bài 3: Data Quality & Feature Engineering
Bài 4: EDA & Visualization for Decision-Making
Bài 5: Linear Regression (Simple & Multiple)
Bài 6: Classification Evaluation: ROC/PR, Thresholding, Cost-Sensitive, Calibration
Bài 7: Segmentation & Dimensionality Reduction
Nếu bài này hữu ích, hãy chia sẻ cho người đang học Data Science / Business Analytics. Mọi góp ý về lỗi kỹ thuật rất được hoan nghênh.