Advanced Analytics and Data Visualization: Telling Stories with Data
Master advanced analytics. Build models, visualize data, communicate insights.
Key Takeaways
- Data modeling: Build clean data pipeline (source → transform → visualize). Typical: Billing system (Stripe) → Data warehouse (Snowflake) → Transform (dbt, SQL) → Dashboard (Tableau). Architecture matters: Bad data architecture = slow dashboards, manual updates, errors. Good = automated, reliable, scales. Cost: 4-6 weeks data engineer time (£2-5K cost). Benefit: Automated reporting (save 5-10 hours/month finance), better data quality, faster decision-making. Tools: Snowflake (cloud data warehouse, £1-5K/month), dbt (transform SQL, free), Tableau (visualization, £500-5K/month).
- Visualization principles: Different data types = different charts. Trend (line chart), comparison (bar), distribution (histogram), correlation (scatter), parts-of-whole (stacked bar, not pie). Interactivity: Drill-down (click metric → detail), filters (by segment, region), export (board presentations). Color: Use to highlight (red = at-risk, green = healthy), not for beauty. Example: Churn dashboard → Line chart (churn trend month-by-month), bar chart (churn by segment), drill-down (click segment → customers churning this month).
- Predictive analytics (advanced): Cohort-based forecasting (next month churn = historical churn rate × customers × cohort quality). Machine learning models (predict which customers at-risk using engagement data). ROI: Identify 50 at-risk customers, save 25 (1% churn reduction = £100K+ ARR). Cost: Data scientist 3 months build model. Benefit: Proactive vs reactive (act before churn, not after). Cadence: Refresh weekly (new at-risk predictions), CS acts immediately (outreach).
Advanced Analytics and Data Architecture
Building analytics infrastructure that scales. **Data architecture** Pipeline: 1. Source: Billing (Stripe, Zuora), CRM (Salesforce), analytics (Amplitude, Segment) 2. Ingestion: Pull data daily (automated) 3. Warehouse: Consolidate (Snowflake, BigQuery, Redshift) 4. Transform: Clean, aggregate, model (dbt, SQL) 5. Visualization: Dashboard (Tableau, Looker, Metabase) 6. Governance: Metadata, documentation, access control Example data model (SaaS): - customers table: ID, name, sign-up date, plan, churn date - contracts table: Customer ID, ARR, start date, end date - usage table: Customer ID, month, feature usage, support tickets - finances table: Customer ID, month, MRR, expansion, churn Transformation (dbt): - daily_cohorts: Customer cohorts by signup date, retention curves - unit_economics: CAC, LTV, payback by customer - churn_analysis: Churn by segment, cohort, reason **Visualization design** Chart selection: | Data Type | Chart | Example | |---|---|---| | Trend over time | Line chart | Churn rate month-by-month | | Comparison across categories | Bar chart | Revenue by region | | Distribution | Histogram | CAC distribution by channel | | Correlation | Scatter | NPS vs retention | | Composition | Stacked bar | Revenue by product | | Hierarchical | Treemap | Customer base by segment | Color usage: - Status: Green (good), yellow (warning), red (alert) - Highlight: Color for focus, gray for context - Avoid: Rainbow (confusing), too many (noisy) Interactivity: - Drill-down: Click bar → detail (Region → Territory → Rep) - Filters: By date, segment, geography - Export: PDF, CSV for presentations - Tooltips: Hover for exact numbers Dashboard design: - Executive (1 page): 7-10 top metrics, status, trend - Team (2-3 pages): Detailed metrics by function - Ad-hoc: Flexible exploration for analysis **Predictive analytics** Churn prediction: - Input: Engagement (login frequency), outcomes (ROI realized), support tickets - Model: Logistic regression, random forest - Output: Risk score 0-100 (predict churn probability) Implementation: 1. Data preparation (historical churn + engagement data) 2. Train model (80% data, test 20%) 3. Validate (accuracy 80%+) 4. Deploy: Weekly refresh, score all customers 5. Action: CSM outreach to top 50 at-risk Cost-benefit: - Cost: Data scientist 12 weeks (£50K) - Benefit: Identify 50 at-risk, save 25 (1% churn save = £100K+ value) - ROI: 2x year 1 Cohort forecasting: - Historical: Q1 cohort 50% month 12 retention - Forecast: Q4 cohort predicts similar (50% month 12) - Refine: If recent improvements, adjust (Q3 cohort 55%)