Data Analytics and Insights for Decision-Making: Using Data Effectively

Building Data Analytics Capabilities for Better Decisions

Using data effectively to drive business strategy and execution. **Data fundamentals** Data strategy: - Identify metrics that matter (actionable, not vanity) - Collect systematically (not ad-hoc) - Analyze regularly (dashboard, weekly/monthly review) - Act on insights (metric → decision → action) - Measure impact (did it work?) Actionable vs vanity metrics: Actionable metric: "CAC is £500" - Decision: If > £1000, reduce marketing. If < £300, increase. - Action: Adjust budget - Impact: Measurable (revenue change) Vanity metric: "10K page views" - Decision: Unclear (is this good? What to do?) - Action: Unclear - Impact: Hard to measure Real example: Metric: "NPS is 32" - Actionable: Yes (target 40, gap 8 points) - Decision: Must improve retention/satisfaction - Action: Interview detractors, improve onboarding - Impact: NPS increases to 40 (measurable) Metric: "We have 1000 active users" - Actionable: Not really (compared to what? Growing?) - Better metric: "Active users grow 20% MoM" (trend clear) - Decision: Growth rate acceptable, continue current strategy - Action: Maintain or accelerate **Data collection and sources** Data sources by topic: Revenue: - Billing system (Stripe, Recurly): Transaction data - CRM (Salesforce): Pipeline, customer lifecycle - Financial system (QuickBooks): Invoices, payments Customer: - Product database: Customer profiles, attributes - Support tickets (Zendesk): Issues, sentiment - Customer survey: NPS, CSAT, feedback Product usage: - Analytics tool (Mixpanel, Amplitude): User events, features - Website analytics (Google Analytics): Traffic, conversions - Log files: System performance, errors Operations: - HR system: Headcount, salaries - Git (code repository): Development metrics - Project management (Asana): Team productivity Integration: - Data warehouse (Snowflake, Redshift, BigQuery): Centralize all data - API connections: Tools connect to warehouse - Result: Single source of truth (all metrics from one place) **Analytics tools by need** Early stage (< £1M revenue): Tools: - Google Analytics: Web traffic (free) - Stripe dashboard: Payment data (free) - Spreadsheet (Google Sheets): Manual analysis (free) - Segment: Event collection (free tier) Cost: £0 (free tools) Limitation: Manual, limited analysis Growth stage (£1-10M revenue): Tools: - Mixpanel/Amplitude: Product usage analytics (£500-5000/month) - Tableau/Looker: Dashboards (£500-2000/month) - Data warehouse (Snowflake): Central data (£1000-5000/month) - Custom queries (SQL): Ad-hoc analysis Cost: £2-10K/month Benefit: Automated, comprehensive, self-service Mature stage (£10M+): Tools: - Enterprise analytics (same as above, scaled) - Data scientist (hire): Advanced analytics - ML/AI (build): Predictive models Cost: £10K-100K+/month Benefit: Advanced insights, predictive, competitive advantage **Analytics frameworks** Framework 1: Descriptive analytics Question: "What happened?" Examples: - Revenue last month: £100K (down 10% from prior month) - Churn: 5% (up from 3% prior month) - NPS: 32 (down from 35) Output: Dashboard (current state) Limitation: Reactive (know problem after it happens) Framework 2: Diagnostic analytics Question: "Why did it happen?" Examples: - Revenue down 10%: Why? (check: churn up, new customers down) - Churn 5% up: Why? (interview customers: missing features, poor support) - NPS down: Why? (detractor reasons: slow response time, bugs) Output: Root cause analysis (understand problem) Improvement: More proactive (know cause, can address) Framework 3: Predictive analytics Question: "What will happen?" Examples: - If churn stays 5%, revenue will be £900K next month (down 10%) - If CAC stays £500, payback period will be 14 months - If NPS stays 32, predict 8% annual churn Output: Forecast (future state) Benefit: Predict consequences (easier to decide) Framework 4: Prescriptive analytics Question: "What should we do?" Examples: - Revenue declining → Recommendation: Reduce CAC or increase retention - Churn 5% → Recommendation: Improve onboarding (interview says setup hard) - NPS 32 → Recommendation: Faster support, product improvements Output: Action recommendation (what to do) Benefit: Actionable insights (decision is clearer) **Building analytics capability** Phase 1: Foundation (months 1-3) Goal: Collect and visualize key metrics Actions: - Identify 5-7 key metrics (revenue, churn, CAC, NPS, runway, growth, margin) - Set up collection (API integrations, manual data entry) - Build dashboard (Google Sheets or Looker) - Weekly review (CEO looks at metrics) Cost: £0-1K (mostly tools, some setup time) Output: One-page dashboard (understand current state) Phase 2: Insight generation (months 4-9) Goal: Move from descriptive to diagnostic Actions: - Monthly analysis: Why did metric move? - Cohort analysis: Which customers/segments different? - Correlation analysis: What drives which metrics? (churn + NPS related?) - A/B tests: Test changes, measure impact Cost: £2-5K (analytics tools, maybe analyst hire part-time) Output: Insights (understand drivers) Phase 3: Prediction (months 10-18) Goal: Forecast, plan proactively Actions: - Build forecasts (revenue, churn, runway) - Scenario planning: "If X, then Y" - Identify leading indicators (what predicts future?) - Trend analysis: Is trend improving or declining? Cost: £5-20K (data scientist time, advanced tools) Output: Foresight (predict future) Phase 4: Prescription (18+ months) Goal: Automated decision-making Actions: - Build models: "If churn up, send CS outreach" (automated) - ML predictions: "Customer X is high churn risk" (flag for action) - Optimization: Automated budget allocation to best channels - Experimentation platform: Auto-test features, measure impact Cost: £20-50K+ (data scientist team, infrastructure) Output: Actionable intelligence (decisions made automatically) **Data-driven decision process** Weekly rhythm: Monday: Collect - Pull data from all systems - Update dashboard - Identify variances (metric off-target?) Tuesday: Analyze - If variance >5%, investigate - Root cause analysis (dashboard → deep dive) - Generate insights Wednesday: Decide - Review findings (leadership meeting) - Decide on action (if needed) - Assign owner Thursday-Friday: Execute - Implement decision - Track impact (does action improve metric?) - Report back (next week, was it effective?) Example: Monday: - Dashboard shows churn 6% (target 3%) - Variance: +3% (problem) Tuesday: - Analyze: Why up? - Interview churned customers: "Product slow", "missing feature", "poor support" - Root cause: Mix of product and support Wednesday: - Decide: (1) Fix performance (product team), (2) Hire support (ops) - Owner: Product lead for perf, ops lead for support - Timeline: 2-week plan Thursday-Friday: - Execution starts - Measure: Track churn weekly Following week: - Churn: 5.5% (improving, not there yet) - Continue actions - Expect 3% in 4 weeks (when changes land) **Common analytics mistakes** Mistake 1: Too many metrics - Problem: 50-metric dashboard (overload, don't know what matters) - Fix: 5-7 key metrics (focus) - Impact: Clear priorities, easier to manage Mistake 2: No baseline - Problem: "Churn is 5%, is that good?" - Fix: Set target (target 3%), benchmark (industry 4-5%) - Impact: Clear accountability (on-track or off-track) Mistake 3: Measure without action - Problem: Dashboard shows CAC £1500 (off-target), no action - Fix: Metric → decision → action (improve channels, reduce CAC) - Impact: Data drives real change Mistake 4: No data quality - Problem: Data incomplete, inconsistent (different systems show different numbers) - Fix: Data warehouse (single source of truth), data validation (clean data) - Impact: Trust in data (decisions are credible) Mistake 5: Ignore outliers - Problem: One customer churned (normal), but why? - Fix: Investigate outliers (often signal bigger problem) - Example: One customer churned because "feature broken" → affects everyone