#forecasting

Build a 3-year financial forecasting model for a SaaS startup. Inputs: 1. Customer Acquisition Cost (CAC). 2. Monthly Recurring Revenue (MRR) growth rate. 3. Churn rate. 4. Headcount expansion plan. 5. Operating expenses (OpEx). 6. Server/infrastructure costs. Outputs: 1. Cash flow statement. 2. P&L statement. 3. Burn rate analysis. 4. Break-even point calculation. Include sensitivity analysis scenarios.

Create comprehensive financial forecasting system. Components: 1. Build three-statement model (P&L, Balance Sheet, Cash Flow). 2. Implement driver-based forecasting with key assumptions. 3. Create best-case, base-case, and worst-case scenarios. 4. Add sensitivity analysis for critical variables. 5. Include variance analysis comparing actuals vs forecast. 6. Build executive dashboard with KPIs and trends. 7. Implement rolling forecasts with monthly updates. 8. Add Monte Carlo simulation for risk assessment. Use Excel with VBA or Python with Pandas.

Build a time series forecasting model using Facebook Prophet. Steps: 1. Prepare historical sales data with daily granularity. 2. Add custom seasonality for Black Friday and holiday peaks. 3. Include external regressors (marketing spend, weather). 4. Generate 90-day forecast with uncertainty intervals. 5. Validate model using cross-validation and MAPE metric. Visualize actual vs predicted with interactive Plotly charts.

Build time series forecasting models using statistical methods and deep learning for accurate predictions. Time series analysis: 1. Stationarity testing: Augmented Dickey-Fuller test, p-value <0.05 for stationarity. 2. Differencing: first-order differencing, seasonal differencing, achieve stationarity. 3. Decomposition: trend, seasonality, residuals, STL decomposition, seasonal pattern identification. Classical methods: 1. ARIMA modeling: AutoRegressive Integrated Moving Average, parameter selection (p,d,q). 2. Seasonal ARIMA: SARIMA(p,d,q)(P,D,Q,s), seasonal parameters, model selection using AIC/BIC. 3. Exponential smoothing: Holt-Winters method, alpha/beta/gamma parameters, trend and seasonality. Deep learning approaches: 1. LSTM networks: sequence modeling, forget gate, input gate, output gate mechanisms. 2. GRU (Gated Recurrent Unit): simplified LSTM, fewer parameters, faster training. 3. Transformer models: attention mechanism for sequences, positional encoding, parallel processing. Feature engineering: 1. Lag features: previous values, window sizes 3-12 periods, correlation analysis. 2. Moving averages: simple MA, exponential MA, different window sizes (7, 30, 90 days). 3. Seasonal features: month, quarter, day of week, holiday indicators, cyclical encoding. Model evaluation: 1. Mean Absolute Error (MAE): average prediction error, interpretable units. 2. Root Mean Square Error (RMSE): penalize large errors, same units as target. 3. Mean Absolute Percentage Error (MAPE): percentage error, scale-independent, <10% excellent. Cross-validation: time series split, walk-forward validation, expanding window, out-of-sample testing for reliable performance assessment.

Qualify opportunities with MEDDIC. Metrics: quantifiable business impact ('20% faster processing'). Economic Buyer: identify and engage decision maker who controls budget. Decision Criteria: understand evaluation process, scoring matrix, must-haves. Decision Process: map timeline, stakeholders involved, approval steps. Identify Pain: technical and business pain points, implications if unsolved. Champion: find internal advocate who sells on your behalf. Score each element 0-10. Deals below 40/60 need more qualification. Update after every call. Forecast only MEDDIC-qualified deals. Prevents wasted time on unwinnable deals.

Build reliable revenue forecasting system. Forecast categories: Commit (90%+ confidence, MEDDIC qualified). Best Case (50-70% confidence, active engagement). Pipeline (30-50% confidence, early stage). Methodology: 1. Reps submit weekly forecasts by category. 2. Manager reviews with rep 1:1 (challenge assumptions). 3. Roll up to leadership with commentary. 4. Track accuracy weekly (forecast vs. actuals). 5. Calculate variance (over-forecast or under-forecast). Improve accuracy: require evidence for Commit (verbal confirmation, signed terms). Stage-weight deals (10% for discovery, 50% for proposal, 90% for negotiation). CRM hygiene (close dates, next steps, MEDDIC scores current). Sanity check: Commit should be 70-90% of quota monthly. Review missed forecasts in post-mortems.

Optimize sales pipeline in CRM (Salesforce, HubSpot). Best practices: 1. Define clear stage criteria (qualification, demo, proposal, negotiation, closed). 2. Set expected close dates and deal values. 3. Track activities (calls, emails, meetings). 4. Automate follow-up reminders. 5. Forecast revenue by stage probability. 6. Identify bottlenecks and drop-off points. 7. Regular pipeline reviews (weekly). Maintain data hygiene. Archive stale deals. Focus on high-value, high-probability opportunities. Target 3x pipeline coverage.