The Macro Forecasting model offers a clear view into future pipeline and booking expectations by predicting values for CQ, NQ, NQ+1 & NQ+2 for key metrics: Generated Pipeline Volume, Generated Pipeline Value, Generated Booking Volume, and Generated Booking Value.
Objective
The model estimates end-of-quarter pipeline and booking potential across the next four quarters, providing visibility into upcoming demand generation capacity.
Key Steps and Principles
-Data Preparation
-Collect historical records of sales activity, marketing campaigns, and macroeconomic indicators.
-We use daily-level datasets with at least two years of historical data.
Engineered features such as:
-Active sales reps or BDRs
-Spend and impressions across channels.
-Open pipeline volume and value at each stage
Macroeconomic factors like interest rates and GDP growth
Calendar attributes such as day-of-quarter, day-of-month, week-of-quarter, month-of-year, etc.
Expected time series forecasts for the quarter
Model Training and Selection
-Separate models are trained for each combination of metric (value/volume) and funnel stage (pipeline/booking), and for each quarter (CQ to NQ+2).
-Machine learning regression models are trained to address specific forecasting objectives. These models automatically manage outliers and are tuned to maximize predictive performance.
Forecast Scoring and Output
-Trained models are applied daily to most recent data to generate forecasts
-Forecasts show the end-of-quarter expectations from the latest date in the current quarter (CQ) for CQ, NQ, NQ+!, NQ+2.
Output Utilization in Pipeline Projection
The macro forecast model is a key component of the broader "Pipeline Projection" system. While record-level predictions capture the readiness of individual leads and opportunities, the macro forecast ensures these projections are grounded in larger patterns observed across marketing and sales activity.
Conversion probabilities from the Lead and Opportunity Propensity models are multiplied by predicted opportunity values (from the Opportunity Size Prediction model).
These record-level projections are aggregated by quarter to generate raw quarterly forecasts.
The macro forecast model provides scaling factors based on historical patterns, economic trends, and demand generation behavior, both at Volume and $ Value level.
The aggregated projections from record-level projections are compared against the macro model forecasts to come up with a scale factor that is applied on the record-level projections.
Record-level projections are then adjusted by the scale factor such that the aggregated quarterly projections match the macro model forecasts.
This reconciliation process between the micro and macro models ensures that the total projections in pipeline readiness and booking readiness modules reflect realistic, quarter-aligned expectations.
Interpretation
The model’s output refines the projected value and volume of pipeline and bookings for CQ, NQ, NQ+1, and NQ+2.
Retraining and Scoring Frequency
Retraining: Once per quarter in sync with the projection engine; earlier retraining may occur when there are configuration or data schema changes.
Scoring: Daily, using the most up-to-date information.