This document outlines the methodology behind predictive models, designed to help revenue teams make informed decisions based on data-driven foresight. The goal is to prioritize high-potential leads and opportunities, forecast demand from ongoing efforts, and generate accurate pipeline projections.
Note on Modeling Approaches:
Wherever applicable, RevSure uses both heuristic and machine learning approaches. Machine learning is the default when sufficient historical data (typically 2 - 3 years) is available. In scenarios with limited history, heuristic logic is used to ensure reliable predictions.
Lead Propensity
The Lead Propensity Model estimates the likelihood of a lead moving to the next stage in the funnel. By analyzing past behaviors and funnel transitions, we assign each lead a probability score that reflects its potential for conversion.
How Scores Are Assigned: Lead Propensity could be generated using heuristic or machine learning approaches to assign scores. By default, we use the machine learning model, provided there is at least 2–3 years of historical data available. If sufficient history isn’t available, we fall back on the heuristic method to ensure reliable estimates.
In the heuristic version, historical conversion rates and average opportunity sizes are computed for different lead segments (e.g., by company size, industry and various persona). These values are then used to calculate the projected value by multiplying the number of open leads with historical conversion and opportunity size estimates (mentioned more about this in the pipeline projection section).
In the machine learning version, a classification model is trained on enriched lead data to estimate conversion probabilities. The model learns from patterns of previous lead behaviors, such as which leads converted in the past, how quickly, and with what factor being impactful. The model outputs a probability that reflects the likelihood of the lead progressing to the next stage. These scores are then computed across multiple quarters (CQ, NQ, NQ+1, NQ+2), representing the likelihood of conversion within each specific quarter.
Data Inputs:
Lead attributes: source, region, company size, industry
Activities and campaign interactions
Funnel timestamps: lead creation date, MQL date, SQL date
Derived metrics: stage duration, time to quarter-end, velocity of movement
Retraining Frequency: Once every quarter during Projection Engine training, or earlier if there are changes in data configurations.
Scoring Frequency: Daily
Interpretation:
A higher score indicates a greater chance of the lead moving to the next stage. Teams should prioritize outreach for leads with high scores and review or nurture those with lower scores.
Opportunity Propensity
The Opportunity Propensity Model predicts how likely an opportunity is to close within each of the next four quarters. It takes into account the opportunity's lifecycle and characteristics to estimate conversion likelihood over time.
How Scores Are Assigned: The scoring methodology for Opportunity Propensity closely mirrors that of Lead Propensity but is enriched by a wider and deeper set of data. While both models learn from past conversions, Opportunity Propensity incorporates more granular and diverse features, such as opportunity type, forecast category, and detailed stage progression, to generate more context-aware predictions. Each opportunity is scored for each of the next four quarters (CQ, NQ, NQ+1, NQ+2), representing the probability of its closing in each respective quarter.
Data Inputs:
Opportunity creation date, close date, forecast category, opportunity type, CRM-provided probability, focus quarter features i.e. how far is the assigned close date quarter
Stage data: current and past stage timestamps, stage sequence
Derived metrics: days in stage, time to close, time to quarter-end
Retraining Frequency: Once every quarter during Projection Engine training, or earlier if there are changes in data configurations.
Scoring Frequency: Daily
Interpretation:
Scores help assess which deals are most likely to close. Focus attention on opportunities with strong probabilities in the current or upcoming quarters.
Demand Generation Potential
This model forecasts the additional pipeline and booking value to the pipeline from outside the funnel. These unseen opportunities can originate from new leads, or ongoing activities. Since these deals do not yet exist in the system, the projection is made at a quarterly level rather than at the individual lead or opportunity level.
How the Methodology Works: We use two approaches, heuristic and machine learning, to estimate unseen contributions.
1. Heuristic Approach:
For each day in a quarter, we calculate the proportion of pipeline or bookings that originated from leads/opportunities not present at the beginning of the quarter.
We apply this average on a rolling basis from the current day to the end of the quarter to project how much additional pipeline or bookings can be expected.
2. Machine Learning Approach:
A regression model is trained using historical patterns of unseen contribution.
The model uses date-based features and cumulative actual pipeline/booking data to predict unseen volume/value percentages.
Retraining Frequency: Once every quarter during Projection Engine training, or earlier if there are changes in data configurations.
Scoring Frequency: Daily
Interpretation: This model provides a forward-looking estimate of pipeline and bookings that are likely to materialize later in the quarter.
Opportunity Size Prediction
This module helps estimate the likely dollar value of a deal based on what we already know about the opportunity. Not every opportunity comes with a reliable booking amount, especially those in the early stages of the funnel. This model helps fill that gap.
How Scores Are Assigned: We use two complementary approaches:
Heuristic Approach: Looks at similar past opportunities grouped by key attributes (like industry, company size, product) and calculates an average booking amount. This value is then assigned to open opportunities that share those attributes.
Machine Learning Approach: Learns from patterns across a broader set of features (e.g., lead source, persona, product history) to predict the most likely booking amount for each opportunity.
Data We Use:
Opportunity fields: type, industry, company size, lead source, country, etc
Historical booking values and conversion patterns
Product details and predicted associations (if available)
Retraining Frequency: Once every quarter during Projection Engine training, or earlier if there are changes in data configurations.
Scoring Frequency: Daily
Interpretation: This model ensures that each opportunity, even if incomplete, has a realistic projected dollar value. This enhances downstream projections and provides a stronger view of revenue potential.
Pipeline Projection
The Pipeline Projection module provides a clear view of how much pipeline and bookings are expected to convert in the upcoming quarters. It helps answer: "How ready is our pipeline to convert to real revenue?"
What It Does: This model shows both pipeline readiness and booking readiness. For each of the next four quarters (CQ, NQ, NQ+1, and NQ+2), it aggregates the projected value and volume of deals expected to close.
How It Works:
It uses the conversion probabilities generated from the Lead and Opportunity Propensity models.
Each lead or opportunity is assigned a projected value using the Opportunity Size Prediction model.
To ensure forecasts are accurate and reliable, a reconciliation step is applied. This step fine-tunes the model predictions based on recent historical behavior using heuristic benchmarks. It works by comparing model-based predictions with simple averages observed for similar leads or opportunities in the past. If the model has been too aggressive or too conservative for a certain segment, this process scales the scores accordingly. As a result, the final projections reflect not only the intelligence of machine learning but also the practical patterns of real-world performance, providing front-line teams with a grounded and trustworthy forecast. This integrated approach blends granular insights with macro trends to deliver precise and strategic forecasting.
Then we take the product of value calculated and the likelihood of conversion to estimate its contribution.
These individual contributions are then aggregated by quarter to produce overall pipeline and booking projections.
The projections undergo careful scaling, informed by the output of a predictive model used for macro adjustments.
In addition to the previously calculated contribution, the Demand Generation Potential observed for the quarter from the corresponding day is subsequently included.
Retraining Frequency: Once every quarter, or earlier if there are changes in data configurations.
Scoring Frequency: Daily
Interpretation: This model gives a quarterly view of how much pipeline and bookings are likely to materialize, combining both projected readiness volume and value.