Building Quantitative Models for Energy Affordability
Building Quantitative Models for Energy Affordability
How predictive modeling and scenario analysis can help utilities better support low-income households
Energy is not just another monthly expense. For many families, it is the difference between a safe home and a risky one. When bills rise faster than income, households face impossible tradeoffs: pay the utility bill or buy groceries, keep the heat on or refill a prescription, avoid arrears or cover rent.
Utilities also face a difficult balancing act. They must manage operational costs, ensure reliability, meet regulatory expectations, and maintain financial sustainability, while protecting customers who are most vulnerable. The good news is that data-driven methods can help utilities act earlier, target support more accurately, and measure what really works.
Quantitative modeling for energy affordability is not about replacing human judgment or community programs. It is about making support systems smarter, faster, and more effective, so customers get help before a crisis becomes unmanageable.
This blog walks through a full, end-to-end approach for building predictive models and running scenario analysis that can improve energy affordability outcomes, especially for low-income households.
Why energy affordability needs a quantitative approach
Traditional affordability approaches often rely on reactive signals: late payments, disconnection notices, collections, or customer calls. By the time these signals appear, the customer may already be under serious stress.
Quantitative models help utilities shift from reactive to proactive. They can support decisions like:
Which customers are most likely to fall behind next month?
Which outreach methods reduce delinquency the most?
What happens if rates increase by 5% or 10%?
How would a new payment plan change arrears and call volume?
Which customers qualify for support but are not enrolled today?
This is not only a customer care improvement. It is also a business improvement. Preventing high-cost escalations reduces repeat calls, avoids service interruptions, lowers debt write-offs, improves customer satisfaction, and strengthens compliance reporting.
Defining “affordability” in measurable terms
Affordability can mean different things depending on policy, regulation, and household circumstances. For modeling, it helps to translate it into measurable signals.
Here are practical affordability metrics utilities can quantify:
1) Energy burden
Energy burden = Annual energy cost / Annual household income
A common benchmark is that households spending more than 6% to 10% of income on energy may be considered burdened.
2) Payment stress indicators
Signals that suggest rising risk:
Increasing past-due balance
Shortening time between bills and payments
More partial payments
Failed payment attempts
Higher proportion of income going toward utility bills
3) Operational hardship signals
These show customer strain even before missed payments:
Higher call frequency
Calls related to payment difficulties
Frequent plan start-stop behavior
Repeated promise-to-pay arrangements
Returned mail or contact instability
4) Disconnection and reconnection risk
For utilities, this is a critical outcome measure:
Disconnection notice issuance likelihood
Disconnection completion likelihood
Reconnection probability and time-to-reconnect
Repeat disconnection cycles
In practice, a single model does not need to cover everything. Most successful programs start by targeting one or two key outcomes and expanding later.
What utilities can model to improve affordability
A strong affordability program typically includes multiple models that work together:
Model A: “Risk of falling behind”
Predicts which customers are likely to become delinquent in the next billing cycle or within 30 to 60 days.
Use cases:
Early outreach
Proactive payment plan offers
Enrollment nudges for assistance programs
Model B: “Risk of disconnection”
Predicts which delinquent customers are most likely to reach disconnection thresholds.
Use cases:
Prioritize intervention
Improve equity outcomes
Model C: “Likelihood of program enrollment”
Predicts which customers qualify for assistance but are unlikely to enroll without targeted help.
Use cases:
Increase assistance participation
Reduce application barriers
Improve outreach efficiency
Model D: “Response to intervention”
Predicts which support action will work best for each customer segment.
Use cases:
Choose between calls vs SMS vs email
Offer best-fit payment arrangements
Target grants for maximum impact
This combination creates a powerful system: identify risk early, intervene intelligently, and continuously learn which actions produce the best results.
The data foundation: what you need and how to use it safely
Affordability modeling can be done with data utilities already have, often without requiring deeply sensitive information.
Core data sources (commonly available)
Billing and payment history
billed amount
due date and payment date
balance, arrears, late fees
payment method and success/failure
Customer account characteristics
rate class (residential/commercial)
service type (electric/gas)
account tenure
premise type (single family, multi-unit when available)
Customer interaction and support history
contact center calls
payment arrangement history
assistance program participation
disputes, extensions, payment promises
External and contextual data
weather and seasonal patterns
inflation or local economic indicators
neighborhood-level census proxies (aggregated and compliant)
energy price and rate changes
One important rule: minimize sensitivity
Even if income indicators are available, it is often safer to model with behavioral signals (payment patterns, arrears slope, bill volatility) rather than direct personal attributes.
The goal is to create an accurate early warning system while reducing privacy risk and ensuring fairness.
Feature engineering that actually drives results
Features are the inputs that help the model learn patterns. Affordability modeling is mostly about capturing “change over time,” not just static values.
Below are highly effective feature groups.
1) Payment behavior trends
number of late payments in last 6 months
average days past due
ratio of paid amount to billed amount
count of partial payments
number of failed transactions
most recent payment gap (days since last payment)
2) Billing volatility
bill amount change month-over-month
3-month rolling average bill
seasonal bill spikes
high consumption anomaly flags
3) Arrears trajectory (the “slope”)
A key signal is whether the past-due balance is stable, decreasing, or accelerating.
arrears increase in last 30 days
arrears increase in last 90 days
percent growth rate in balance
4) Household stability proxies
address changes
returned mail indicator
frequent contact changes
short account tenure
5) Interaction intensity
call count in last 30, 60, 90 days
number of billing dispute calls
payment help call types
6) Program engagement
prior LIHEAP enrollment flag (if applicable)
payment plan history
number of broken arrangements
hardship verification status
In many real systems, the best single predictor is recent trend: how quickly balance is rising, how payments are shifting, and whether bill size changed suddenly.
Choosing the right modeling approach
Utilities often do best with models that are accurate and explainable. The best model is the one your teams can trust, deploy, and operationalize.
Strong starter models
Easy to explain
Works well with engineered features
Quick to validate
High accuracy
Handles non-linear patterns
Strong on structured tabular utility datasets
Best when predicting “time-to-event” like disconnection timing
Helps prioritize urgency
A practical strategy is:
Start with logistic regression baseline
Move to gradient boosting for higher performance
Keep explainability tools like SHAP for transparency
Model outputs must translate into actions
This is where many analytics projects fail: a model produces a risk score, but no one knows what to do with it.
So you need an “action layer” that turns scores into decisions.
Example: a 3-tier intervention framework
Tier 1: Low risk (monitor)
No outreach
Low-touch messages (education, self-service tips)
Tier 2: Medium risk (early support)
Payment reminder messages
Offer flexible payment plan
Provide enrollment link for assistance
Tier 3: High risk (proactive intervention)
Priority outreach by phone
Faster hardship screening path
Instead of a generic outreach blast, you design targeted workflows that match the customer’s predicted risk and predicted response.
Scenario analysis: the tool that helps plan for change
Predictive models answer: “Who is at risk?”
Scenario analysis answers: “What happens if conditions change?”
Utilities deal with changing environments constantly:
rate adjustments
extreme winter or summer seasons
policy changes
economic stress periods
new assistance programs
Scenario analysis allows leadership to evaluate decisions before implementing them.
Common affordability scenarios utilities simulate
1) Rate increase impact
“What happens if rates rise by 5% or 10%?”
projected increase in delinquency
projected increase in disconnection risk
expected increase in call volume
2) Weather-driven bill spikes
“What happens if we get an extreme winter?”
higher consumption periods
higher arrears trajectory
earlier intervention needs
3) Payment plan policy changes
“What happens if we offer a longer-term plan?”
reduced delinquency
improved on-time payment probability
reduced disconnection events
4) Assistance expansion
“What happens if we expand eligibility or simplify enrollment?”
enrollment lift
reduction in past-due balances
avoided collections cost
Building a simple scenario engine (conceptual framework)
To run scenarios, you typically combine:
A bill forecast model (or rule-based estimation)
A customer behavior model (payment probability, delinquency probability)
An intervention effect estimate (how much the program reduces risk)
You can start simple, using elasticities and historical relationships.
Simple example logic
Increase expected bill by rate change (ex: +8%)
Estimate risk uplift using historical sensitivity
Apply intervention effect if action is taken
Aggregate results by segment and geography
You can run these simulations weekly or monthly and produce executive-ready summaries.
Measuring success: what to track after deployment
Affordability modeling only matters if outcomes improve. Your measurement framework should include both customer outcomes and operational outcomes.
Customer outcome metrics
reduction in past-due balances
fewer disconnections
improved payment regularity
increased assistance program enrollment
reduced repeat delinquency cycles
Operational outcome metrics
call deflection (fewer repeated calls)
fewer high-cost escalations
improved agent efficiency
reduced write-offs
better forecasting and planning accuracy
Program metrics
outreach success rate
conversion rate to payment plans
conversion rate to assistance enrollment
drop-off points in enrollment journeys
The most important metric: avoided harm
Even if revenue protection improves, a model is successful when it reduces the likelihood of customers losing essential service.
Fairness, transparency, and trust: non-negotiable requirements
Affordability modeling must be built responsibly. When the model influences outreach and assistance decisions, it becomes a real-world decision engine.
Here are practical safeguards utilities should implement:
1) Explainability
Teams should understand why the model flags a customer.
Top reasons should be visible to agents and program managers
Use interpretable features like “arrears rising for 3 months”
2) Fairness checks
Evaluate outcomes across groups and areas:
Are certain zip codes consistently flagged but not helped?
Are interventions unevenly distributed?
Are disconnection reductions equitable?
3) Human-in-the-loop decisioning
The model should recommend actions, not enforce punitive outcomes.
A safe principle:
Model outputs should increase support, not increase penalties.
4) Privacy and compliance
Use the minimum data needed.
mask sensitive identifiers
limit access to raw data
log decisions and interventions
apply secure governance standards
Trust is built when customers feel the system is designed to help them, not profile them.
A real-world example workflow (end-to-end)
Here is what an operational affordability pipeline can look like.
Step 1: Weekly scoring
compute features from last 90 days
score all active residential accounts
rank by delinquency risk and disconnection risk
Step 2: Segment customers into action groups
Tier 1: monitor
Tier 2: message and plan offers
Tier 3: priority outreach and assistance support
Step 3: Trigger targeted outreach
SMS for reminders and self-service
email for documentation and program links
outbound calls for high-risk customers
Step 4: Track responses and outcomes
did they pay within 14 days?
did they enroll?
did arrears stop growing?
Step 5: Measure intervention effectiveness
compare to a control group where possible
refine strategy monthly
Step 6: Continuous improvement
retrain model quarterly
adjust thresholds seasonally
incorporate feedback from agents and field teams
This becomes a living system that learns and improves over time.
Implementation roadmap: what a utility can do in 6 to 12 weeks
If you want a realistic timeline, here is a simple phased plan:
Weeks 1 to 2: Problem definition + data access
define outcomes (delinquency, disconnection, enrollment)
align with customer care teams
confirm data sources and availability
Weeks 3 to 4: Build features + baseline model
engineer time-based features
train a baseline model
validate performance and stability
Weeks 5 to 6: Deploy scoring + pilot outreach
build batch scoring job
create tier thresholds
pilot with one region or segment
Weeks 7 to 8: Scenario analysis + leadership reporting
build rate/weather scenarios
create dashboards and summaries
recommend policy actions
Weeks 9 to 12: Expand and optimize
refine intervention mapping
add response-to-intervention modeling
measure ROI and customer outcomes
Most importantly, keep it practical and operational from day one.
What “success” looks like in 2025 and beyond
Energy affordability challenges are not going away. Climate volatility, inflation, and evolving rate structures will keep adding pressure to households. Utilities that invest in predictive support systems will be better prepared to protect customers and manage risk.
In 2025, the most effective affordability strategies will look like this:
early risk detection, not late-stage crisis response
targeted support based on customer needs and behavior
scenario planning built into leadership decision-making
measurable outcomes with transparent reporting
customer-first use of analytics to reduce harm
Quantitative modeling does not solve affordability alone. But it makes affordability programs smarter, more proactive, and more equitable.
When done right, it helps utilities deliver what customers need most: stability, dignity, and support when it matters.
Closing thoughts
Energy is essential. Affordability is not a side project. It is a core part of how modern utilities serve their communities.
Predictive modeling and scenario analysis give utilities a powerful way to understand risk early, intervene with precision, and design programs that actually work.
The best outcome is not just lower delinquency. It is fewer families forced into impossible decisions, and fewer households pushed into hardship by a bill they cannot control.
That is the real value of building quantitative models for energy affordability.
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