Live Workshop Series

From Intelligence to Impact: Turning AMI Data Into Higher-Impact Programs

Learn how to move from broad averages, census data, misspent incentives, and self-reported eligibility toward premise-level intelligence that shows what's really happening behind-the-meter.

Utility Program Managers Grid Planning Teams DSM & Customer Program Staff

7 Sessions Standalone modules

30 min Per Session Live & interactive

Free For Utilities Register below

Choose Your Workshop

Attend one session or follow the full series from targeting through continuous optimization.

Filter:

◷ 30 min

Flexible Load & Demand Response

Flexible Load

Identify customers with the greatest flexible load potential and maximize measurable peak reduction through smarter targeting and program design.

5 agenda items View Details →

Objective

Enrollment alone doesn’t deliver flexibility. Find the customers with real shiftable load, then use premise-level insights to sharpen EV, TOU and demand response recruitment, improve peak reduction and inform grid planning.

What You'll Learn

Explain why EV ownership and enrollment counts alone are insufficient for managed charging and grid planning
Identify behind-the-meter signals that reveal meaningful load flexibility and customer shiftability
Build adaptive recruitment strategies that prioritize measurable program impact over participation volume
Apply premise-level intelligence to improve managed charging, demand response, and TOU performance

Agenda

Why customer counts aren't enough: moving beyond EV ownership and demographic targeting
Finding the customers who matter most: appliance detection, charging behavior, cooling load
Case study: NV Energy — from 33,000+ detected EVs to the 1,000 driving 13% of peak EV demand
Prioritizing impact over participation: building a shiftability funnel
Workshop exercise: design a flexible load recruitment strategy for your service territory

Key Takeaway

The most successful flexible load programs don't recruit the most customers — they recruit the right customers.

◷ 30 min

Weatherization & Inefficient Homes

Weatherization

Understand which homes are driving the greatest weather-sensitive load, appliance inefficiency, and peak demand — and what intervention delivers the greatest impact.

4 agenda items View Details →

Objective

Limited budgets demand smarter targeting. Pinpoint homes with the greatest weather-sensitive load and appliance inefficiencies, prioritize the right interventions, and measure the impact on savings, comfort, affordability and peak demand.

What You'll Learn

Identify homes with the greatest weather-sensitive load and savings opportunities
Apply premise characteristics to sharpen weatherization targeting and avoid double-incentivization
Use interval data to establish pre-treatment baselines and track persistence

Agenda

Why broad eligibility criteria underperform against limited weatherization budgets
Premise-level indicators: heat loss, envelope inefficiency, weather-normalized savings potential
Sharpening the list: home size, ownership status, year built
Case reference: Avista's targeting task cut from 160 hours to 3, unlocking 1+ MW in savings

Key Takeaway

The biggest constraint on weatherization ROI is usually targeting speed and precision, not incentive size.

◷ 30 min

Improving Targeting to Achieve Affordability Goals

Affordability

Move from coarse census targeting to premise-level affordability intelligence.

4 agenda items View Details →

Objective

ZIP codes don’t tell the whole story. Combine premise-level energy, billing and customer insights to identify households facing high energy burdens, match them with relevant programs, and make affordability outreach more effective.

What You'll Learn

Explain the limitations of census- and ZIP-based affordability targeting
Build income clusters using premise-level signals for outreach and program fit
Understand the boundary between prioritizing outreach and verifying income

Agenda

Where census and ZIP-code targeting miss the households who need help most
Premise-level signals: income indicators, energy burden, arrearage, program participation history
From data to action: building income clusters for community-based organizations
Ethics and boundaries: why this approach prioritizes, but does not verify, income

Key Takeaway

Premise-level affordability signals improve program fit and equity alignment — they're a targeting tool, not an eligibility verification tool.

◷ 30 min

Program Planning & Simulation

Program Planning

Design smarter customer programs by modeling participation, energy savings, peak demand reduction, and outcomes before launch.

4 agenda items View Details →

Objective

Know what will work before you launch. Model customer participation, savings, peak demand and budget impact using premise-level intelligence to compare scenarios, sharpen incentives and invest in higher-performing programs.

What You'll Learn

Explain how premise-level intelligence improves program planning vs. traditional forecasting
Evaluate alternative program scenarios using customer segmentation, appliance ownership, and load characteristics
Estimate participation, energy savings, demand reduction, and budget impacts before program launch
Prioritize investments that maximize customer engagement while improving cost-effectiveness

Agenda

Pitfalls in traditional program planning: limitations of demographic assumptions
Building better program scenarios: selecting segments, modeling participation potential
Balancing customer outcomes and operational objectives
Scenario comparison workshop: compare multiple program designs

Key Takeaway

The best-performing programs aren't created after launch — they're designed before launch.

◷ 30 min

Measurement & Verification (M&V)

M&V

Measure program performance with confidence and use near-real-time insights to continuously optimize customer programs.

5 agenda items View Details →

Objective

M&V shouldn’t end with a report. Use appliance-level intelligence, baselines and treatment comparisons to verify savings, understand what’s working and improve targeting and program performance while programs are still running.

What You'll Learn

Explain the difference between traditional retrospective evaluation and continuous M&V
Establish meaningful baselines using premise-level customer data
Measure program performance using before-and-after analysis and treatment/control comparisons
Attribute savings and demand reduction to specific appliances, customer actions, and interventions

Agenda

Why traditional M&V isn't enough: annual reporting vs. continuous operational improvement
Building a defensible measurement framework: baselines, treatment and control groups
From savings to customer behavior: appliance-level attribution
Closing the optimization loop: refining targeting strategies, improving recruitment quality
Case studies: continuous M&V across electrification, demand response, weatherization

Key Takeaway

Measurement & Verification isn't the final step in a program — it's the feedback loop that makes every future program better.

◷ 30 min

Building a Scalable, Data-Driven Program Strategy

Strategy

Develop a repeatable framework for identifying opportunities, prioritizing investments, recruiting the right customers, measuring outcomes, and continuously improving utility programs.

5 agenda items View Details →

Objective

Turn premise-level insights into a repeatable program strategy. Connect opportunity identification, customer targeting, recruitment, measurement and optimization across programs to improve outcomes and maximize the value of AMI investments.

What You'll Learn

Connect premise-level analytics across the entire utility program lifecycle
Develop a repeatable framework for planning, targeting, launching, measuring, and refining programs
Identify opportunities to apply Analytics Workbench across electrification, demand response, affordability, and weatherization
Build organizational alignment around a shared analytics platform

Agenda

Bringing the pieces together: reviewing the Analytics Workbench program lifecycle
Designing a modern utility program: identify, prioritize, recruit, launch, measure, optimize
One platform, multiple programs: applying a common analytics foundation
Interactive workshop: participants work through a sample utility challenge
Developing your Analytics Workbench roadmap: immediate opportunities and phased adoption

Key Takeaway

By applying a consistent, premise-level analytics framework across every stage of program delivery, utilities can improve targeting, increase program performance, and create a continuous cycle of learning and optimization.

Move from Broad Averages to Premise-Level Intelligence

Each session is available standalone. Attend one or all eight. Free for utility program managers, grid planning teams, and DSM staff.

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