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Energy Efficiency Manager

Targeting and marketing programs to eligible customers

Enhances✓ Available Now

What You Do Today

Identify which customers would benefit most from efficiency programs, target marketing to reach them, and overcome the behavioral barriers that keep people from participating.

AI That Applies

AI analyzes usage patterns to identify high-saving-potential customers, personalizes program recommendations, and optimizes marketing channel selection by customer segment.

Technologies

How It Works

The system ingests usage patterns to identify high-saving-potential customers as its primary data source. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Marketing is precision-targeted. AI identifies specific customers who would benefit from specific measures based on their actual usage patterns.

What Stays

Understanding behavioral barriers and designing program approaches that overcome inertia. Getting people to act on energy efficiency requires psychology, not just data.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for targeting and marketing programs to eligible customers, understand your current state.

Map your current process: Document how targeting and marketing programs to eligible customers works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Understanding behavioral barriers and designing program approaches that overcome inertia. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support customer analytics tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long targeting and marketing programs to eligible customers takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

What are the top 5 reasons customers contact us, and which of those could be resolved without a human?

They're prioritizing which operational processes to automate

your process improvement or lean lead

How do we currently measure service quality, and would AI-assisted responses change that measurement?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.