From 1,227 City Leads to 30 Laser-Targeted Texas Counties: A Roadshow You Can Bank On

TL;DR

I started with 1,227 “hot” municipal leads.
I built a county‑level model that blends EPA non‑compliance, CWSRF activity, inbound interest, and population change.
The output is a ranked list of ~30 Texas counties to anchor a 2–3 day roadshow with partner outreach.

Why This Project?

Moving from classic “tech” sales into utilities tech forces you to slow down. In utilities, you do not sell to one account. You sell into a small ecosystem:

Buyer: the city or utility district
Planner: the engineering firm that scopes and stamps projects
Legislator/Regulator: the folks who approve, fund, or block

My job was to turn an EPA list into meetings. After ~6 weeks, I had mixed results. Some cities engaged. Others did not.

Problem

When I planned a small roadshow for the AE after a conference, I saw how far apart the cities were and calculated how much time and money we would spend for very few meetings. This showed me I needed a better targeting approach than the EPA list.

Discovery

The key question was: Where should we focus in Texas to plan a roadshow?

Data sources:

EPA Lagoon Inventory (2023): non-compliant lagoon flags per city
CWSRF: Clean Water State Revolving Fund activity signals
HubSpot interest log: cities that showed inbound interest (not shown here)
Population growth by city: Wikipedia scrape to create a simple growth score
City-to-County map: to roll signals up to the county level

Steps Taken:

Filter cities to the ICP window we had set.
Aggregate by county, count ICP-fit cities, then pull the top 10.

3. Connect EPA and CWSRF data to the cities database, then find the top counties.

4. Based on conversations with our Engineering team, we know that cities with the most significant population growth or decline will need our solution, so we know where it happens.

Col1 = County

Col2 = Population Change Metric

Col3 = City population

=QUERY(

{D2:D, O2:O, F2:F},

"select Col1, max(Col2)

where Col2 is not null

and Col3 >= 500

and Col3 <= 100000

group by Col1

order by max(Col2) desc

limit 10",

)

5. to see which counties face current challenges, based on city interest.

Results

Stakeholder alignment: shift from ‘Texas’ to specific counties.
Sales ops impact: created a repeatable template. When new data lands, we drop it in, and the model updates.
Produced a target list of counties with city shortlists for roadshow planning.
Shortlisted engineering firms per target county for partner outreach.
A way of targeting that we can duplicate in different states.

The Big Result:

We were able to define our TAM, SAM, and SOM for the first time. More importantly, we built the infrastructure to forecast when a city will need us and when to approach our ICP, saving resources.

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