The Target List Is Not the Deliverable

Executive Summary

• A diagnostic — the Same List Test — for determining in thirty seconds whether your target industry study describes your region or describes the American industrial economy
• An explanation of why conventional screening methodology converges on the same six sectors across regions with genuinely different economies, and why that convergence is structural rather than a failure of execution
• The distinction that governs everything downstream — attractiveness versus winnability — and why a target you cannot physically serve is not a stretch goal but a fiction
• A six-test winnability screen covering power, sites, water, labor, operating cost, and logistics, with the failure mode each test catches and the source that answers it honestly
• A four-quadrant framework for allocating business development capacity, including the overlooked quadrant where resource-constrained regions actually win projects

Opening: The Same List Test

Here is a target industry list. It is not from a real study, but you have read it.

Advanced manufacturing. Logistics and distribution. Life sciences. Value-added agriculture. Information technology and data centers. Professional and business services.

Now take your own region's list and hold it next to that one. Then do the harder version of the exercise: take your list, cross out your region's name at the top, and write in the name of the county two hours down the interstate — the one you lose projects to.

I want to be careful here, because this is easy to misread as a criticism of the people who do this work. It is not. I have run this analysis, I have written these lists, and the methodology behind them is sound. Location quotient analysis, shift-share decomposition, and cluster mapping are legitimate tools that answer the questions they were built to answer.

The problem is what happens next. The study is delivered, the board receives it warmly, six sectors are adopted, and eighteen months later the organization is chasing three of them with no more success than before — because nobody asked whether the region could deliver what those sectors require. The list named what was growing. It never named what could be won.

Section 1 — Why Every List Converges

The Machinery Produces the Consensus

Convergence is not sloppiness. It is what the standard toolkit is built to produce, and understanding why is the first step to getting something better.

Location quotient measures you against the nation. An LQ tells you which sectors are more concentrated in your region than in the country as a whole. That is useful. But every region in the country is measured against the same denominator, so the exercise reliably surfaces the same broad categories — and in a Midwest with a shared industrial history, neighboring regions frequently surface identical ones.

Shift-share tells you what already happened. Decomposing growth into national, industrial, and regional components is a good way to understand the last decade. It is a weaker way to choose the next one, and it systematically rewards sectors that have already arrived somewhere.

Growth overlays import the consensus directly. When the screen weights sectors by national growth forecasts, it is weighting by exactly the same published projections every other region's consultant is reading. A sector that appears on your list because it is forecast to grow nationally confers no positioning, because it appears on everyone's list for the same reason.

Run identical methodology against a shared national dataset and you should expect similar outputs. The machinery is working correctly. It is simply answering a question about the national economy that has been mistaken for a question about your region.

The Incentive Problem Inside the Engagement

There is a second reason, and it is uncomfortable to write as someone who sells this work.

A target industry study is commissioned by an organization, approved by a board, and often presented at a public meeting. The sectors the board is already excited about — the ones in the strategic plan, the ones in the governor's speech, the ones the chamber has been talking about — carry political weight before the analysis begins. A study that confirms them gets adopted. A study that says the region cannot win three of the five things it is currently chasing gets litigated, softened, and eventually shelved.

So the incentive runs toward a list that is defensible, inclusive, and comfortable. Every sector with a constituency makes the cut. The document is analytically unimpeachable and strategically inert, and everyone involved has behaved reasonably.

Section 2 — Attractiveness Is Not Winnability

Two Different Questions

Conventional screening answers one question well: is this sector growing, and do we have some existing presence in it? Call that attractiveness. It is a question about the sector.

There is a second question the standard methodology does not ask: if a company in this sector ran a site search tomorrow, could we survive the first cut? Call that winnability. It is a question about you.

These are independent. A sector can be enormously attractive and completely unwinnable for your region, and nothing in an LQ table will tell you so. The two get conflated because attractiveness is measurable from a desk with purchased data, and winnability requires knowing uncomfortable things about your own infrastructure.

The Fiction Target

The clearest example is the one most regions are living through right now.

A region adds data centers to its target list. The reasoning is not stupid — fiber runs through the county, the natural disaster profile is favorable, the state has a sales tax exemption on equipment, and every peer organization is chasing the same thing. Attractiveness is genuinely high.

Then look at delivery. The largest interconnection the utility can serve inside thirty-six months is eighteen megawatts. The nearest transmission-adjacent site with sufficient acreage is under three separate ownerships and has never been optioned. Nothing in the county has been through a load study.

The distinction matters because the two conditions call for opposite responses. An unwinnable target that is close to winnable is an infrastructure roadmap — this is what we would have to build, here is the cost, here is the timeline, here is the board decision. An unwinnable target that is nowhere near winnable is a target to drop. Both are useful. Neither is available if the study never distinguished them.

Section 3 — The Winnability Screen

Winnability is not a feeling and it is not a SWOT exercise. It is six deliverability questions, each with a specific failure mode and a source that answers it honestly. Every candidate sector gets run against all six, scored against that sector's actual requirements rather than against a generic industrial profile.

The Six Tests

Why the Two Scores Never Get Averaged

This is the single most important mechanical rule in the framework, and it is where most scoring exercises quietly fail.

The instinct is to build a composite — weight attractiveness and winnability, produce one number per sector, rank the list. Do not do this. A composite score is an averaging device, and averaging is precisely how a fatal flaw disappears. A sector that scores a nine on attractiveness and a two on power comes out as a mid-table five-and-a-half and stays on the list, when the correct output is that it is disqualified until the power condition changes.

Score the two axes separately, plot them, and let the quadrants do the work.

Section 4 — The Four Quadrants

Plotting attractiveness against winnability produces four positions, and each one implies a different allocation of an organization's scarcest resource, which is staff attention.

Pursue — High Attractiveness, High Winnability

Real targets. This quadrant is almost always smaller than the board expects — frequently two or three sectors rather than six or eight — and the narrowness is the point. These get named company universes, mapped sites, and the majority of business development capacity.

The Trap — High Attractiveness, Low Winnability

This is where most target lists live and where most business development budgets die. The sectors here are genuinely exciting, they poll well with the board, and the region cannot serve them. Time spent here produces conference attendance, marketing spend, and RFI responses that die at the first technical screen without ever generating a debrief that explains why.

Each target in this quadrant deserves a specific verdict rather than continued drift: either a costed infrastructure roadmap that would move it into the first quadrant, or removal from the list.

Overlooked — Low Attractiveness, High Winnability

The most valuable quadrant for a resource-constrained region, and the one no study recommends because nothing here photographs well. Metal fabrication, plastics and packaging, cold storage and food distribution, building products, specialty industrial services. Modest national growth rates, unglamorous board presentations — and you can actually serve them, which means you can actually land them.

Decline — Low Attractiveness, Low Winnability

Name these, briefly, in writing. An explicit list of what the organization is not pursuing is the artifact that protects staff time when a board member returns from a conference with an enthusiasm, and it is the part of the study most often omitted for being impolitic.

Section 5 — From List to Motion

A target list that does not specify behavior is a document, not a strategy. The test of a finished study is whether a business development officer can open it on Monday morning and know what to do.

Four things convert a list into a motion.

• Who you are calling. A named company universe per target — actual firms with locations, expansion histories, and supply chain relationships to your region, not a NAICS code
• Where they would go. The specific site or sites each target lands on, with the utility, acreage, and timeline figures a consultant will ask for in the first conversation
• What has to change. For every target sitting in the second quadrant, the specific deliverability gap, its estimated cost, and who owns closing it
• What would change the answer. The conditions that would move a sector between quadrants — a substation upgrade energized, a site optioned, a discharge permit expanded — reviewed on a set cadence rather than at the next study

That last item is what keeps the work alive. Winnability is not a fixed property of a region; it is a function of infrastructure decisions that are themselves in motion. A target list built on a winnability screen has an expiration date attached to each line, which is a substantial improvement over a list that is quietly wrong for four years.

Section 6 — Reading a Study You Already Paid For

Most organizations reading this are not commissioning a study. They are holding one. Three questions will tell you what you have, and none of them require reopening the engagement.

Does it name anything you cannot win?

Search the document for a sector it recommends against, or for the sentence explaining why a sector the region has been chasing was excluded. If every plausible sector survived, the study did not screen. It described.

Does every target map to a site?

Take each recommended sector and name the specific property a project in that sector would occupy, with its power position and its due diligence status. Any target that cannot be paired with real acreage is aspirational, and aspirational targets should be labeled as such so the board knows what it is funding.

Would a peer region's board recognize this as their list?

The Same List Test, applied to your own document. If the answer is yes, the study told you about the economy. What you needed was a study that told you about the economy and then told you which part of it you can serve.

Section 7 — Three Scenarios

The three scenarios below are illustrative models, not client engagements. They are constructed from the patterns and failure modes described in this paper, with figures set at realistic values for organizations of this size, to show how the framework behaves under pressure. Every number in them is hypothetical. What is not hypothetical is the shape of the problem — I have watched each of these dynamics play out, in some form, on real engagements.

Scenario 1: The List That Excluded Nothing

Profile: County EDO, five staff, $780,000 operating budget. Commissioned a $60,000 target industry study following a strategic planning process. Adopted eight target sectors.

Discovery

• Three years after adoption, the organization had not closed a project in six of the eight sectors, and the two it had closed in were the two it had been active in before the study
• Every sector with an existing constituency — the chamber, a board member's industry, a state priority — appeared on the final list; nothing had been removed between the draft and the adopted version
• Four of the eight sectors had load requirements the county's largest available service could not meet inside three years, a fact the study did not address because it was not asked to
• Business development travel and marketing spend had been allocated evenly across all eight targets, on the reasoning that all eight had been board-adopted

The organization re-scored its eight sectors on deliverability alone, using data it already held from a prior utility assessment. Three cleared. Two were reclassified as conditional with a named infrastructure gap and a cost estimate. Three were removed. Business development spend was reallocated to the three surviving targets and the two conditional ones, and the infrastructure gaps became the organization's capital request to the county.

Key Lesson: The failure was not in the analysis. It was in the absence of a screen that could remove anything, and in an allocation model that treated eight adopted targets as eight equally viable ones.

Scenario 2: The Fiction on the Cover

Profile: Regional partnership, three counties, eleven staff. Adopted hyperscale data centers as its lead target following state incentive legislation and heavy peer activity. Built a marketing campaign and a dedicated web presence around the target.

Discovery

• The region's marketed site had never been through a load study; the utility's informal estimate for firm service above twenty megawatts was five to seven years
• The 300-acre site described as available was under four ownerships with no options in place and no assemblage agreement
• Across eighteen months, the partnership had participated in nine data center RFIs and advanced past the first round in none, receiving substantive feedback on one
• A neighboring region with a weaker tax position and less acreage had landed two projects in the same period, having spent the prior three years on transmission planning with its utility

The partnership kept the target but reclassified it as conditional, with a written five-year roadmap covering the load study, the assemblage, and a transmission upgrade with a costed utility contribution. Near-term business development was redirected to two sectors the region could serve immediately. The data center campaign was paused rather than cancelled, on the reasoning that marketing a capability you do not have damages the relationship with the consultants you most need to believe you later.

Key Lesson: An unwinnable target is not a stretch goal. It is either an infrastructure program with a budget and a date, or it is a line to remove — and the difference between those two is a decision the board should be asked to make explicitly.

Scenario 3: The Quadrant Nobody Presents

Profile: City and county partnership, population 34,000. Modest industrial park with reliable but unremarkable utility service, good interstate access, no rail, and a labor shed of roughly 60,000. Four-year target list led by life sciences and advanced mobility.

Discovery

• Neither lead target had produced a site visit in four years; both had been selected on statewide growth data with no regional deliverability test
• Screened on deliverability alone, the region's strongest positions were in cold storage and food distribution, plastics and packaging, and metal fabrication — none of which appeared on the adopted list
• All three of those sectors had existing employers in the shed, proven labor availability at the relevant wage bands, and requirements the industrial park could meet without new infrastructure
• Staff had raised two of the three informally over the years; none had survived into a board presentation, on the reasoning that they were not growth sectors

The partnership restructured its list around the three deliverable sectors, built named company universes for each from the existing supply chain relationships of employers already in the shed, and retained life sciences as a long-horizon aspiration with no near-term budget attached. Over the following two years it closed three projects, none large, together representing roughly 180 jobs and $40 million in capital investment.

Key Lesson: The overlooked quadrant is where small regions win, and the reason it stays overlooked is presentational. Nobody wants to stand in front of a board and recommend plastics. The organizations willing to do it close projects.

Conclusion

The target industry study is not broken. It is scoped to answer half the question, and the half it answers is the half that does not decide outcomes.

Knowing which sectors are growing is table stakes; every region has that document and most have the same one. Knowing which of those sectors your region can physically serve — and being willing to write down the ones it cannot — is the part almost nobody does, and it is the part that determines whether a strategy produces projects or produces a binder.

The projects are real and they are being decided on deliverability. The question is not which sectors are growing. It is which of them your region can actually serve, and whether anyone has written that down.