Which markets deserve the next marketing dollar?

Enrollment April 2026 CMS county file · score v0.1 generated 2026-08-26modeled

A 0–100 Marketing Opportunity Score for all 3,199 US counties, in two modes. MA mode ranks general Medicare Advantage marketing opportunity; D-SNP mode re-weights the same market toward the dual-eligible opportunity. Every component, every weight and every input that is not yet computable is on this page. The score is arithmetic over published CMS counts, not a prediction and not a model output in the machine-learning sense.

3,199
Counties scored
42
In the HIGH band, MA mode
Score 75-100
5/9
MA components computable
Applied weight 0.60 of 1.00
6/6
D-SNP components populated
3 county-grain · 3 state-grain applied to counties

D-SNP county rankings partially inherit state-level capture, concentration and integration values. Use them to prioritize markets for review, not as county-level estimates of those three measures.

Enrollment, penetration, momentum and dual counts are the April 2026 CMS county file. D-SNP capture proxy, competitive openness and integration readiness are state-grain figures from the CMS SNP Comprehensive Report, applied to the counties in that state and labeled as such.

What this score is for, and what it is not for. Four of the nine published MA components are not computable yet, and all four describe competitive difficulty. The score therefore ranks market attractiveness for marketing investment. It does not rank winnability, it does not know what your product costs, and it has no view on your network. Treat a high score as a reason to look, not a reason to buy media.

Rank the markets

#CountyScoreBandMedicareMA & other health plan share12-mo ptsDualsDual density
1Merced County, CA90HIGH43,16829.5%+1.714,72034.1%
2Tulare County, CA87HIGH70,87037.3%+1.426,13136.9%
3Imperial County, CA85HIGH37,50638.0%+1.317,49246.6%
4Monterey County, CA84HIGH74,74816.9%+0.616,72822.4%
5Kings County, CA81HIGH19,71837.8%+1.46,40932.5%
6Suffolk County, NY80HIGH334,55630.1%+0.754,82016.4%
7Grant County, WA80HIGH18,19437.3%+2.33,55419.5%
8Allegany County, MD80HIGH17,39118.8%+1.13,29418.9%
9Okanogan County, WA80HIGH12,19229.6%+2.22,30618.9%
10Nassau County, NY79HIGH303,09733.4%+0.754,39817.9%
11District Of Columbia, DC79HIGH97,55434.5%+0.634,31035.2%
12Orange County, NY79HIGH72,78838.2%+1.013,79218.9%
13Benton County, WA79HIGH41,63932.4%+1.45,80813.9%
14Sullivan County, NY79HIGH18,35038.5%+1.44,88026.6%
15Marion County, IL79HIGH10,00031.9%+2.22,33723.4%
16San Francisco County, CA78HIGH159,38951.3%+1.355,64834.9%
17Caddo Parish, LA78HIGH52,70655.3%+4.413,69126.0%
18Wicomico County, MD78HIGH22,12727.4%+1.13,69816.7%
19Mckinley County, NM78HIGH12,55833.7%+3.12,12616.9%
20Westchester County, NY77HIGH198,26839.1%+0.737,92119.1%
21Rockland County, NY77HIGH60,25832.6%+0.512,94421.5%
22Lafayette Parish, LA77HIGH47,85141.2%+1.09,10719.0%
23Yuma County, AZ77HIGH46,25952.4%+1.415,09032.6%
24Calcasieu Parish, LA77HIGH40,21245.6%+1.57,84519.5%
25Franklin County, WA77HIGH12,73337.3%+2.12,48719.5%

Top 25 in MA mode, rendered without JavaScript. With JavaScript the table becomes the full 3,199-county explorer with filters, mode switching, a scatter view and export.

How the score is built

Each available component is converted to a percentile rank against every scored county, ties sharing the mean rank. Percentiles are combined with published weights, and the weights are renormalized over the components that are actually available rather than treating a missing component as a zero. Percentile ranking rather than min-max scaling is deliberate: county MA enrollment growth spans roughly −89% to +132%, and min-max would let a handful of extreme counties compress everything else into a narrow band.

How this score is calculated

Percentile rank of every available component against all scored counties, combined with published weights renormalized over the available components. Deterministic arithmetic over published CMS counts. Not a prediction, not AI.

Version 0.1, generated 2026-08-26 by tools/build_marketing_data.py. Status MODELED. Bands: HIGH 75-100, MODERATE 50-74, LOW 25-49, MINIMAL 0-24. Counties below 2,000 Medicare beneficiaries are flagged low reliability, because a percentile rank there moves sharply on a small absolute change. They are flagged, not hidden.

ComponentWeightDirectionGrainStatus
Addressable Medicare population
Total Medicare beneficiaries in the county. Reach is bounded by this number before any creative decision.
0.20higher is betterCountyavailable
Health-plan share headroom
Remaining share relative to the CMS MA-and-other-health-plan measure. This is a directional headroom indicator, not a count of MA-eligible beneficiaries available to convert.
0.15higher is betterCountyavailable
MA & other health plan share momentum
Change in MA & other health plan share over twelve months, in percentage points. A market already moving costs less to move further.
0.09higher is betterCountyavailable
MA & other health plan enrollment growth
Year over year change in MA & other health plan enrollment in the county.
0.06higher is betterCountyavailable
Dual / D-SNP opportunity
Dual-eligible density. Duals hold year-round enrollment rights, so they change the shape of a media plan, not only its size.
0.10higher is betterCountyavailable
Competitive openness
How concentrated the carrier field is. Needs the CMS CPSC contract/plan/county enrollment file, not yet ingested.
0.15lower is betterCountynot yet
Market disruption
Plan exits and non-renewals displacing members in this county. Needs the CMS service-area and plan-year crosswalk files, not yet ingested.
0.10higher is betterCountynot yet
Product differentiation room
How much benefit design space is unclaimed locally. Needs the MA Landscape and Plan Benefit Package files, not yet ingested.
0.10higher is betterCountynot yet
Quality / Stars context
Star ratings of contracts serving the county. Needs the contract-level Star Ratings files, not yet ingested.
0.05lower is betterCountynot yet

Any change to a weight, a component, the normalization method or a band threshold increments the version and requires a changelog entry. A score that changes without a version change is a bug.

D-SNP mode components

ComponentWeightDirectionGrainStatus
Dual-eligible population
Dual-eligible beneficiaries in the county, used as a market-size input. Actual D-SNP eligibility, plan availability and enrollment opportunity vary by state, eligibility category and plan design.
0.35higher is betterCountyavailable
Full-dual share
Share of duals with full Medicaid benefits. Full duals are the core D-SNP eligibility group.
0.10higher is betterCountyavailable
D-SNP capture-proxy headroom
Dual population minus reported D-SNP enrollment, as a share. Cross-vintage proxy: August 2026 SNP enrollment over April 2026 dual population. State grain: CMS publishes SNP enrollment by state, not by county.
0.25higher is betterState, applied to countyavailable
D-SNP competitive openness
Concentration of D-SNP enrollment across parent organizations in the state (HHI over single-state plans). State grain.
0.15lower is betterState, applied to countyavailable
Integration readiness
FIDE and HIDE share of state D-SNP enrollment. High integration markets reward a different message than coordination-only markets. State grain.
0.10higher is betterState, applied to countyavailable
MA & other health plan share momentum
Change in MA & other health plan share over twelve months, in percentage points.
0.05higher is betterCountyavailable

D-SNP mode is fully computable today because CMS publishes SNP enrollment, integration status and plan detail. Three of its six components are state-grain, which is stated on the component and is the honest limit of the D-SNP view: applying a state capture rate to a county assumes the county behaves like its state.

What is missing from MA mode, and what it waits on

Component not yet computablePublished weightWhat it waits on
Competitive openness0.15How concentrated the carrier field is. Needs the CMS CPSC contract/plan/county enrollment file, not yet ingested.
Market disruption0.10Plan exits and non-renewals displacing members in this county. Needs the CMS service-area and plan-year crosswalk files, not yet ingested.
Product differentiation room0.10How much benefit design space is unclaimed locally. Needs the MA Landscape and Plan Benefit Package files, not yet ingested.
Quality / Stars context0.05Star ratings of contracts serving the county. Needs the contract-level Star Ratings files, not yet ingested.

Weights are renormalized over the available components, so the score is a weighted average of what exists rather than a score with silent zeros. Coverage: 5/9 components, applied weight 0.60 of 1.00.

So what / Now what Interpretation

On the current inputs the highest-scoring large market is Merced County, CA at 90, with 43,168 Medicare beneficiaries, 70.5% of them not yet in a Medicare health plan and +1.7 points of twelve-month movement. That combination, large and still converting, is the profile the MA weights are built to surface. It is also exactly the profile most likely to be contested, and the score cannot see that yet.

Limitations you should carry into the meeting

What should I do next?

Sources and vintage

DatasetSourceVintageStatus
MA and other health plan enrollment and share by countyCMS Medicare Monthly Enrollment (BENE_GEO_LVL=County)April 2026live
Dual-eligible counts by countyCMS Medicare Monthly Enrollment (DUAL_TOT_BENES / FULL_DUAL_TOT_BENES)April 2026live
SNP enrollment, integration and plansCMS SNP Comprehensive Report (SNP_REPORT_PART_17)August 2026live
Marketing Opportunity ScoreDerived, tools/build_marketing_data.py v0.12026-08-26modeled

Full methodology: Marketing Opportunity Score · site methodology.