Marketing intelligence

Where should Medicare marketers invest — and what should shape the strategy?

Market opportunity, D-SNP audience landscape, competitive positioning, policy guardrails, campaign planning, and evidence-backed marketing strategy.

This is not Sales intelligence, and it is not trying to be. Sales intelligence answers where the winnable members are: AEP targeting, market headroom, battlecards, field deployment. Marketing intelligence answers the question one step earlier and one level up: where the investment should go, what the audience landscape looks like, what competitors are structurally doing, what policy allows you to say, and what the campaign has to assume to pay back. The intended path is market → marketing strategy → sales execution.
3,199
Counties scored for marketing opportunity
April 2026 CMS county file
5,767,705
D-SNP market-sizing gap ⓘThis is a directional market-sizing proxy. It does not represent a verified count of beneficiaries currently eligible and available to enroll in a D-SNP. It subtracts August 2026 reported D-SNP enrollment from the April 2026 dual population, two different CMS products.
August 2026 SNP report
51.8%
National D-SNP market-sizing proxy
Same basis as /market/snp/
12
CMS actions with a marketing implication
Mirrors /policy/whats-changed/

Unless a figure names its own vintage, it comes from the April 2026 CMS county file and the August 2026 CMS SNP Comprehensive Report. Derived scores are labeled modeled.

Where should I look first?

Five questions, in the order a marketing plan is usually built. Start with the first; each question opens the evidence behind it. Modeled figures are labelled modeled and are prioritization aids, not predictions.

Who and what is thereAudience, competition, carrier position, D-SNP landscape and market structure.
How to competeProduct positioning, benefits, competitive differentiation, and the policy context that limits what can be said.
Can it pay backCampaign economics from your own assumptions. Observed data, your assumptions and modeled outputs are labelled apart.

beta What would have to be true for this campaign to pay back?

0invented inputs: every assumption in the model is one you enter
Modeled from user assumptions · no published response rates exist
Model the campaign →

live observed CMS data · assumption your inputs · modeled outputs computed from both. No response or conversion rate is supplied by MedicareInsights.

What to investigate nextAn evidence-backed strategy brief: what the data shows, what it does not, and what to check before acting.

So what / Now what Interpretation

The market picture a marketer needs is mostly already published: county size, penetration, twelve-month momentum, dual density, D-SNP market-sizing proxy, carrier structure and integration mix are all in named CMS files. What is not published is benefit-level comparison, plan exits by county and contract-level Stars, and those are precisely the inputs that would turn an investment ranking into a positioning argument. Everything on this hub is built from the first list and explicit about the second.

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.