Channel Concentration Risk

Definition

The percentage of total applicants and touched persons whose first engagement came from each source group, with attention to how dependent the applicant pipeline is on any single channel. High concentration in a paid vendor means application volume is exposed to contract changes, budget cuts, or vendor performance shifts.

Summary

FY2023 — 15,034 touched persons

Source Group	People	Applicants	% of Touched	% of Applicants
direct / unknown	9,246	2,094	61.5%	54.7%
organic search	4,157	1,609	27.7%	42.0%
paid / carnegie	1,061	33	7.1%	0.9%
email	276	53	1.8%	1.4%
organic social	116	24	0.8%	0.6%
other	108	10	0.7%	0.3%
paid display	51	4	0.3%	0.1%
vendor / purchased list	10	0	0.1%	0.0%
paid search	9	0	0.1%	0.0%

FY2024 — 22,264 touched persons

Source Group	People	Applicants	% of Touched	% of Applicants
direct / unknown	12,696	2,902	57.0%	53.5%
organic search	7,864	2,276	35.3%	42.0%
organic social	473	65	2.1%	1.2%
paid / carnegie	401	52	1.8%	1.0%
email	305	70	1.4%	1.3%
paid display	205	24	0.9%	0.4%
paid social	152	1	0.7%	0.0%
paid search	101	23	0.5%	0.4%
other	54	6	0.2%	0.1%
vendor / purchased list	13	2	0.1%	0.0%

FY2025 — 22,543 touched persons

Source Group	People	Applicants	% of Touched	% of Applicants
direct / unknown	10,843	2,080	48.1%	44.6%
organic search	9,252	2,312	41.0%	49.6%
paid display	543	84	2.4%	1.8%
paid / carnegie	510	42	2.3%	0.9%
paid social	411	7	1.8%	0.2%
email	395	60	1.8%	1.3%
organic social	329	41	1.5%	0.9%
paid search	189	24	0.8%	0.5%
other	36	7	0.2%	0.2%
ai referral	25	4	0.1%	0.1%
vendor / purchased list	10	2	0.0%	0.0%

The channel mix is diversifying over time. Direct/unknown shrank from 61.5% to 48.1% of touched persons as organic search grew from 27.7% to 41.0%. In FY2025, organic search overtook direct/unknown as the largest single source of applicants (49.6% vs. 44.6%).

All paid channels combined represent less than 5.5% of touched persons in any year, and less than 3.5% of applicants. No single paid channel exceeds 2.4% of touched persons.

Key Insight

This funnel is not exposed to paid channel concentration risk. Paid vendors are individually too small to materially move application volume up or down — which also means budget cuts to any single vendor would not crater the pipeline.

The real concentration is in brand channels — organic search and direct/unknown together account for 90%+ of applicants in every year. This is structurally healthy: the institution is not dependent on any external vendor for pipeline. The shift underway (direct/unknown declining, organic search growing) is the right direction — it replaces weakly-attributed volume with clearly self-directed demand.

Note

AI referral appeared as a distinct source group in FY2025 with 25 touched persons and 4 applicants. It is small today but worth tracking — AI-driven search (ChatGPT, Perplexity, Gemini) is becoming a real referral channel in higher ed and may grow materially in coming cycles.

Why This Is Important

A diversified channel mix is more resilient than one dependent on a single vendor or tactic. If a large share of applicants traces back to one paid channel, a budget cut, contract change, or platform policy shift can materially impact enrollment pipeline with little warning.

This metric is particularly relevant for board-level risk conversations because it quantifies exposure. It also identifies over-reliance on channels that convert below baseline — paying for volume that doesn’t convert is a different risk than paying for volume that does.

Methodology

Source group classification uses the same CASE logic as the first-touch application rate page. Concentration is measured two ways: share of all touched persons (funnel dependency) and share of applicants only (conversion dependency). The Herfindahl-Hirschman Index (HHI) is included as a single summary concentration score — higher values mean greater concentration.

Note

HHI is calculated on touched persons only. A score below 1,500 is generally considered diversified; above 2,500 is highly concentrated.

Query

WITH first_touch AS (
  SELECT
    person_id,
    utm_source,
    utm_medium,
    ROW_NUMBER() OVER (
      PARTITION BY person_id
      ORDER BY touched_at ASC
    ) AS rn
  FROM person_touches
  WHERE touch_category = 'engagement'
),

source_groups AS (
  SELECT
    person_id,
    CASE
      WHEN lower(utm_source) IN ('google', 'bing', 'yahoo', 'yahoo!', 'duckduckgo', 'ecosia', 'yandex', '360.cn', 'baidu')
        AND lower(utm_medium) IN ('organic', 'organic_search', 'search')
        THEN 'organic search'
      WHEN lower(utm_source) IN ('google', 'bing')
        AND lower(utm_medium) IN ('cpc', 'paid', 'paid_search', 'ppc')
        THEN 'paid search'
      WHEN lower(utm_source) IN ('googlead', 'mediately')
        OR lower(utm_medium) IN ('mediately', 'display', 'lightbox', 'banner')
        THEN 'paid display'
      WHEN lower(utm_source) IN ('carnegie-digital', 'carnegie', 'carnegie_digital')
        AND lower(utm_medium) IN ('ppc', 'paid-social', 'paid_social', 'cpc', 'display', 'lightbox', 'discovery', 'custom-content', 'video')
        THEN 'paid / carnegie'
      WHEN lower(utm_source) IN ('facebook', 'instagram', 'linkedin', 'youtube', 'tiktok', 'snapchat', 'twitter', 'reddit', 'meta')
        AND lower(utm_medium) IN ('paid', 'paid-social', 'paid_social', 'cpc')
        THEN 'paid social'
      WHEN lower(utm_source) IN ('facebook', 'instagram', 'linkedin', 'youtube', 'tiktok', 'snapchat', 'twitter', 'reddit')
        OR lower(utm_medium) IN ('social')
        THEN 'organic social'
      WHEN lower(utm_source) IN ('slate', 'gmail', 'miami-matters', 'mass_mailer', 'inbound-email', 'notifier', 'email', 'newsletter')
        OR lower(utm_medium) IN ('email')
        THEN 'email'
      WHEN lower(utm_source) IN ('nrccua', 'encoura', 'collegeboard', 'cappex', 'niche', 'niche.com', 'qs', 'usnews_studentconnect', 'hotcoursesinternational', 'idp', 'ziprecruiter')
        THEN 'vendor / purchased list'
      WHEN lower(utm_source) IN ('chatgpt.com', 'perplexity', 'claude', 'gemini', 'copilot.com')
        THEN 'ai referral'
      WHEN utm_source IS NULL OR utm_source = '' OR utm_source = '(none)'
        THEN 'direct / unknown'
      ELSE 'other'
    END AS source_group
  FROM first_touch
  WHERE rn = 1
),

cohort AS (
  SELECT
    pd.person_id,
    pd.fiscal_year,
    pd.applied,
    sg.source_group
  FROM persons_dashboard pd
  JOIN source_groups sg ON pd.person_id = sg.person_id
  WHERE pd.fiscal_year IN ('2023', '2024', '2025')
),

totals AS (
  SELECT fiscal_year, COUNT(*) AS touched_persons
  FROM cohort
  GROUP BY fiscal_year
),

by_group AS (
  SELECT
    c.fiscal_year,
    c.source_group,
    COUNT(*) AS people,
    SUM(CASE WHEN applied = 'Yes' THEN 1 ELSE 0 END) AS applicants,
    t.touched_persons
  FROM cohort c
  JOIN totals t ON c.fiscal_year = t.fiscal_year
  GROUP BY c.fiscal_year, c.source_group, t.touched_persons
)

SELECT
  fiscal_year,
  source_group,
  people,
  applicants,
  ROUND(people * 100.0 / touched_persons, 1) AS pct_of_touched,
  ROUND(applicants * 100.0 / NULLIF(SUM(applicants) OVER (PARTITION BY fiscal_year), 0), 1) AS pct_of_applicants
FROM by_group
ORDER BY fiscal_year, people DESC;