Engaged Pipeline Health

Definition

The application rate, cohort size, and year-over-year trend among persons who reached the conversion threshold of 36 or more engagement touches. This is the engaged pipeline — prospects who received enough marketing contact to be considered genuinely nurtured — isolated from the list imports and low-touch volume that dilute overall funnel metrics.

Summary

Fiscal Year	Engaged Persons (36+ touches)	Engaged Applicants	Engaged App Rate	Overall App Rate	Lift vs. Overall
FY2023	5,437	3,482	64.0%	9.7%	6.60x
FY2024	9,124	4,944	54.2%	14.6%	3.71x
FY2025	8,608	4,290	49.8%	8.6%	5.79x
FY2026*	4,387	2,422	55.2%	6.8%	8.12x

*FY2026 is a near-complete cycle as of reporting date.

The engaged application rate declined from FY2023 (64.0%) through FY2025 (49.8%), then rebounded to 55.2% in FY2026 — the first year-over-year improvement in the series. This happened while the overall application rate continued to fall (8.6% → 6.8%), producing the highest lift in the dataset at 8.12x. The overall rate decline in FY2026 reflects the suppression effect of the application fee introduced that year on lower-intent prospects; the engaged rate recovery suggests the fee is doing its job — removing noise without touching the core engaged pipeline.

In FY2025, the 36+ touch segment represented roughly 15% of total persons in the funnel but accounted for 89% of all applicants (4,290 of 4,813).

Key Insight

The engaged pipeline is recovering. After two years of declining application rates among the most nurtured prospects, FY2026 shows the engaged segment converting at 55.2% — above FY2025 and approaching the FY2024 peak. The lift vs. overall (8.12x) is the highest recorded, meaning the gap between engaged and non-engaged prospects has never been wider.

The application fee introduced in FY2026 suppressed the overall rate, but it is not suppressing the engaged segment. Persons who reached 36+ touches converted at a higher rate in FY2026 than FY2025 despite a more restrictive application environment. This is the clearest evidence in the dataset that deep engagement is a durable conversion driver — not just a correlation with high-intent prospects who would have applied anyway.

Why This Is Important

Overall application rates are sensitive to funnel composition — a large name buy inflates the denominator and suppresses the rate without reflecting any change in how well the institution converts engaged prospects. The engaged pipeline health metric strips that noise out.

The 36-touch threshold comes from the touch volume application rate analysis, where 36–50 touches is the first bucket that exceeds the overall baseline. Persons above this threshold are demonstrably more likely to apply than the average prospect.

For board presentations, this metric answers: is our marketing actually working for the prospects we’re reaching? A healthy engaged pipeline rate, even in a year where the overall rate declined, is evidence that the decline is a list quality problem — not a brand or marketing effectiveness problem.

Methodology

Persons are filtered to those with 36 or more engagement touches in person_touches. Application rate, cohort size, and applicant volume are calculated for each fiscal year. The overall funnel application rate is shown alongside for comparison.

Query

WITH touch_counts AS (
  SELECT
    person_id,
    COUNT(CASE WHEN touch_category = 'engagement' THEN 1 END) AS engagement_touches
  FROM person_touches
  GROUP BY person_id
),

applied AS (
  SELECT DISTINCT person_id
  FROM person_tags
  WHERE tag_type = 'applied'
    AND tag_value = 'Yes'
),

labeled AS (
  SELECT
    pd.person_id,
    pd.fiscal_year,
    COALESCE(tc.engagement_touches, 0) AS engagement_touches,
    CASE WHEN a.person_id IS NOT NULL THEN 1 ELSE 0 END AS applied
  FROM persons_dashboard pd
  LEFT JOIN touch_counts tc ON pd.person_id = tc.person_id
  LEFT JOIN applied a ON pd.person_id = a.person_id
  WHERE pd.fiscal_year IN ('2023', '2024', '2025', '2026')
),

overall_rates AS (
  SELECT
    fiscal_year,
    ROUND(SUM(applied) * 1.0 / COUNT(*), 3) AS overall_application_rate
  FROM labeled
  GROUP BY fiscal_year
),

engaged AS (
  SELECT
    fiscal_year,
    COUNT(*) AS engaged_persons,
    SUM(applied) AS engaged_applicants,
    ROUND(SUM(applied) * 1.0 / COUNT(*), 3) AS engaged_application_rate
  FROM labeled
  WHERE engagement_touches >= 36
  GROUP BY fiscal_year
)

SELECT
  e.fiscal_year,
  e.engaged_persons,
  e.engaged_applicants,
  e.engaged_application_rate,
  o.overall_application_rate,
  ROUND(e.engaged_application_rate / NULLIF(o.overall_application_rate, 0), 2) AS lift_vs_overall
FROM engaged e
JOIN overall_rates o ON e.fiscal_year = o.fiscal_year
ORDER BY e.fiscal_year;