Touch Volume Application Rates

Definition

How many marketing touches it takes to meet or surpass our standard application rate.

Summary

This analysis groups people by the total number of marketing touches they received and compares each group’s application rate to the overall application rate. The goal is to identify the point at which touch volume appears to be associated with application behavior at or above our baseline.

Rather than evaluating a single message, channel, or campaign, this view looks at cumulative exposure. It helps answer a practical planning question: how much marketing contact is typically needed before a population begins to perform at or above the standard application rate?

All Fiscal Years

Touch Bucket	People	Applicants	App Rate	Lift
0	126,843	12,916	10.2%	0.60x
1–2	15,956	606	3.8%	0.22x
3–5	7,123	470	6.6%	0.39x
6–10	7,570	425	5.6%	0.33x
11–20	11,729	644	5.5%	0.32x
21–35	10,206	1,018	10.0%	0.59x
36–50	5,995	1,248	20.8%	1.23x
51–75	5,956	2,151	36.1%	2.14x
76–100	4,062	2,208	54.4%	3.22x
101–150	5,352	3,711	69.3%	4.10x
151–250	5,962	4,928	82.7%	4.89x
250+	6,306	5,666	89.9%	5.32x

The threshold bucket — where application rate first exceeds the overall baseline of 16.9% — is 36–50 touches at 20.8% (1.23x lift).

Key Insight

The application rate crosses the overall baseline at 36–50 engagement touches. Below that threshold, even persons with 20+ touches convert well below average. Above it, rates climb steeply and consistently — reaching 89.9% at 250+ touches. A person with 36 or more recorded engagement touches is meaningfully more likely to apply than the average prospect in the funnel.

Why This Is Important

Touch volume is one of the clearest ways to evaluate whether a prospective student population has received enough marketing exposure to reasonably expect application behavior.

This metric helps us:

Understand whether low-performing populations may be under-touched
Identify the touch-volume range where application behavior begins to normalize
Compare lightly touched and heavily touched audiences
Inform campaign planning, audience prioritization, and communication strategy
Avoid assuming that a single touch or small number of touches is enough to influence behavior

This analysis should not be interpreted as proof that touches alone caused applications. Higher-touch audiences may differ from lower-touch audiences in important ways, including inquiry source, intent level, geography, academic interest, or stage in the funnel. However, the metric is useful for understanding the relationship between cumulative marketing activity and application outcomes.

Methodology

The query first counts the total number of marketing touches associated with each person. It then identifies whether each person has an applied status tag.

Each person is assigned to a touch-volume bucket based on their total number of touches:

0
1-2
3-5
6-10
11-20
21-35
36-50
51-75
76-100
101-150
151-250
250+

For each bucket, the query calculates:

The number of people in the bucket
The number of applicants in the bucket
The application rate for the bucket
The overall application rate across all people
The lift compared to the overall application rate

The vs_overall_lift value shows how each bucket performs relative to the overall baseline. A value of 1.00 means the bucket is performing exactly at the overall application rate. A value above 1.00 means the bucket is outperforming the overall average, while a value below 1.00 means it is underperforming.

The key threshold is the first touch bucket where application_rate is greater than or equal to overall_application_rate, or where vs_overall_lift is at least 1.00.

Query

WITH touch_counts AS (
  SELECT
    person_id,
    COUNT(*) AS total_touches,
    COUNT(CASE WHEN touch_category = 'engagement' THEN 1 END) AS engagement_touches,
    COUNT(CASE WHEN touch_category = 'exposure' THEN 1 END) AS exposure_touches,
    COUNT(CASE WHEN touch_type = 'ping' THEN 1 END) AS pageview_count,
    COUNT(CASE WHEN touch_type = 'form_response' THEN 1 END) AS form_response_count
  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
    p.id AS person_id,

    COALESCE(tc.total_touches, 0) AS total_touches,
    COALESCE(tc.engagement_touches, 0) AS engagement_touches,
    COALESCE(tc.exposure_touches, 0) AS exposure_touches,
    COALESCE(tc.pageview_count, 0) AS pageview_count,
    COALESCE(tc.form_response_count, 0) AS form_response_count,

    CASE
      WHEN a.person_id IS NOT NULL THEN 1
      ELSE 0
    END AS applied

  FROM persons p
  LEFT JOIN touch_counts tc
    ON p.id = tc.person_id
  LEFT JOIN applied a
    ON p.id = a.person_id
),

bucketed AS (
  SELECT
    person_id,
    applied,
    total_touches,
    engagement_touches,
    exposure_touches,
    pageview_count,
    form_response_count,

    CASE
      WHEN engagement_touches = 0 THEN '0'
      WHEN engagement_touches BETWEEN 1 AND 2 THEN '1-2'
      WHEN engagement_touches BETWEEN 3 AND 5 THEN '3-5'
      WHEN engagement_touches BETWEEN 6 AND 10 THEN '6-10'
      WHEN engagement_touches BETWEEN 11 AND 20 THEN '11-20'
      WHEN engagement_touches BETWEEN 21 AND 35 THEN '21-35'
      WHEN engagement_touches BETWEEN 36 AND 50 THEN '36-50'
      WHEN engagement_touches BETWEEN 51 AND 75 THEN '51-75'
      WHEN engagement_touches BETWEEN 76 AND 100 THEN '76-100'
      WHEN engagement_touches BETWEEN 101 AND 150 THEN '101-150'
      WHEN engagement_touches BETWEEN 151 AND 250 THEN '151-250'
      ELSE '250+'
    END AS touch_bucket

  FROM labeled
),

overall AS (
  SELECT
    ROUND(
      SUM(applied) * 1.0 / COUNT(*),
      3
    ) AS overall_application_rate
  FROM labeled
)

SELECT
  b.touch_bucket,

  COUNT(*) AS people,
  SUM(b.applied) AS applicants,

  ROUND(
    SUM(b.applied) * 1.0 / COUNT(*),
    3
  ) AS application_rate,

  o.overall_application_rate,

  ROUND(
    (
      SUM(b.applied) * 1.0 / COUNT(*)
    )
    /
    NULLIF(o.overall_application_rate, 0),
    2
  ) AS application_rate_lift,

  ROUND(AVG(b.total_touches), 1) AS avg_total_touches,
  ROUND(AVG(b.engagement_touches), 1) AS avg_engagement_touches,
  ROUND(AVG(b.exposure_touches), 1) AS avg_exposure_touches,
  ROUND(AVG(b.pageview_count), 1) AS avg_pageviews,
  ROUND(AVG(b.form_response_count), 1) AS avg_form_responses

FROM bucketed b
CROSS JOIN overall o

GROUP BY
  b.touch_bucket,
  o.overall_application_rate

ORDER BY
  CASE b.touch_bucket
    WHEN '0' THEN 1
    WHEN '1-2' THEN 2
    WHEN '3-5' THEN 3
    WHEN '6-10' THEN 4
    WHEN '11-20' THEN 5
    WHEN '21-35' THEN 6
    WHEN '36-50' THEN 7
    WHEN '51-75' THEN 8
    WHEN '76-100' THEN 9
    WHEN '101-150' THEN 10
    WHEN '151-250' THEN 11
    ELSE 12
  END;