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Tracking Comms Cadence Across Manual and Automated Messages

sydneymack_

I'm looking to get a sense of the average comms cadence our members typically receive. It’s a bit tricky to assess since our messaging is highly targeted and includes a mix of automated and manual communications. Just wanted to see if anyone else has successfully tackled this—I’d love to hear what’s worked for you!

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Comments

DeepanshuP18

Hey @sydneymack_ I think you can probably use Tags in Braze dashboard.
You can create a parent tag with the name of Campaign_Cadence and then nest the child tags like this :
Campaign_Cadence/Automated(Daily, weekly, monthly) or Campaign_Cadence/Manual (one-off)

and then you can see the numbers populate against each category in the Tags section in the braze dashboard and maybe use it for analytics or further segmentation purposes.

Hope this works

sydneymack_

Thanks for your suggestion, Deepanshu!

I was able to figure out an alternative way using Braze Queries as well! The query below analyzes message volume per user for Email, Push, and In-App over the past 30 days. It groups users into buckets based on their message counts relative to percentile thresholds — median (50th), 75th, and 90th percentiles. For each bucket, it summarizes the number of users, as well as the average, minimum, and maximum messages received, and provides a sample of 5 users per bucket per channel. In case this might be helpful for you as well:

WITH email AS (
SELECT
EXTERNAL_USER_ID,
COUNT(DISTINCT DISPATCH_ID) AS message_count,
'Email' AS channel
FROM USERS_MESSAGES_EMAIL_SEND_SHARED
WHERE TIME > (DATE_PART(EPOCH_SECOND, CURRENT_TIMESTAMP()) - 2592000)
AND EXTERNAL_USER_ID IN ()
GROUP BY EXTERNAL_USER_ID
),
inapp AS (
SELECT
EXTERNAL_USER_ID,
COUNT(*) AS message_count,
'In-App Message' AS channel
FROM USERS_MESSAGES_INAPPMESSAGE_IMPRESSION_SHARED
WHERE TIME > (DATE_PART(EPOCH_SECOND, CURRENT_TIMESTAMP()) - 2592000)
AND EXTERNAL_USER_ID IN ()
GROUP BY EXTERNAL_USER_ID
),
push AS (
SELECT
EXTERNAL_USER_ID,
COUNT(DISTINCT DISPATCH_ID) AS message_count,
'Push Notification' AS channel
FROM USERS_MESSAGES_PUSHNOTIFICATION_SEND_SHARED
WHERE TIME > (DATE_PART(EPOCH_SECOND, CURRENT_TIMESTAMP()) - 2592000)
AND EXTERNAL_USER_ID IN ()
GROUP BY EXTERNAL_USER_ID
),
all_messages AS (
SELECT * FROM email
UNION ALL
SELECT * FROM inapp
UNION ALL
SELECT * FROM push
),
stats AS (
SELECT
channel,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY message_count) AS median,
PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY message_count) AS p75,
PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY message_count) AS p90
FROM all_messages
GROUP BY channel
),
buckets AS (
SELECT
m.channel,
m.EXTERNAL_USER_ID,
m.message_count,
s.median,
s.p75,
s.p90,
CASE
WHEN m.message_count <= s.median THEN 'Up to median'
WHEN m.message_count <= s.p75 THEN 'Above median up to 75th percentile'
WHEN m.message_count <= s.p90 THEN 'Above 75th percentile up to 90th percentile'
ELSE 'Above 90th percentile'
END AS bucket
FROM all_messages m
JOIN stats s ON m.channel = s.channel
),
bucket_counts AS (
SELECT
channel,
bucket,
COUNT(*) AS user_count,
AVG(message_count) AS avg_messages,
MIN(message_count) AS min_messages,
MAX(message_count) AS max_messages
FROM buckets
GROUP BY channel, bucket
)
-- Final output: bucket stats + sample users per bucket
SELECT
b.channel,
b.bucket,
bc.user_count,
bc.avg_messages,
bc.min_messages,
bc.max_messages,
b.EXTERNAL_USER_ID,
b.message_count
FROM buckets b
JOIN bucket_counts bc
ON b.channel = bc.channel AND b.bucket = bc.bucket
QUALIFY ROW_NUMBER() OVER (PARTITION BY b.channel, b.bucket ORDER BY b.EXTERNAL_USER_ID) <= 5
ORDER BY b.channel, b.bucket, b.EXTERNAL_USER_ID;

AllanHeoFNM

@sydneymack_ This is a SUPER COOL query solution! Thank you so much for sharing!