Calculate the Recency Score:
Use the values from the columns "[Column with last interaction date]" (ISO date or yyyy-mm-dd) and the current date at runtime to calculate Recency (in days) using the following formula:
Recency (days) = Current Date − Last Interaction Date
Output requirements:
Compute per-participant recency (days).
Report Average Recency (days), Median Recency (days), and % of users active in the last 7 days.
Provide a table of bucketed counts: 0–3d, 4–7d, 8–14d, 15–30d, >30d.
If multiple audiences exist (optional column "[Segment/Audience]"), compute the same stats per segment.
After calculating, report where the Average Recency lands on the following scale (lower is better). Replace the bracketed thresholds with the targets you use:
Very Good = ≤ [X] days
Good = [X+1–Y] days
Average = [Y+1–Z] days
Poor = [Z+1–W] days
Very Poor = > [W] days
Example defaults you can use if no thresholds are provided:
Very Good ≤ 3d, Good 4–7d, Average 8–14d, Poor 15–30d, Very Poor > 30d.
Next, analyze the responses in the column "[Column with qualitative explanations]" in the CSV (e.g., user feedback on why they return or lapse). Identify common themes and provide clear, actionable suggestions to improve recency (e.g., value reminders, content cadence, notifications, onboarding nudges).
Recency — Example Report
Summary
Average Recency: 9.2 days → Average
Median Recency: 6 days
% Active in last 7 days: 44%
Calculation
Recency (per user)
=
Current Date
−
Last Interaction Date
Recency (per user)=Current Date−Last Interaction Date
Example: For today (Aug 29, 2025), if a user last interacted on Aug 20, Recency = 9 days.
Distribution of Users by Recency
Bucket Users % of Total
0–3 days 220 22%
4–7 days 220 22%
8–14 days 300 30%
15–30 days 180 18%
>30 days 80 8%
Interpretation: Most users (52%) are within the last 7 days, but nearly 26% are inactive for over two weeks.
Segment Breakdown
By audience type
New sign-ups (last 90 days): Avg Recency = 5.5 days (Good)
Returning customers: Avg Recency = 11.8 days (Average)
Dormant users (>6 months registered): Avg Recency = 19.3 days (Poor)
Qualitative Insights (from explanations column)
Top reasons participants gave for not returning sooner:
Lack of new content / updates — 32%
“Nothing fresh since my last visit.”
Notifications not engaging — 25%
“I got emails but ignored them—they didn’t feel relevant.”
Friction at login / access — 20%
“I forgot my password and didn’t bother resetting.”
Competing tools / apps — 15%
“I already use another app daily for this.”
Low perceived value — 8%
“Didn’t see enough reason to come back often.”
Recommendations
Increase content cadence: publish weekly updates/features to reduce lapses >14 days.
Personalize re-engagement: tailor notifications by last activity type (e.g., “finish your saved draft” vs. generic emails).
Streamline login flows: add social logins and keep users signed in across sessions.
Introduce streaks / habit loops: reward users for consecutive logins (gamification, discounts, points).
Survey dormant users: identify specific barriers to returning after >30 days.
Next Checks
Monitor % of users in 0–7 day bucket weekly; target = ≥60% (move into Good).
Track re-engagement campaign performance (click-to-open and subsequent login rates).
Run cohort analysis comparing new vs. returning users to isolate retention drivers.
Target: Reduce Average Recency from 9.2 days → ≤7 days (Good).