Calculate the Visit Frequency:
Use the values from the columns "[Column with total number of visits]" and "[Column with number of unique users]" in the attached CSV to calculate Visit Frequency using the following formula:
Visit Frequency = (Total Number of Visits ÷ Number of Unique Users)
Output requirements:
Report the average visits per user.
Provide a distribution table showing % of users who visited: 1x, 2–3x, 4–5x, 6–10x, >10x in the reporting period.
If a "[Segment/Device/Channel]" column exists, repeat the stats by segment.
Report min, median, and max visits per user for additional context.
After calculating, report where the Average Visit Frequency lands on the following scale (higher is better). Replace the thresholds with the ones you use internally, or use these defaults:
Very Good = ≥ 4 visits per week
Good = 3 visits per week
Average = 2 visits per week
Poor = 1 visit per week
Very Poor = < 1 visit per week
Next, analyze the responses in the column "[Column with qualitative explanations]" in the CSV (e.g., open-text reasons for visiting or not visiting often). Identify common themes and provide clear, actionable suggestions for how Visit Frequency could be improved (e.g., reminders, new feature alerts, personalized content, loyalty programs).
Visit Frequency — Example Report
Summary
Average Visit Frequency: 2.0 visits/user/week → Average
Total Visits (period): 8,400
Unique Users (period): 4,200
Min / Median / Max (visits per user): 1 / 2 / 18
Calculation
Visit Frequency
=
8,400
4,200
=
2.0
visits per user
Visit Frequency=
4,200
8,400
=2.0 visits per user
Scale placement: 2.0 visits/user falls in Average (2 per week).
Distribution of Users (visits per user, this period)
Bucket Users % of Users
1× 1,470 35%
2–3× 1,680 40%
4–5× 630 15%
6–10× 336 8%
>10× 84 2%
Observation: 75% of users visit ≤3×/week; only 10% visit ≥6×/week.
Segment Breakdowns
By device
Mobile: 5,200 visits / 2,900 users → 1.79 visits/user (Average–Poor)
Desktop: 3,000 visits / 1,200 users → 2.50 visits/user (Good)
Tablet: 200 visits / 100 users → 2.00 visits/user (Average)
By channel
Channel Visits Users Visits/User
Direct 2,100 800 2.63 (Good)
Email 1,900 700 2.71 (Good)
Organic Search 2,600 1,600 1.63 (Poor)
Paid Social 1,100 800 1.38 (Poor)
Referral 700 300 2.33 (Good)
Qualitative Insights (from explanations column)
Top reasons influencing frequency:
Content cadence & relevance — “Weekly updates feel light; I check less often.”
Notifications — “Emails don’t match what I care about.”
Mobile friction — “Slow on first open; I bail.”
Utility spikes — “I return more when there are fresh promos/tools.”
Trust cues — “I rely on email summaries rather than visiting daily.”
Recommendations
Personalize re-engagement: segment by last action and interests; send targeted nudges (e.g., “New items in your saved category”).
Increase valuable content cadence to at least 2 drops/week; spotlight reasons to return (fresh deals, new guides).
Improve mobile first-load: optimize LCP; store session state so users return where they left off.
Create lightweight daily hooks: streaks, rotating highlights, or quick tasks to encourage habitual visits.
Channel tuning: shift budget from Paid Social to Email/Direct lookalikes; align SEO pages with reasons to return (evergreen hubs).
Next Checks
Track % of users ≥4×/week (currently 25%); target ≥35% in 6 weeks.
Monitor mobile LCP and correlate with visits/user; goal LCP ≤ 2.5s on 75th percentile.
A/B test personalized notifications vs. generic; success = +0.3 visits/user for targeted cohorts.
Target: Raise average visit frequency from 2.0 → ≥3.0 (Good).