How Often to Track AI Visibility: Daily, Weekly or Monthly?

Track often enough to collect the answers your decision needs. A 10% mention rate measured on one run of 50 prompts on one engine has a 95% interval of ±8.5 points. Across four engines it's ±4.2, and over a week of daily runs ±1.6. Track daily if you report weekly, weekly if you report monthly, and monthly only for a baseline.

The question "how often should we track AI visibility?" sounds like scheduling. It's really statistics. AI answers change on every re-ask, so each run is a sample, and the precision of your number depends on how many samples you pool.

This post shows real intervals from our own tracking data and the answers you need for a given precision. It also gives a script for planning your schedule and a decision framework for daily vs weekly vs monthly.

Data used: 35 unbranded prompts × 4 engines (ChatGPT, Gemini, Perplexity, Claude) × 2 runs on 2026-10-20, collected by BugViso's AI Visibility Tracker for bugviso.com against five anonymised competitors.

Why Frequency Is a Sample-Size Question

Two facts from our data drive everything below.

Answers drift. We asked the same 50 prompts twice, 23 minutes apart. In pairs where an answer named a tracked brand, the second answer named a different set of brands 40% of the time (full drift study). A single run is one draw from a distribution.

Rates are small. Our brand was named in 21 of 280 unbranded answers, a 7.5% mention rate. Small proportions have wide intervals relative to their size. Moving from 6 mentions to 9 looks like +50% growth but is well within noise.

The right tool is a confidence interval for a proportion. We use the Wilson score interval. It behaves well near 0% and with small samples, where the textbook normal approximation breaks (binomial proportion confidence intervals).

Real Intervals From One Day of Tracking

Mention rates for our brand on unbranded prompts, with 95% Wilson intervals:

What the table lets you say, and not say:

• ChatGPT vs Gemini: no conclusion. 8.6% and 4.3% look different, but the intervals overlap heavily.
• Perplexity vs Claude: a real gap. The intervals don't overlap.
• Claude's 0% is not "zero". With 70 answers, the true rate could be as high as 5.2%.
• The leader is clearly ahead. Its interval starts at 20.6%, far above our upper bound of 11.2%.

BugViso's tracker computed this interval itself on the first run alone: 4.4% – 13.5% on 140 answers. The second run narrowed it, which is the point of collecting more answers.

How Precision Grows with Answers

This is the planning table. It assumes a true mention rate of 10% and shows the 95% interval for common setups. It's the output of the script further down.

true mention rate assumed: 10%
 prompts engines  runs  answers      95% interval  +/- pts
      25       1     1       25       2.2-25.0      11.4
      50       1     1       50       4.3-21.4       8.5
      50       4     1      200       6.6-14.9       4.2
      50       4     7     1400       8.5-11.7       1.6
      50       4    30     6000       9.3-10.8       0.8
     100       4     7     2800       8.9-11.2       1.1

And for a brand with a 25% mention rate:

true mention rate assumed: 25%
 prompts engines  runs  answers      95% interval  +/- pts
      50       1     1       50      14.3-37.4      11.6
      50       4     1      200      19.5-31.4       6.0
      50       4     7     1400      22.8-27.3       2.3
      50       4    30     6000      23.9-26.1       1.1

Higher rates need more answers for the same margin in points, because the variance of a proportion peaks at 50%.

Answers needed for a target margin

Read it as: to report a brand at about 10% visibility to within ±3 points, you need about 384 answers in the window. This is ordinary sample size determination, applied to answers instead of survey respondents. With 50 prompts and 4 engines (200 answers per run), that's two runs. To get ±2 points, it's five runs.

Daily vs Weekly vs Monthly: What Each Schedule Buys

Assume 50 prompts and 4 engines, so 200 answers per run, and a 10% mention rate.

"Detects reliably" is a rule of thumb: two periods differ convincingly when their intervals don't overlap, which needs a change of about twice the margin.

This gives a simple rule. Report one level slower than you run. What goes into that report is covered in our AI visibility report template. Daily runs make weekly reports trustworthy. Weekly runs make monthly reports trustworthy. Monthly runs are a snapshot, fine for a baseline or a quarterly check, but too noisy to show the effect of a single content change.

💡 Rule of thumb: if you can't act on a weekly number, you don't need daily runs. If you show clients a weekly chart, you do.

A Decision Framework

Two things change the math for you:

• More prompts beat more runs when your prompts are diverse. 100 prompts weekly gives the same answer count as 50 prompts twice a week, with more topic coverage. Our prompt set guide covers how to build one.
• Per-engine and per-topic segments need their own sample. A ±1.6-point weekly headline can still hide a Claude segment of 70 answers with a ±6-point interval.

Segments Need Their Own Sample

A headline built on 1,400 weekly answers can be precise while the segments under it are not. Our 50 prompts fell into 7 topics with 3 to 9 prompts each. With 4 engines and daily runs, a topic of 3 prompts collects 84 answers a week. One of 9 prompts collects 252.

Two practical consequences:

1. Small topics need longer windows. Report a three-prompt topic monthly, or add prompts to it. A weekly chart for it would be mostly noise.
2. Engine × topic cells are almost always low confidence. 21 answers a week can't separate 5% from 25%. Use these cells to find answers worth reading, not to report movement.

This is why a minimum segment size matters more than the schedule. A report that shows every engine × topic cell weekly invites clients to read noise as news.

Plan Your Schedule with a Script

This standard-library script computes Wilson intervals, the answers needed for a target margin, and the planning grid above.

#!/usr/bin/env python3
"""Confidence intervals and sample sizes for AI visibility (mention rate) tracking.

Usage:
  python3 wilson_sample_size.py ci 21 280            # 21 mentions in 280 answers
  python3 wilson_sample_size.py plan 0.10 0.03       # answers needed for +/-3 pts at a 10% rate
  python3 wilson_sample_size.py grid 0.10            # interval width for common prompt x engine x run setups
"""
import math, sys

Z = 1.96  # 95% confidence

def wilson(k, n, z=Z):
    if n == 0:
        return 0.0, 0.0
    p = k / n
    d = 1 + z * z / n
    centre = (p + z * z / (2 * n)) / d
    half = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / d
    return max(0.0, centre - half), min(1.0, centre + half)

def answers_needed(p, margin, z=Z):
    """Smallest n whose Wilson half-width at rate p is <= margin."""
    n = 1
    while True:
        lo, hi = wilson(round(p * n), n, z)
        if (hi - lo) / 2 <= margin:
            return n
        n += 1

def main():
    cmd = sys.argv[1] if len(sys.argv) > 1 else "grid"
    if cmd == "ci":
        k, n = int(sys.argv[2]), int(sys.argv[3])
        lo, hi = wilson(k, n)
        print(f"{k}/{n} = {100 * k / n:.1f}%   95% interval {100 * lo:.1f}% - {100 * hi:.1f}%")
    elif cmd == "plan":
        p, m = float(sys.argv[2]), float(sys.argv[3])
        print(f"rate {100 * p:.0f}%, margin +/-{100 * m:.0f} pts -> {answers_needed(p, m)} answers")
    else:
        p = float(sys.argv[2]) if len(sys.argv) > 2 else 0.10
        print(f"true mention rate assumed: {100 * p:.0f}%")
        print(f"{'prompts':>8}{'engines':>8}{'runs':>6}{'answers':>9}{'95% interval':>18}{'+/- pts':>9}")
        for prompts, engines, runs in [(25, 1, 1), (50, 1, 1), (50, 4, 1), (50, 4, 7), (50, 4, 30), (100, 4, 7)]:
            n = prompts * engines * runs
            lo, hi = wilson(round(p * n), n)
            print(f"{prompts:>8}{engines:>8}{runs:>6}{n:>9}{100 * lo:>10.1f}-{100 * hi:<6.1f}{100 * (hi - lo) / 2:>8.1f}")

if __name__ == "__main__":
    main()

Real output for our numbers:

$ python3 wilson_sample_size.py ci 21 280
21/280 = 7.5%   95% interval 5.0% - 11.2%
$ python3 wilson_sample_size.py ci 0 70
0/70 = 0.0%   95% interval 0.0% - 5.2%
$ python3 wilson_sample_size.py plan 0.10 0.03
rate 10%, margin +/-3 pts -> 384 answers

Edit the (prompts, engines, runs) list in grid to match the plan you're considering. The interval math uses only Python's math module. Answers are counted as independent samples. Repeat answers to the same prompt are correlated, so treat the result as a lower bound when your prompt set is small.

How BugViso Schedules and Pools Runs

BugViso's AI Visibility Tracker is built around run frequency and pooled measurement:

• You choose when it runs. Automatic runs are off by default. Turn them on per brand and pick daily, weekly or monthly, or start a run any time with Run now. Each run asks every prompt on every engine and uses 1 credit.
• At most one automatic run a day. The scheduler starts a brand's automatic run once per period: once a day on daily, every 7 days on weekly, once a calendar month on monthly.
• The schedule card shows the cost. For each frequency it shows credits per month and how long your current balance lasts, before you switch.
• Weekly wrap-ups pool daily runs. The weekly report combines every daily run of the week on the same prompt version, and compares prompts won, gained and lost with the previous week.
• Intervals and low-confidence flags. Pooled visibility shows a 95% Wilson interval, and any segment under 20 answers is flagged low confidence.

The schedule options and pooled reports are on the AI visibility tracker page.

Mistakes That Waste Runs

Running daily, reporting daily. A day of 200 answers at 10% visibility is ±4.2 points. Daily charts mostly show noise, so pool before you plot.

Comparing a pooled week with a single run. Use the same window length on both sides of any comparison.

Editing prompts mid-period. A changed prompt set is a different instrument, and pooling across versions mixes two measurements. Change prompts at period boundaries.

Ignoring per-engine sample size. Four engines share the headline sample, but each engine only has a quarter of it. Check engine intervals before you write "Claude dropped".

Paying for precision you won't use. If a report goes out monthly and nobody acts in between, weekly runs give ±2 points a month, which is enough.

FAQ

How often should I check my brand in ChatGPT?

Check it as part of a fixed prompt set, at least weekly. One run of 50 prompts on ChatGPT alone gives a ±8.5-point interval at a 10% mention rate. Pool a week of daily runs if you need to see small changes.

Is daily AI visibility tracking worth it?

It is if you report weekly or are measuring the effect of a change. Daily runs give about 1,400 answers a week across 4 engines, enough for ±1.6 points. If you only report monthly, weekly runs are usually enough.

How many prompts do I need to track AI visibility?

Think in answers: prompts × engines × runs. For ±3 points at a 10% rate you need about 384 answers. That's 50 prompts on 4 engines for 2 runs, or 100 prompts for 1 run.

Why does my AI visibility jump around every day?

Because each answer is a sample. Engines rewrite your question into different searches and read different pages on each call. Pool several runs and look at intervals before treating a change as real.

Is monthly tracking enough for AI visibility?

For a baseline or a quarterly check, yes. A single monthly run of 200 answers can only show large moves (around 8 points or more at a 10% rate). It won't show the effect of individual content changes.

Conclusion

Set your AI visibility schedule from the answers you need, not the calendar: run one level faster than you report and always show intervals, which is how BugViso's AI visibility tracking pools daily runs into weekly trends.