---
title: "Spreadsheet, Script or Tracker? The Small-Business AI Mention Test"
description: "Compare manual checks, an API script and paid trackers on cost, prompt volume, noisy answers and useful fixes. Then run a two-week test before you buy."
url: "https://groas.com/post/spreadsheet-script-or-tracker-three-ways"
image: "https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/2ca555ff-5f14-49cf-a2e7-4606344c87f7.png"
published: "2026-10-25T05:25:11.024Z"
modified: "2026-10-25T05:25:11.085Z"
---

October 25, 2026 · 11 min read

# Spreadsheet, Script or Tracker? The Small-Business AI Mention Test

[Alexander PerelmanHead Of Product @ groas](https://groas.com/author/alexander-perelman)[LinkedIn](https://www.linkedin.com/in/alexander-433793253/)

![A tabletop miniature where a ledger clerk, a paper-clip contraption and a sleek kiosk all pour reports onto one desk whose chair sits empty, its red pen capped.](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/2ca555ff-5f14-49cf-a2e7-4606344c87f7.png)

In this article

1. [The contenders: who does the checking, and who reads it?](#the-contenders-who-does-the-checking-and-who-reads-it)
2. [Axis 1: price the hours, not just the subscription](#axis-1-price-the-hours-not-just-the-subscription)
3. [Axis 2: find the prompt count you will sustain](#axis-2-find-the-prompt-count-you-will-sustain)
4. [Axis 3: separate a trend from a noisy answer](#axis-3-separate-a-trend-from-a-noisy-answer)
5. [Axis 4: keep the competitor and citation beside the mention](#axis-4-keep-the-competitor-and-citation-beside-the-mention)
6. [Axis 5: demand a fix, not another missing-mentions chart](#axis-5-demand-a-fix-not-another-missing-mentions-chart)
7. [Run the two-week, 20-prompt comparison](#run-the-two-week-20-prompt-comparison)
8. [Verdict: the spreadsheet wins first](#verdict-the-spreadsheet-wins-first)

A small business tracking 20 buyer prompts does not need a paid AI visibility dashboard. It needs someone to read the answers. If you are choosing between a spreadsheet, a homemade script and a paid tracker to monitor mentions in ChatGPT, Perplexity and Google AI Overviews, that is the decision hiding beneath the feature lists.

**Prompt tracking is the search terms report of AI search:** collecting it is cheap; acting on it is the expensive part. I watched the same pattern in Google Ads. The report showed which searches wasted money, but access to the report never added a negative keyword. For most small operations, a spreadsheet wins until the workload reaches roughly 30 prompts. Beyond that, the question becomes whether you have someone to maintain a script or someone who will read a tracker and make the fixes.

## The contenders: who does the checking, and who reads it?

**Option A: a spreadsheet.** Pick 10 to 20 real buyer questions, run them weekly in the consumer interfaces, and record whether your brand appears, which competitors appear and what sources the answer cites. A [manual baseline of 10 to 20 prompts](https://trydecoding.com/) is a sensible place to start. It costs no software subscription and makes you look at the answers rather than admire a score.

**Option B: a script connected to model APIs.** The [open-source script pattern](https://github.com/comeca-ai/llm-visibility-tracker) queries prompts on a schedule and writes the results to Sheets. For a small prompt set, the example puts API spend at roughly $1 to $3 a month. The trade is setup and maintenance, plus a crucial coverage gap: an API response is not necessarily the answer a buyer sees in a consumer app. Keep manual interface checks in the routine.

**Option C: a paid visibility tracker.** Example plans start at [$29 a month for 15 prompts and reach roughly $95 a month for 50](https://auditae.app/); other tiers run from [$99 to $649 as check frequency and volume rise](https://elmohq.com/). You pay to avoid building and tending the machinery. You do not, by paying, acquire a person who will decide what to change.

Those are the contenders. I would compare them on five axes: real cost, sustainable prompt volume, answer noise, diagnostic detail and the distance from a report to a fix.

## Axis 1: price the hours, not just the subscription

A spreadsheet is free in the same sense that a search terms report is free. **The expensive line is the time spent checking and reading it.** At 20 prompts across three engines, you have 60 checks a week. Each takes a copy, a paste and a judgment call about the answer. At an illustrative two to three hours weekly and $40 an hour for an in-house marketer, that is about $320 to $480 a month in time.

Do not compare that whole figure with a tracker subscription as though the tracker eliminates it. Some of those hours are the work you came to do: spotting the competitor that keeps appearing, reading the cited page and deciding whether your own page needs a better answer. A tracker can remove repetitive collection. It cannot make the decision for you.

A script makes the collection bill small. One [pay-per-check menu](https://auditae.app/) lists Perplexity at 5 cents, ChatGPT at 7 cents and AI Overviews at 9 cents per check. Those prices look delightful beside a salary until an API changes or the scheduled job fails. Allow roughly four to eight hours to build the setup and an hour a month to maintain it. If nobody owns that hour, the cheap script becomes an old spreadsheet that stopped updating.

A paid tracker substitutes a recurring bill for that upkeep. A [one-time audit of 50 buyer prompts run daily for seven days](https://aiseotracker.com/) is another price shape: $49 once, rather than an ongoing subscription. Neither price tells you whether the output is worth buying. Ask what time it saves _and_ what decision it helps you make. **Pay to remove work you would otherwise do, not to avoid work you still need to do.**

## Axis 2: find the prompt count you will sustain

My working cutoff is **about 30 prompts for a manual spreadsheet**. Below that, opening the apps and logging answers is usually less trouble than configuring a system. Above it, three engines mean more than 90 checks per session before you repeat anything. Sessions get long; then a week gets skipped. A tracker that runs faithfully and a person who does not will produce the same number of useful decisions: zero.

This is not a universal limit. If you have a catalog with 200 SKUs and distinct buyer questions for each group, skip the spreadsheet as your primary collection method. If you have 15 questions that matter, do not buy capacity for hundreds simply because the sales page offers it. Count the prompts you can name from sales calls, support tickets and search terms, not the prompts a dashboard invites you to invent.

A maintained script handles a larger set without multiplying copy-paste work. The [open-source pattern logs mentions, position, sentiment and brand lists on a schedule](https://github.com/comeca-ai/llm-visibility-tracker). That makes it attractive when you have technical help and a growing prompt list. A tracker offers volume without code maintenance; some [paid tiers check each prompt four times daily](https://elmohq.com/). That frequency matters only if you need repeated answers and will inspect the pattern they form.

Prompt volume is therefore a staffing question disguised as a feature question. A 30-prompt threshold is not a command to buy software on prompt 31. It is the point to ask whether manual checking still happens every week. **Choose for the list you will actually maintain.**

## Axis 3: separate a trend from a noisy answer

Run the same buyer question twice and you may get two different brand lists. I would not treat either answer as a verdict on your visibility. In a [2,961-run test of 12 prompts](https://sparktoro.com/blog/new-research-ais-are-highly-inconsistent-when-recommending-brands-or-products-marketers-should-take-care-when-tracking-ai-visibility/), SparkToro and Gumshoe found striking inconsistency in the brands and their order across ChatGPT, Claude and Google AI. Mention rate across repeated runs was more useful than any single ordering.

**One answer is an observation; mention rate is the pattern.** This is where the methods diverge. A spreadsheet with one weekly check per prompt gives you a yes or no that may flip next week without a meaningful change behind it. You can still [standardize and store manual checks over time](https://trydecoding.com/), and you can repeat the prompts you care about most. You just pay for those repeats in time.

A script can repeat API queries cheaply, provided you program it to save every run instead of overwriting the last one. A tracker built around repeated sampling can do that collection for you and average the results. Neither escapes the underlying variation. A tidy [AI visibility score is still a sample, not a ranking](https://groas.com/post/why-your-ai-visibility-number-changes-ev). If a report shows a single score without telling you how often it checked, do not read its precision as certainty.

There is a second source of noise: methods can observe different surfaces. The answer returned through an API may differ from the answer in the consumer interface your customer uses. Repeated API checks are useful for monitoring the API result; they do not erase that gap. **Keep a recurring manual check in the actual apps, whichever automated method you choose.**

## Axis 4: keep the competitor and citation beside the mention

The useful question is not simply, “Did the AI mention us?” It is, “**Which buyer question mentions a competitor instead, and what did the answer cite?**” That turns a visibility report into a possible task. Without the competing brand and source, a missing mention is just another red cell.

A spreadsheet can preserve that context at small scale. Alongside prompt, engine and date, add two columns people tend to skip: _other brands named, in order_ and _links cited_. When a prompt misses your brand, you can read what appeared instead. It is unglamorous. So was negative-keyword mining, and that did not make it optional.

A [script can log the brand list and a diff from the prior run](https://github.com/comeca-ai/llm-visibility-tracker). That helps you find changes without comparing rows by eye, provided the script also keeps the answer and sources you need to inspect. A tracker may present the full answer with brand mentions, competitors and citations together. That is the part of the interface worth evaluating, not the color of the chart.

If you want to [build a weekly monitor with Sheets and APIs](https://groas.com/post/build-your-own-ai-visibility-monitor-in), copy the logging discipline before copying the schedule. A list of Y/N mentions scales easily and explains very little. **Save enough of the answer to know what you could change.**

## Axis 5: demand a fix, not another missing-mentions chart

All three options can disappoint here. A spreadsheet says you are absent. A script says it on schedule. A cheap tracker may say it with better colors and a sentiment label. None of those outputs, by itself, rewrites a page or earns a citation.

**The paid feature worth inspecting is a prioritized action tied to a prompt and a source.** Better trackers [pitch repeated sampling alongside fixes](https://elmohq.com/), not just a count of mentions. The [one-time audit pattern](https://aiseotracker.com/) makes the distinction clear: its 50 prompts run daily for seven days across five engines, with rank by engine, brands above you, cited sources and a prioritized action plan. That plan is the purchase. If it cannot point you toward the page to improve, the list to get onto or the question to answer better, it is another search terms report waiting for an owner.

The spreadsheet can still win this axis for a small business because its owner has to read each answer. A person close to the product may see a missing explanation immediately. A tracker wins when it preserves that context at a volume the person cannot collect by hand. A script wins on collection cost if somebody will translate its diffs into work. For every option, put a name beside the next action before you pay for the next check.

## Run the two-week, 20-prompt comparison

Here is the test I would run before committing to software. **Question:** Which buyer prompts mention your brand, and which apparent changes survive repeated checks? My expectation is that individual answers will disagree more than the mention-rate pattern. The mechanism is sampling: each run can return a different brand list, so a change deserves attention only when it persists across repeats.

**Setup:** Choose 20 actual buyer questions from sales calls, support tickets and search terms. Use the questions people ask, such as “best payroll software for 10-person construction firm,” rather than a bare category like “payroll software.” Track the same prompt wording for two weeks across at least three engines. Give the spreadsheet, script and tracker the same prompt list. Repeat checks rather than asking each question once.

**Controls:** Use fresh chats, stay logged out where possible, keep the city setting consistent and paste the wording exactly. Check consumer products in the interfaces buyers see; do not treat an API-only script as an exact substitute. If your script uses APIs, pair it with a manual interface pass each week and label those results separately. The [difference between API checks and the buyer view](https://elmohq.com/) is part of what you are testing, not an inconvenience to hide.

**Log on every run:**

- Prompt wording, engine and date.
- Brand mentioned: Y or N, plus its position if present.
- Other brands in answer order, and links cited.
- A one-line sentiment note and the method that collected the answer.

Then compare mention rates over repeats, not the order in one answer. A move from 30% to 40% across 10 runs is not a reason to announce a win. Look at the answers and keep checking. If one prompt consistently names a competitor and cites a source you lack, you have a more useful finding than a small shift in your overall score. The [repeat-and-log mention audit](https://groas.com/post/run-your-own-ai-mention-audit-a-repeatab) gives you a reusable version of the same routine.

What changes after two weeks? If the spreadsheet produces readable findings without skipped sessions, keep it. If collection crowds out reading, automate the collection. If an automated report gives you more checks but no clearer next task, do not mistake that for progress. **The test is whether a method changes what you do, not how many rows it fills.**

![Spreadsheet, script logs and a tracker dashboard showing different brand mentions](https://pub-87da24ecbbfc4c3bad6875f3aa013712.r2.dev/generated-images/636df042-a025-4b5b-8499-850f26716f65.png)

## Verdict: the spreadsheet wins first

**Under about 30 prompts, pick the spreadsheet.** It costs $0 in tools, makes you inspect the answers and leaves you with enough context to act. Its estimated $320 to $480 a month in staff time at 20 prompts is real, but a subscription does not eliminate the reading you need to do. Switch when collection becomes the reason you stop checking.

Pick the script instead if checks recur, your prompt list is growing and somebody technical can own roughly four to eight hours of setup plus an hour a month of maintenance. Pick the paid tracker if you need more prompts or engines and do not intend to maintain code. That is the case for the spreadsheet to lose. The tracker still needs a named reader.

| Setup                 | Cost illustrated here                                                                | Where it strains                                                     | Handling of noisy answers                           | Route to a fix                                         |
| --------------------- | ------------------------------------------------------------------------------------ | -------------------------------------------------------------------- | --------------------------------------------------- | ------------------------------------------------------ |
| Spreadsheet           | $0 in tools; at 20 prompts, roughly 8–12 staff hours monthly                         | Around 30 prompts becomes hard to sustain manually                   | Weak with one check; improves if you repeat and log | Strong context if you record competitors and citations |
| API script and Sheets | Roughly $1–$3 monthly API spend in the small-set example, plus setup and maintenance | Needs technical ownership; API results may differ from consumer apps | Can repeat cheaply if built to save each run        | Logs changes, but a person must decide the fix         |
| Paid tracker          | Example subscriptions start at $29 monthly and reach $649 at higher tiers            | The subscription is wasted if nobody reads it                        | Repeated sampling depends on the plan               | Only useful when its findings lead to a specific task  |

I would pay for collection only after proving that collection is the bottleneck. groas is built for the second half: continuous execution against what the prompts show, with a named strategist accountable for the outcome rather than another chart announcing that you are missing. Run the two-week test. Keep the method someone will read, then spend your effort on the page, answer or citation that reading tells you to fix.

## Frequently Asked Questions

### How many buyer prompts can I track manually in a spreadsheet before it becomes unmanageable?

Roughly 30 prompts is the working cutoff for a manual spreadsheet. Below that, running 10 to 20 real buyer questions weekly in ChatGPT, Perplexity and Google AI Overviews and logging answers is usually less trouble than configuring a system. Above it, sessions get long and weeks tend to get skipped.

### What does it really cost to track AI mentions manually each week?

At 20 prompts across three engines you have about 60 checks a week, which adds up to roughly $320 to $480 a month in staff time at an illustrative $40 an hour. Some of those hours are the work you came to do, such as reading cited sources and deciding what to change, and a subscription does not eliminate them.

### Is a DIY API script cheaper than a paid AI visibility tracker?

Usually yes on collection: a small prompt set costs roughly $1 to $3 a month in API spend, plus about four to eight hours to build and an hour a month to maintain. But an API response is not necessarily the answer a buyer sees in a consumer app, and if nobody owns the maintenance, the script stops updating.

### Why do AI answers give different brand recommendations when I run the same question twice?

Repeated runs of the same prompt can return different brand lists and orderings. In a 2,961-run test of 12 prompts, SparkToro and Gumshoe found striking inconsistency across ChatGPT, Claude and Google AI. Treat one answer as an observation and mention rate across repeats as the pattern, not either single run as a verdict.

### What should I record when checking whether AI mentions my brand?

Log the prompt wording, engine and date, whether your brand appeared and its position, the other brands named in order, the links the answer cited, and a one-line sentiment note plus the method used. The competitors and citations are what turn a missing mention into a task you can act on.

### Is a paid AI visibility tracker worth the money for a small business?

Only if it produces a prioritized action tied to a prompt and a source, not just a count of missing mentions. Example subscriptions start at $29 a month for 15 prompts, but paying does not buy a person who decides what to change. The worth depends on whether findings point to the page or question to improve.

### How should I test AI visibility tools before choosing one?

Pick 20 real buyer questions from sales calls, support tickets and search terms, give the spreadsheet, script and tracker the same list, and track the same wording for two weeks across at least three engines. Use fresh chats, stay logged out where possible, repeat checks rather than asking once, and compare mention rates over repeats instead of single answers.

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