Scrunch AI alternative: share of voice vs the query list
Scrunch AI is a capable AI brand-visibility and share-of-voice tool. The reason to weigh an alternative is what it hands back. Here's a fair, output-first comparison of share of voice against reverse search.
Scrunch AI is a capable AI brand-visibility tool built around share of voice — measuring how often, and how prominently, a brand shows up in AI answers relative to competitors. That is a legitimate and useful lens, especially for teams reporting category presence over time. The reason to consider an alternative is structural: share of voice is an aggregate. MentionRadar returns the underlying query-level list instead — the actual questions a domain is cited or mentioned on across ChatGPT, Gemini and Grok, each row carrying the query intent, which models named you, and the competitor domains named in the same answer. So the trade-off is summary versus source data: a single share-of-voice figure you can trend, or the raw list of queries and rivals you can act on row by row. If your job is discovery and gap-finding rather than trend reporting, the reverse-search model fits better. The free Domain Check returns that list with no card.
What is Scrunch AI, and what is it good at?
Scrunch AI is an AI brand-visibility tool oriented around share of voice — the measure of how present your brand is in AI answers compared to its competitors. That is a real strength: share of voice is easy to communicate, trends cleanly, and gives marketing leaders a single category-level pulse they can put in front of stakeholders. For teams whose primary need is to benchmark presence and show it moving, that aggregate is doing useful work, and we’ll say so plainly.
This page is about a different need: when the summary isn’t enough and you want the source data underneath it — the specific queries, the specific rivals, the specific gaps.
Share of voice vs the query list: what's the real difference?
A share-of-voice score answers “how much presence do I have?” A reverse search answers “on exactly which questions, and beside whom?” The first is an aggregate built for reporting; the second is a list built for action. Neither is wrong — they answer different questions. Here is the contrast by category, kept fair to each shape.
| Attribute | AI share-of-voice tool (Scrunch-style) | Reverse search (MentionRadar) |
|---|---|---|
| Primary output | A share-of-voice metric — aggregate presence vs competitors, trended over time | The complete query-level list a domain is cited or mentioned on |
| Names competitors per query? | Aggregated into share-of-voice comparisons across the category | Yes — the rival domains named in the same answer, row by row |
| Query-level list | Rolled up into the metric; individual queries are summarised | Discovered, itemised list with intent per row |
| Grok coverage | Varies by tool / plan — confirm on their site | Built to span ChatGPT, Gemini and Grok |
| Free check | Varies — check current plans | Free Domain Check returns a real query list, no card |
| Best for | Category benchmarking and trend reporting on brand presence | Discovery, competitive gap-finding and per-query worklists |
When is a share-of-voice tool the right call?
Be fair to the shape. A share-of-voice tool like Scrunch AI is the right call when:
- You need a single, trendable number. Leadership wants one figure that captures category presence and moves over time. An aggregate is exactly that.
- You report on cadence. Monthly or weekly benchmarking against named competitors is the job, and a clean metric communicates it fastest.
- Breadth over depth. You care more about “are we present overall?” than “which exact queries?” at this stage.
When do you outgrow the aggregate?
The moment someone asks “okay, which queries are we losing, and to whom?” the aggregate stops being enough. You need the itemised list: this question, these named rivals, this intent, this gap. That is the reverse AI search job — and it’s where a share-of-voice dashboard, by design, hands you a summary rather than a worklist. The most useful version of competitive data lives at the query grain, not inside a percentage.
How do teams combine the two?
Plenty of teams run both: a share-of-voice tool to report presence to leadership, and reverse search to do the actual gap-finding and content prioritisation underneath it. If you can only start with one, start with the artifact your team will act on this week. For most operators in build mode, that’s the query list. For agencies specifically, we cover how the two fit a client lifecycle in best AI visibility tools for agencies in 2026.
How do I decide?
Decide on output, not adjectives. Run the free Domain Check on your domain and a competitor’s, read the query list and the competitors named on each row, and ask whether that or a single share-of-voice figure is what your team will actually act on. For the full landscape — Scrunch AI, Peec AI, Profound, Otterly and reverse search side by side — start with the honest comparison of AI visibility tools in 2026.
Frequently asked questions
Is share of voice a bad metric?
No — share of voice is a perfectly good summary metric, and tools like Scrunch AI use it well for trend reporting and category benchmarking. The limitation is that it is an aggregate: it tells you how much presence you have, not which exact queries you win or lose. Reverse search gives you the source data under the metric. Use share of voice to report; use the query list to decide what to do next.
Does MentionRadar give a share-of-voice number too?
MentionRadar is built around the query list first — the questions a domain is cited on and the competitors named beside it. You can certainly count rows to understand presence, but the product’s primary unit is the query, not a single rolled-up score. We avoid leading with a number because the actionable detail lives in the list, not the aggregate.
Which models does each tool cover?
Model coverage varies by tool and changes over time, so confirm Scrunch’s current coverage on their site rather than trusting a static claim. MentionRadar’s index is built to span ChatGPT, Gemini and Grok, because the three regularly disagree on which sources they cite. Grok in particular is the most under-covered model across the category, so make “which models” an explicit checkbox when you compare.
Can I see the difference for free?
Yes. The free Domain Check returns a real query-level list for any domain across ChatGPT, Gemini and Grok — no card, no demo gate. Run it on your domain and a competitor’s and compare that artifact against a share-of-voice dashboard before you choose.