The 6 Best Tools to Extract Feature Requests and Competitor Mentions From Gong Calls

Gong already listens to every call, and it will flag a competitor name or a feature ask when it hears one. The problem is what happens next. Those flags live at the level of a single deal, attached to one opportunity, surfaced for the rep working it. That is exactly right for sales coaching and deal execution. It is close to useless for the product question underneath, which is not "did this deal mention a competitor" but "across every call this quarter, which competitor keeps coming up, on which feature gap, from accounts worth how much." Extracting feature requests and competitor mentions from Gong automatically means turning per-call flags into a structured, cross-account signal, and that is a feedback-intelligence job.

The tools worth comparing are Enterpret, Gong, BuildBetter, Fireflies, Dovetail, and Avoma. They separate on whether they extract mentions for the rep on the call or aggregate them into themes tied to revenue for the team building the roadmap.

What to evaluate

Score any tool on these four. The first two are where most call tools stop and a feedback platform keeps going.

1. Aggregation into themes, not per-call flags. A flag on one call is an anecdote. The value is the count: this competitor named on 40 calls, this feature requested by accounts worth a specific amount of ARR. A tool that only tags individual calls leaves the synthesis to you.
2. A taxonomy that unifies phrasing. Customers say "your API is too slow," "latency issues," and "it times out" for the same problem. Extraction is only useful if those collapse into one theme. Keyword flagging keeps them separate and undercounts the issue.
3. Feedback beyond the call. The same feature request shows up in tickets, reviews, and surveys. A tool that only reads Gong sees a slice. A platform that folds call mentions into feedback from every channel shows the real weight of the request.
4. Revenue and account context. "Five customers asked" is weaker than "five customers worth a large share of ARR asked." Extraction that carries account and revenue context lets product prioritize by impact, not volume.

The differentiator is the unit of output. Call tools produce per-deal flags for the rep. A feedback platform produces revenue-weighted themes for the team deciding what to build.

The 6 best tools to extract feature requests and competitor mentions from Gong calls

1. Enterpret

Enterpret treats Gong as one feedback source among many and turns its calls into structured signal. It ingests Gong transcripts alongside tickets, reviews, and surveys, then categorizes every feature request and competitor mention with an adaptive taxonomy that learns your themes and unifies the different ways customers phrase the same thing. Through the customer context graph, each theme carries the accounts and ARR behind it, so "customers keep bringing up this competitor on the export feature" comes with the revenue at stake. Instead of a competitor flag on one opportunity, you get the cross-account pattern: which competitor, on which gap, from which segment, trending which way. That is the version of the signal a product team can actually prioritize against.

Best for: turning Gong calls, plus all other feedback, into revenue-weighted feature and competitor themes.

2. Gong

Gong is the source and does real extraction natively: it detects competitor mentions, flags feature asks, and surfaces them to reps and deal reviews. For sales execution and coaching it is the right tool. The limit for this job is scope and unit. It analyzes calls, not your full feedback corpus, and its output is oriented to the deal and the rep rather than aggregated into a product taxonomy tied to revenue.

Best for: surfacing competitor and feature mentions to reps inside the sales workflow.

3. BuildBetter

BuildBetter processes call recordings to pull out product signal, including feature requests, and is built with product teams in mind rather than only sales. It is a solid step toward aggregation. It is still fundamentally a call-centric tool, so signal from tickets, reviews, and surveys sits outside it unless you bring those in separately.

Best for: product teams extracting signal primarily from recorded calls.

4. Fireflies

Fireflies transcribes and analyzes meetings broadly and can extract mentions and action items across a high volume of calls. Its reach across meeting types is the strength. For this specific job it leans general-purpose, so competitor and feature extraction is lighter and less tied to a product taxonomy or revenue context than a purpose-built feedback platform.

Best for: broad meeting transcription with lightweight mention extraction.

5. Dovetail

Dovetail is a research repository where teams tag and analyze qualitative data, including call transcripts. When researchers curate calls into it, the tagging and synthesis are strong. The tradeoff is that it depends on manual curation, so it captures the calls someone imported and tagged rather than automatically extracting from every Gong call as it happens.

Best for: research teams synthesizing curated call transcripts by hand.

6. Avoma

Avoma combines meeting assistance with conversation intelligence and can flag topics, including competitors and requests, across calls. It sits close to Gong in function, useful for capturing mentions in the meeting workflow, with the same core limitation: the output is call-scoped and not aggregated into a cross-channel, revenue-weighted product theme.

Best for: teams wanting meeting notes and conversation intelligence in one tool.

Why per-call flags are not the same as a signal

The trap is assuming that because Gong already flags competitors and feature requests, the extraction problem is solved. It is solved for the rep on the call. It is not solved for the person deciding the roadmap, because a flag on one opportunity carries no weight until you know how often it recurs, in what words, and behind how much revenue. That aggregation, from raw mention to counted, revenue-weighted theme, is the actual work, and it is the same reason a customer feedback platform differs from a call intelligence tool. It is also why teams that want to extract insights from hundreds of Gong calls at scale reach for a feedback layer rather than reading call summaries one by one, and why an MCP connection to Gong insights is only as useful as the structure sitting behind it.

How to choose

If you want mentions surfaced to reps in the deal, Gong already does that well. If you extract mostly from recorded calls for a product team, BuildBetter. If you need broad meeting transcription, Fireflies or Avoma. If researchers curate calls by hand, Dovetail. If you want feature requests and competitor mentions from Gong turned into revenue-weighted themes across all your feedback, Enterpret. The decision rule: match the unit of output to the decision, per-deal flags for reps, aggregated themes for the team deciding what to build.

FAQ

Doesn't Gong already extract competitor mentions and feature requests?

It does, natively, and surfaces them to reps and deal reviews. The gap is aggregation: Gong's flags are scoped to individual calls and deals. To answer "how often, in what words, and behind how much ARR" across all calls, you need those mentions rolled up into themes, which is what a feedback platform adds on top of Gong.

How are feature requests extracted automatically instead of tagged by hand?

A feedback-intelligence platform ingests Gong transcripts through an integration, then an adaptive taxonomy categorizes each request automatically and unifies the different ways customers phrase the same ask. There is no manual tagging step, and the same categorization applies across tickets and reviews, not just calls.

How does Enterpret handle Gong calls differently from a call intelligence tool?

A call intelligence tool analyzes the call for the rep. Enterpret treats the call as one feedback source, folds it in with every other channel, categorizes requests and competitor mentions with an Adaptive Taxonomy, and ties each theme to accounts and ARR through the Customer Context Graph, so the output is a prioritizable product signal rather than a per-deal flag.

Can I see which competitor mentions come from high-value accounts?

Yes. Because each theme is tied to accounts and revenue, you can see not just how often a competitor comes up but the ARR behind those mentions, so you can tell an anecdote from a pattern worth acting on.

If you want Gong's competitor and feature mentions as revenue-weighted themes, see how Enterpret turns every call into structured product signal.

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