Grok

Verified against Grok · 2026-07-27

Build a competitive intelligence report from live X mentions, not a vibe

Structures a competitor-mentions scan into a comparison table with sourced quotes and explicit per-dimension confidence levels, so a competitive read holds up under a follow-up question instead of collapsing into an unsupported impression.

GrokGrok on X5 fillable variables

The prompt

Ready to copy — highlighted parts are example details you can swap.

You are building a competitive intelligence report from live X mentions of named competitors, using Grok's real-time post access. Every comparative claim needs a source; an impression that people seem to like a competitor more than us, with no post backing it, is not a finding — it's a guess wearing a findings format.

OUR BRAND
Fieldnote, a field-service scheduling app for small contracting businesses

NAMED COMPETITORS
Jobber and Housecall Pro

TIME WINDOW
Past 30 days

SPECIFIC COMPARISON DIMENSIONS
Mobile app reliability complaints, and response time when users publicly complain about a bug

DECISION THIS REPORT FEEDS
Product team deciding whether mobile stability should be next quarter’s top engineering priority over the two features currently ahead of it in the backlog

REPORT RULES
Search live X mentions for our brand and each named competitor separately across Past 30 days, then compare only along the specific dimensions listed — do not drift into a general who's-winning narrative if the ask was about one specific thing, like response time to complaints or a specific feature people are requesting. Match volume to context before drawing any conclusion from it: a competitor with ten times the mention volume might simply have ten times the user base, so a raw mention-count difference is not evidence of a sentiment or quality gap on its own — normalize your read by mentioning the scale difference explicitly rather than letting it silently inflate a comparison. Separate what users are praising a competitor for from what users are merely mentioning neutrally — a competitor named in passing while a user is still deciding is a different data point than one being actively praised, and folding both into one tally overstates the competitor's actual standing. Look specifically for churn signals — users explicitly stating they left one product for another and why — since these are the highest-value data points in a competitive scan and are easy to lose inside a larger volume of generic mentions if you're not searching for that pattern directly. Note anything a competitor appears to be doing that's generating notably positive reaction and that we are not currently doing, since that is the most actionable output of a report like this — a comparison that only confirms existing assumptions without surfacing a concrete gap has not earned its own existence. State your confidence per dimension separately rather than one blanket confidence for the whole report — a comparison might be well-supported on pricing sentiment and thin on support-quality sentiment within the same time window, and collapsing that into one number hides which conclusions are safe to act on.

OUTPUT FORMAT
1. A table: Dimension | Us | Jobber and Housecall Pro | Confidence | Key supporting quote per brand.
2. Churn signals found, quoted, with the stated reason for switching.
3. The single most actionable gap: something a competitor is doing well that generated real positive reaction and we are not currently doing.
4. What this report cannot tell you — the specific question Product team deciding whether mobile stability should be next quarter’s top engineering priority over the two features currently ahead of it in the backlog needs answered that live X mentions alone cannot settle.

Customize

Optional — swap in your own details for the highlighted parts above.

Why this works

The requirement to normalize mention volume against user-base scale addresses the single most common distortion in any social-listening comparison: absolute mention counts are driven as much by how many people use a product as by how they feel about it, and a report that presents raw counts side by side without acknowledging the scale gap invites a reader to draw a sentiment conclusion from what is actually mostly a market-share fact, which is a different claim entirely. Separating praise from neutral mention matters because a passing mention from someone still deciding and a genuine 'this is better than everything I've tried' post are both technically mentions of a competitor, but only one of them is evidence of anything resembling satisfaction, and a naive tally that counts both the same way manufactures a rosier competitor picture than the actual posts support. The explicit instruction to search for churn signals as their own pattern, rather than trusting them to surface inside a general mentions scan, reflects how live search actually behaves: a churn admission is one post among potentially hundreds of more generic mentions in a 30-day window, and a broad query optimized for volume will not reliably surface the rare, highest-value post unless the search is specifically pointed at that pattern rather than left to emerge on its own. Per-dimension confidence, instead of one report-wide confidence score, matches how live-social evidence actually distributes — a heavily discussed reliability complaint might produce dozens of clear posts while a support-response-time comparison might rest on five ambiguous ones within the same window, and forcing a single confidence number across both dimensions would either overstate the thin one or understate the strong one. The final requirement to name what this report cannot tell you exists because a competitive intelligence report built entirely from public social posts is structurally blind to anything users don't post about — churned users who left quietly without a public complaint, or satisfied users who never mention a product they have no complaint about — and naming that blind spot explicitly is what stops a decision-maker from treating a social-listening scan as a substitute for the actual usage or win-loss data the underlying decision may still require.

What you get back

Dimension | Fieldnote | Jobber | Housecall Pro | Confidence | Quote Mobile reliability complaints | 4 posts, all about a specific scheduling-sync crash | 11 posts, mostly about slow load times, not crashes | 6 posts, mixed | medium — scale-adjusted, Jobber's rate is still higher than ours | "Jobber crashed on me mid-job again, third time this month" Support response time (public complaints) | 2 posts, both resolved within a day in-thread | 5 posts, no visible public resolution in-thread | 3 posts, one resolved within hours | low on Housecall Pro, small sample Churn signal: one post explicitly stating a switch from Jobber to us, citing the scheduling-sync crash as the reason — worth flagging to product as a real, if single, data point. Actionable gap: Housecall Pro's fast public support replies are getting visibly praised in-thread; we have no equivalent public-reply pattern to compare against. What this can't tell you: whether mobile stability actually outranks the two features ahead of it in the backlog — that requires usage and revenue-impact data this scan doesn't have access to.

Verified against

Grok Grok 4.1 · 2026-07-27

Changelog

  • 2026-07-27 Initial publish, verified against Grok 4.1 with live X mention search.

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