Rating distribution
Star breakdown (1–5) per location with delta vs. last period.
Insights
63 signals from your data.
Every insight below is grouped under the question it answers. Some are live today, some are in the build queue — every one of them ships included on every plan. No upsells, no “contact us” add-ons, no roadmap math.
Reviews & Sentiment
What are customers actually saying — and is it moving the number?
Every review across every location, decomposed by sentiment, theme, language, and named staff. Plus the connection between those themes and the KPIs you care about.
Review volume trend
New reviews per day, week, month — across all locations.
Average rating trend
Rolling average rating per location with anomaly detection.
Sentiment trend
Positive / neutral / negative reviews plotted over time per location.
Theme extraction
What customers actually talk about: communication, service, wait time, pricing, technician quality.
Negative-review → call impact
What happens to calls and impressions in the 7, 14, and 30 days after a 1- or 2-star review at this location.
AI review summary
Plain-English summary of what changed in the last 7, 30, or 90 days per location.
Spanish-language sentiment
Reviews in Spanish detected, translated, and scored.
Unanswered review queue
Sorted by age and severity — never miss a negative review again.
Response-time SLA tracker
How fast each location replies, vs. corporate target.
AI reply drafting
Draft replies in your brand voice. Owner approves or edits before sending.
Response quality scoring
Score each reply on empathy, specificity, and brand alignment. Suggest rewrites where useful.
Staff Attribution
Who’s actually earning the reviews — and what should they get paid?
The category-first feature nobody else does. Every staff name mentioned in a review, scored against your rubric, and tied (optionally) to revenue.
Named-staff extraction
Automatically detect every staff name mentioned in review text.
Top staff leaderboard per location
Who got called out most this period, with sentiment weighting.
Configurable scoring rubric
Set $ values per mention type (5-star named, 4-star named, negative mention, etc.).
Monthly bonus calculation
Auto-tally points and dollars per staff per location per month.
Payroll-ready CSV export
Export the bonus report as a CSV your payroll system or finance lead can ingest directly.
Corporate staff leaderboard
Top-performing staff across the entire franchise, not just one location.
Mention-to-call correlation
Whether days following a named mention see higher tap-to-call at that location.
Backfill mode
Re-score 12 months of historical reviews on first connect, so you don’t lose the past quarter.
Keywords & Discovery
Why does this location rank for what it ranks for — and what feeds the next campaign?
Every query bringing impressions to every profile, what’s changing, and what to do about it — for paid, for SEO, and for customer-vocabulary research.
How people find you
Top search queries driving impressions to each location.
Branded vs. non-branded share
Hide branded queries to see only the search terms that found you on intent.
Rising keywords
Queries gaining share month-over-month.
Declining keywords
Queries losing impressions before it shows up in calls.
New keywords this month
Brand-new queries finding you — surface emerging demand.
Monthly evolution table
Every keyword, every month, in one searchable view.
Keyword-to-call correlation
Which queries actually drive tap-to-call — not just impressions.
Cross-location keyword compare
Same keyword, two locations, side by side: why one ranks and the other doesn’t.
Profile / post / photo diagnosis
When two comparable locations rank differently, cross-references what’s different in their copy, posts, and photos.
Paid search feed
Export your top non-branded queries directly as a paid search keyword list.
SEO content brief generator
Turn a rising keyword cluster into a brief your writer can run with.
Excluded keywords
Hide queries you don’t care about (branded, off-topic, your own name spelled wrong).
Posts & Content
Are posts doing anything — and which ones?
Every post tagged, measured against a matched baseline, and reported with sample sizes that matter. No 3.3x lift on 7 posts.
Best day to post
Statistically meaningful lift by day of week — with confidence interval.
Best post intent
Educational / transactional / urgency / showcase. We tell you which works at which location.
Action lift by attribute
Performance breakdown by intent, tone, format, day, and image presence.
Lift duration
How long the post effect lasts: 24h, 72h, 7 days. So you can time the next one.
Image vs. text-only lift
Whether photos in your posts actually outperform plain text per location.
Causal lift with confidence intervals
Every reported lift number comes with a confidence indicator. Below-threshold claims are hidden, not faked.
Matched-pair lift
“Locations that posted X saw 18% more calls vs. matched controls.”
Bulk publish with localization
Write once, publish across your whole portfolio with auto-localized copy per location.
Post idea suggestions
Suggested next post based on what’s working at similar locations.
Performance & Calls
How many taps become conversations — and how many become revenue?
The KPIs your owners actually care about — calls, clicks, directions — and the closure all the way to a booked job when CRM is connected.
Impressions trend
Views per location with WoW / MoM deltas.
Total actions
Calls, direction requests, website clicks rolled up and broken down.
Per-channel breakdown
Views vs. calls vs. directions vs. website plotted independently.
Photo views and impact
Which photos are getting seen, which are stale, what to upload next.
Tap-to-call tracking
Every GBP tap-to-call event captured per location, per day.
Call answered / abandoned
Tie each call to whether your team picked up — via CallRail / Twilio / CTM.
Call duration buckets
Quick hangups vs. real conversations vs. long calls — and which drive bookings.
Call-to-job conversion
Optional CRM integration: ServiceTitan, Jobber, HouseCallPro — close the loop to revenue.
Trigger attribution
For each tap-to-call peak, the most likely upstream cause: review, post, keyword, photo.
Anomalies & Prescriptions
What changed — and what should we do about it?
Anomaly detection that doesn’t just send an alert. Recenzi diagnoses the proximate cause and drafts the next two or three actions.
Anomaly detection
Rating drops, call drops, keyword loss, post cadence breakdowns, photo staleness.
Cross-reference diagnostics
When calls drop, we tell you what else changed: new negative review, post gap, keyword loss, photo report.
Recommended actions
2–3 prescribed actions per anomaly, with draft text where applicable.
Aging unanswered review alerts
Threshold alerts when a negative review sits unanswered past your SLA.
Daily / weekly digest
Email summary of anomalies + recommended actions, per owner or per corporate role.
Anomaly-to-revenue impact
Estimated calls (and, with CRM, dollars) lost while the anomaly is unresolved.
Cross-Location & Franchise
Which locations are quietly slipping — and on what?
For franchise corporate, the bottom-10 matters more than the average. Recenzi surfaces who’s slipping and where, dimension by dimension.
Location pulse
All locations side-by-side: rating, review count, actions, sentiment, top staff.
Compliance score per location
Post cadence, photo freshness, response SLA, profile completeness, NAP consistency.
Bottom-10 view (corporate)
Lowest-performing locations + the specific failure for each one.
Franchise median benchmark
Each location vs. the network median across every metric.
External market benchmark
Each location vs. nearest comparable profiles in the market, pulled from public GBP data.
Corporate vs. franchisee views
Role-based access: corporate sees the bottom-10; franchisee sees their own pulse.
Dimension-by-dimension ranking
Same 47 locations, ranked seven different ways. The “best” location isn’t the best on everything.
See it on your own data
Connect a profile. Get the insights back inside the hour.
We backfill twelve months of history on first connect, so the dashboard isn’t empty the first time you open it.