Recenzi / Insights for multi-location operators
Recenzi pulls every signal from every Google Business Profile you run — reviews, posts, keywords, photos, impressions, calls — and shows you what actually moves the number. It’s an insight engine, not another review dashboard. You make the call. We make sure you’re calling it on the data.
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What Recenzi answers
Every multi-location operator has theories about what works. Recenzi turns the theories into evidence — by correlating every signal across every location and telling you what the data actually says.
Do the locations with the most reviews actually get the most calls?
Recenzi correlates review volume to tap-to-call, direction requests, and website clicks across your portfolio. Sometimes the answer is yes. Sometimes it’s “only above 4.6 stars.” Sometimes geography swamps everything. We tell you which.
Does posting move impressions — or does it just feel productive?
We score lift by post intent, day of week, format, and image presence — with a confidence interval so you’re not staring at noise. If your 47 “tip Tuesday” posts did nothing, we’ll say so.
Why does one location rank for “water damage” and another for “carpet cleaning”?
Recenzi cross-references the profile, the posts, the photos, and the geography to show you which lever moved the keyword. So you can feed paid search, brief SEO content, and understand who your customer actually is in each market.
Which staff member is actually driving repeat business?
We extract every staff name mentioned in your reviews, score them by your rubric, and — with optional CRM tie-in — connect those mentions back to booked jobs and revenue. Your spreadsheet, but auditable.
Does a 1-star review tank next week’s calls — or do customers not care?
Recenzi watches what happens to calls, impressions, and direction requests in the 7, 14, and 30 days after a negative review lands. Per location. Across the portfolio. Sometimes the dip is real. Sometimes it isn’t.
Which of your locations is quietly slipping — and on what?
Corporate gets a bottom-10 view with the specific dimension each location is failing on: post cadence, response SLA, photo freshness, NAP drift, or all of the above. So the conversation stops being about the franchise average and starts being about the locations dragging it down.
How it works
Recenzi is built around six workspaces. Each one collects a signal, surfaces a correlation, and points to an action. Here’s a single trio from each.
Feature
Every review across every location, tagged by sentiment, theme, language, and staff mention. Filter, search, drill, and watch trends move per location.
Insight
Which themes are pulling sentiment up or down at which locations — and whether that movement is showing up in calls.
In practice
Your Tulsa location slipped from 4.7 to 4.4 over six weeks. Nine of fourteen new 3-star reviews mention “wait time.” Tap-to-call to that location dropped 22% in the same window. Your Wichita location, same theme, lost 11% of calls. Two markets, same diagnosis, one fix.
Feature
Detects every staff name mentioned in review text (English + Spanish), scores them against your rubric, and outputs a bonus-ready report. Configurable scoring per mention type.
Insight
Who is actually generating named, repeat, high-rating mentions — and what that mention is worth in your incentive system.
In practice
At one ServPro location, “Mike” shows up in 32% of 5-star reviews across the last four months. Your spreadsheet had him at 18% because nobody had time to comb the archive. Recenzi backfilled the missing mentions and flagged $475 in bonus payouts you’d under-counted last quarter.
Feature
Every post you publish, tagged by intent, day, format, image, and CTA. Auto-localize and bulk-publish across your whole portfolio in one pass. We track what each post did after it went up.
Insight
Whether posts move impressions, clicks, or calls — and which attributes do it. With sample sizes large enough to act on.
In practice
Across 12 locations and 47 “tip Tuesday” posts last quarter, direction requests rose 14% in the 72 hours after publish (95% confidence). The 11 “urgency” posts moved nothing. The 6 before/after image posts drove 28% more website clicks per location. So next quarter you write fewer, better posts.
Feature
Every query bringing impressions to every location. Branded vs. non-branded toggle. Rising, declining, and brand-new keywords per month. Excluded-term list per profile.
Insight
Why one location ranks for one cluster of terms and another ranks for a different cluster — and whether the gap is profile, posts, photos, or geography.
In practice
Phoenix appears for “flood cleanup near me” 4,200 times/month. Tucson, identical services and a comparable service area, appears 380. Recenzi cross-references both profiles: Phoenix posts three times a month with “water,” “flood,” and “damage” in the copy. Tucson posts about staff anniversaries. The data shows you the gap — and gives you a keyword list to brief your SEO writer and your paid search team.
Feature
Per-location tap-to-call tracking via CallRail, Twilio, or CallTrackingMetrics. Optional CRM tie-in (ServiceTitan, Jobber, HouseCallPro) closes the loop from tap → answered → booked → dollars.
Insight
Which signal triggered each call — a review, a post, a keyword, a photo view — and how many of those calls actually become revenue.
In practice
Your Atlanta location got 218 tap-to-calls last month. Your team answered 142. Fifty-one became booked jobs. The Tuesday-May-6 peak came 14 hours after a 5-star review named Maria by name. Recenzi connects the dots; without it you’d be looking at three separate dashboards and guessing.
Feature
Monitors every signal at every location, flags shifts, diagnoses the cause, and drafts the response. Corporate gets a bottom-10 view with the specific failure beside each location.
Insight
Not “calls dropped 28%.” Why — and what to do next.
In practice
Memorial Day weekend, calls at your Indianapolis location dropped 38%. Recenzi flagged a 1-star review three days earlier that called out a busy dispatch line — and pointed to a post cadence gap (zero posts in 18 days) that lined up with a 17% impression drop on “water damage near me.” The recommended action wasn’t another post. It was a staffing conversation.
Why this exists
“At our ServPro franchise, we were tracking review mentions by staff name in a spreadsheet to figure out monthly bonuses — and squinting at Google’s native insights to guess what was actually driving calls. No tool tied any of it together. So we built one.”
— Jennifer Halstead, co-founder · 15 years in marketing, current franchise marketing operator
Founding Partner Cohort
Same product. Half the price on both the platform fee ($19 instead of $39) and the per-location data line ($5 instead of $9). In exchange, give us monthly feedback and a case study when you’re happy. After 12 months your founding rate is locked in for as long as you stay with us.
