AI BUSINESS INSIGHTS · GROWTH COACHING

Know exactly what to fix —
and what it’s worth.

An AI business coach that reads every review, call, and complaint across your business — then hands you prioritized action cards and goal-based coaching instead of another dashboard to decode.

Action cards, not raw charts
Trends across every channel
Goal-based AI coaching
AI business coach · this week
Average rating trend4.3 → 4.6 ★
Action card · high impact
“Wait time” complaints up 3× on Friday evenings. Add front-desk cover 5–8 pm — similar businesses gained +0.3★ in 60 days.
You asked the coach:
“What should I focus on this month?”
Coach:Reply rate is 100% — solid. Your biggest lever is review volume: turn on SMS requests for repeat customers…
THE PROBLEM

You have plenty of data. What you don’t have is time to read it.

Dashboards don’t make decisions

Charts tell you your rating dipped. They don’t tell you why, what to do about it, or what fixing it is worth. The analysis is left to you — at 11 pm.

Feedback lives in silos

A complaint on a call, a 2-star review, a private feedback form — the same problem, three places, never connected. Patterns hide in the gaps.

Small issues compound quietly

By the time a recurring problem is obvious in your rating, it’s cost you months of customers. The signal was there — buried in text nobody had time to read.

THE SOLUTION

Decisions, not dashboards

AI Business Insights connects the dots across everything Replora sees — reviews, call transcripts, private feedback, complaints — and turns them into a ranked to-do list for growth.

Action cards with expected impact

“Add front-desk cover Friday 5–8 pm — estimated +0.3★ in 60 days.” Specific, prioritized, and tied to the outcome you care about.

Trends across every channel

Rating trends, complaint themes, reply analytics, request conversion — one intelligence layer reading calls, reviews, and feedback together.

A coach you can talk to

Set a rating goal and ask anything: “Why did March dip?” “What should I focus on?” The coach answers from your own data, not generic advice.

HOW IT WORKS

Up and running in minutes

1
Signals flow in

Reviews, transcripts, feedback, and complaints arrive from across the platform — automatically.

2
AI finds the patterns

Themes, spikes, and correlations surface across channels and locations — including the ones you’d never spot.

3
Action cards arrive

Ranked by impact, each with a specific fix and the expected rating effect.

4
Track the payoff

Set goals, act on cards, and watch complaints fall and ratings climb — measured, not guessed.

SEE IT IN ACTION

From raw feedback to a rating gain

Step through how scattered signals become one clear, prioritized plan — and a measurable result.

Signals · collected automatically
Google reviews
Every star, every word
47 this month
Call transcripts
From AI Receptionist
132 calls
Private feedback
From Review Booster
11 submissions
Complaints
All sources, one inbox
13 logged
Pattern detected
Complaint theme frequency · 4 weeks
Wait time
×3
Booking issues
Parking
The AI cross-references timestamps: spikes cluster on Friday, 5–8 pm — across reviews, calls, and feedback alike.
Action cards · prioritized
HIGHAdd front-desk cover Fri 5–8 pm
Fixes the wait-time spike · est. +0.3★
MEDTurn on SMS review requests
Repeat customers convert 2× better
LOWAdd parking info to AI Receptionist FAQ
9 callers asked this month
Specific, ranked, and tied to expected rating impact
90 days later
Goal: reach 4.7★ by Q4On track
4.3 → 4.6 · wait-time complaints down 70%
↓70%
wait complaints
+38
reviews / mo
+0.3★
rating gain
WHY IT MATTERS

What your business gets

Hours of reading, done for you

Every review, transcript, and complaint analyzed — you get the conclusions.

Problems caught while they’re small

Recurring themes surface after 3 mentions, not after your rating drops.

Priorities backed by impact

Know which fix is worth +0.3★ and which is noise — spend effort where it pays.

Cross-channel truth

Calls, reviews, and feedback analyzed together — the full picture, not one channel’s bias.

Goal-based coaching

Set a target rating; the coach breaks it into weekly, achievable moves.

Multi-location clarity

See which locations lead, which lag, and exactly why — in one view.

INTEGRATIONS

Plays well with your tools

Gemini AI
Analysis & coaching
Google Business Profile
Review & rating data
Email
Daily reports & digests
See all integrations →
FAQ

AI Business Insights — common questions

How is this different from a normal analytics dashboard?

Dashboards show you numbers and leave the thinking to you. Insights does the thinking: it reads the actual text of reviews, calls, and complaints, finds patterns, and outputs ranked action cards with expected impact. You get a to-do list, not a chart to interpret.

Where does the data come from?

From the rest of the Replora platform: Google reviews via AI Responder, call transcripts via AI Receptionist, private feedback and interceptions via Review Booster, plus your complaint inbox. The more of the platform you use, the sharper the insights.

What exactly is an action card?

A single recommended action with its evidence and expected payoff — for example: “Wait-time complaints tripled on Friday evenings (8 mentions across reviews and calls). Add front-desk cover 5–8 pm. Businesses that fixed this pattern gained ~0.3★ within 60 days.”

Can I ask it questions directly?

Yes — the AI business coach is conversational. Ask “why did my rating dip in March?” or “what should I focus on this month?” and it answers from your own business data, with the evidence attached.

Does it work across multiple locations?

Yes. Trends and action cards can be viewed per location or across your whole portfolio — ideal for spotting why one branch outperforms another. Agency plans add centralized multi-client views.

Stop reading feedback. Start acting on it.

Let the AI coach read everything, rank what matters, and hand you the plan — starting this week.

Start free →View pricing
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AI Business Insights works even better with the rest of the platform

One connected platform — every product feeds the next.