Review management for hospitality and multi-location teams
Review management that shows you the pattern, not just the review.
Obenan brings reviews from your locations into one queue, reads each one for sentiment and topics, drafts a reply in the guest’s language and turns the problems worth fixing into tasks with an owner. Your team decides what gets posted, or sets the rules that post for it.
Tell us about your locations. We reply to arrange a time.
One sample review, from arrival to an owned fixSample data
Reviews from your connected platforms land in one list you can filter by location, platform, rating and reply status.
GoogleToday, 08:122 of 5 stars
“The room was lovely, but check-in took forty minutes and nobody at the desk told us why.”
Guest review, City CentreNot repliedEmotion AI marks it positive, neutral or negative and tags the topics. Counted across locations, a pattern shows.
SentimentNegative
TopicsCheck-in waitRoom
Across your locations
“Check-in” in 14 negative mentions this month, at 3 locations
Obi drafts a reply in the reviewer’s language. Your team edits it, or writes its own, and posts it.
Reply drafted by ObiIn the reviewer’s language
“Thank you for staying with us, and I’m sorry about the wait at check-in. That is not the welcome we want to give. We are looking at how our front desk handles busy arrival times and hope to see you again soon.”
Draft. Nothing is posted until someone on your team presses Reply.
The review becomes a task with a due date and a status, so the cause gets fixed, not only answered.
Suggested in the AI Driven Action Plan
Add front desk cover at peak evening arrival times.
Task
Review front desk cover for evening arrivals
- Assigned to
- Front office lead
- Task status
- Open
- In progress
- Completed
Due in 7 days
What changes in your week
One queue, not a tab per platform
Reviews from your connected platforms and locations in one place, with filters for what still needs a reply.
Catch the pattern early
Topics that keep coming up, good and bad, are counted across locations, so a recurring problem shows early.
A named owner for the fix
Reviews that point to a real problem become tasks with a due date, and task rules can create them for low ratings.
Clear about who does what
AI reads, suggests and drafts. People decide what is posted, unless you set a rule that answers for you.
| Who | What it does | What the guest sees |
|---|---|---|
| Emotion AI | What it doesMarks each review positive, neutral or negative and tags its topics. | What the guest seesNothing. It informs your team. |
| AI Driven Action Plan | What it doesSuggests next steps from your latest reviews. | What the guest seesNothing. Your team decides what to do. |
| Obi reply draft | What it doesWrites an editable draft in the reviewer’s language. | What the guest seesNothing until someone presses Reply. |
| Your team | What it doesEdits and posts replies, assigns tasks and follows them to done. | What the guest seesThe reply you post. |
| Auto-reply rule | What it doesAnswers matching reviews by location, star rating and whether the review has text. | What the guest seesA reply posted straight to Google, with no extra approval. Test it first and pause it at any time. |
Posting from Obenan depends on the platform. Google replies post directly; for some other platforms the reply is saved as a draft for you to post there. Features depend on your plan and team permissions.
Emotion AI
Eleven ways to read what guests keep telling you
Emotion AI turns reviews into views your team can act on. Explore each one below with sample data.
Reviews and Average Rating Graph
Sample dataReview volume and average rating over time, as a graph or a table.
| Reviews | Average rating | |
|---|---|---|
| June | 112 | 4.3 |
| July | 128 | 4.2 |
| August | 121 | 4.1 |
| September | 146 | 4.2 |
Rating Variance
Sample dataHow each location’s average rating moved against the previous period.
| Average rating | Previous | Change | |
|---|---|---|---|
| City Centre | 3.9 | 4.3 | -9.3% |
| Harbourside | 4.1 | 4.2 | -2.4% |
| Old Town | 4.6 | 4.4 | +4.5% |
Online Reputation
Sample dataA satisfaction trend from 0 to 100, built from your reviews.
| Satisfaction | |
|---|---|
| June | 72 / 100 |
| July | 70 / 100 |
| August | 66 / 100 |
| September | 68 / 100 |
Platform Overview
Sample dataRating, review count and the share of reviews replied to, per platform.
| Platform | Rating | Reviews | Replied |
|---|---|---|---|
| 4.2 | 412 | 86% | |
| Tripadvisor | 4.0 | 138 | 41% |
| 4.5 | 57 | 12% |
Rating Distribution Over Time
Sample dataHow the mix of one to five star reviews changes from period to period.
| 1★ | 2★ | 3★ | 4★ | 5★ | Total | |
|---|---|---|---|---|---|---|
| June | 6 | 5 | 11 | 30 | 60 | 112 |
| July | 8 | 6 | 12 | 33 | 69 | 128 |
| August | 11 | 9 | 13 | 31 | 57 | 121 |
| September | 9 | 7 | 12 | 36 | 82 | 146 |
Reviews Rating Breakdown
Sample dataReviews per star rating for each location.
| 1★ | 2★ | 3★ | 4★ | 5★ | |
|---|---|---|---|---|---|
| City Centre | 14 | 9 | 15 | 38 | 70 |
| Harbourside | 9 | 7 | 12 | 35 | 68 |
| Old Town | 3 | 4 | 10 | 30 | 95 |
Replies Rating Breakdown
Sample dataReplies per star rating for each location, so unanswered low ratings stand out.
| 1★ | 2★ | 3★ | 4★ | 5★ | |
|---|---|---|---|---|---|
| City Centre | 9 | 7 | 12 | 30 | 51 |
| Harbourside | 4 | 4 | 9 | 28 | 50 |
| Old Town | 3 | 4 | 10 | 29 | 88 |
Review Velocity
Sample dataReviews this period against the previous one, for each location.
| This month | Last month | Change | Total | |
|---|---|---|---|---|
| City Centre | 58 | 44 | +31.8% | 1,240 |
| Harbourside | 41 | 43 | -4.7% | 980 |
| Old Town | 47 | 39 | +20.5% | 1,310 |
Keywords Performance Cloud
Sample dataThe words guests use and how often each comes up positive or negative. Open one to read the reviews behind it.
- Check-inPositive 6 · Neutral 3 · Negative 14
- BreakfastPositive 31 · Neutral 4 · Negative 3
- WelcomePositive 27 · Neutral 5 · Negative 2
- CleanlinessPositive 18 · Neutral 6 · Negative 5
- ParkingPositive 2 · Neutral 4 · Negative 9
Latest Impressions
Sample dataAn AI-written summary of what recent guests say, with the time it was last updated.
Guests praise the breakfast and the welcome from staff. Several recent reviews at City Centre and Harbourside mention long waits at evening check-in, and a few ask for clearer parking directions.
AI Driven Action Plan
Sample dataAI-written suggestions from your latest reviews. They are suggestions only: nothing changes until your team acts.
- 1Add front desk cover at peak evening arrival times.Suggested
- 2Send parking directions with the booking confirmation.Suggested
- 3Keep the breakfast offer that guests mention most often.Suggested
Updated today, 07:00
Emotion AI views depend on your plan and team permissions.
Rules for the replies you would write the same way every time
Set a rule per location or group and star rating, choose whether it covers reviews with or without text, and answer with Obi Intelligence or a template. Pick the tone, language and signature, generate a test response before you switch it on, and pause any rule when you need to.
Reports on a schedule
Send review and Emotion AI reports by email every day, week or month, as PDF or Excel, to the people who need them.
Auto-reply rule
Sample data- Applies to
- City Centre, Harbourside
- Rating
- 5 stars
- Review text
- Present
- Respond using
- Obi Intelligence
- Tone
- Friendly
- Language
- Reviewer’s language
See it with our team
Tell us how many locations you run and where your reviews come from. We will walk you through the queue, Emotion AI and reply rules, and answer what matters for your setup.
Sending the form is a request, not a booked meeting. We reply to arrange a time.