Review Predictions: Acting Before Reviews Post
Written By Chad McGuire (Sparrow Intel)
Overview
Most review-management work happens after the review posts. By then the public outcome is already decided — you can only respond to it.
Predictions invert that. Chirp AI estimates what every completed stay is likely to be reviewed as, at checkout — before the guest has typed a word publicly. The window between "guest checked out" and "guest posts a public review" is often hours to days. Predictions give you that window to act.
This is the longest lesson in the Reputation track because Predictions are the highest-leverage feature in the platform and the one teams most consistently underuse on day one.
What a prediction is
When a guest checks out, Chirp AI looks at the entire stay — the conversation history, detected sentiment, action items, cleaning and maintenance issues, response times, the reservation context — and produces a prediction:
Predicted sentiment — positive / neutral / negative
Predicted issues — likely cleaning or maintenance complaints
Top likely positives — the things the guest is most likely to praise
Top likely negatives — the things the guest is most likely to complain about
This is generated automatically. You don't have to ask. It shows up in the Predictions inbox the moment a stay completes.
📌 Predictions are available on the Premium plan. If you don't see `Predictions` in the left navigation, it's not enabled on your account — reach out via in-product chat.
The Predictions inbox
Open the Predictions Inbox (or click Predictions in the left navigation). The layout mirrors the Reviews inbox you already know: a list of predictions in the main area, and a collapsible Filters panel on the right (toggle with the funnel icon).
Click a row (or the Analyze button) to open the prediction detail page — predicted sentiment with confidence, top positives, top negatives, detected issues, and a link to the originating Conversation thread and reservation. Filters stack the same way. "Predicted negative + last 7 days + property group X" is a normal triage view.
What's actually in a prediction
Open any prediction and you'll see a property summary down the left rail and the prediction itself on the right.
The left rail carries the at-a-glance context:
Property photo and ID — the listing the stay belongs to (e.g.
Property Nickname)Predicted Experience — a single label: Positive, Neutral, or Negative
Departure date — when the stay ends, so you know how much runway you have to act
The right pane is the prediction, broken into sections:
Positive Insights — the things the guest is likely to praise, pulled from what they actually said ("clear check-in instructions," "host was responsive and accommodating"). Typically a handful of bullets.
Negative Insights — the same structure for likely complaints, drawn from any friction noticed during the stay. Reads "None" when the conversation was clean.
Cleaning Issues — cleaning problems that surfaced in the conversation, listed separately so they route to the right team
Maintenance Issues — maintenance problems, same idea — these may or may not have been resolved by the time the prediction renders
Show Conversation — expands the original message thread that informed the prediction, so you can read the source rather than trust the summary
Predictions also carry Internal Discussion and Notes tabs alongside the main view, for team follow-up and internal conversation, including the ability to tag other Sparrow Intel users.
Predictions are grounded in what the guest actually said, not in generic stay scoring. A clean, short conversation with no complaints produces a Positive prediction with empty Negative, Cleaning, and Maintenance sections — exactly what Carol's stay shows. Three maintenance issues and a frustrated tone halfway through would surface in those sections instead — and give you something to act on before checkout.
What to do with a negative-sentiment prediction
This is the core workflow. A guest just checked out, the prediction is red, and the OTA review window is open.
A pattern that consistently works:
Open the linked Conversation — re-read what happened during the stay; remind yourself of specifics
Reach out personally — a non-templated message from your senior agent or manager: "We saw the trouble with X, we wanted to make sure it was resolved before you left."
Offer real recovery if warranted — a partial refund, a future-stay credit, a sincere apology
Wait — don't ask for the review. The point isn't to silence the negative review; it's to give the guest a reason not to leave one in the first place, or to leave a softer one
Teams running this loop consistently see negative-prediction stays convert to neutral or positive public reviews more often than not.
What to do with a positive-sentiment prediction
The temptation is to skip these — but they're the easiest review-volume wins you have.
Pair the positive prediction with Feedback Requests (previous lesson). A guest predicted to review positively, who gets a Feedback Request, who rates 5 stars privately, and who clicks through to leave a Google review — that's an entirely new review on a high-visibility surface that wouldn't have existed otherwise.
The pattern:
Prediction is green
Rule fires (see next lesson) sending a feedback request with a Google review link
Guest leaves the review
Done
Many teams treat positive predictions as a quiet "schedule the Google nudge" trigger and forget about them. That's the right move.
Rules on Predictions
Predictions, like reviews and conversations, can drive Rules. Common patterns:
Predicted negative → email a supervisor so the recovery touchpoint happens promptly
Predicted negative → create a task for the right department
Predicted negative → send a follow-up message via template to open a recovery channel
Predicted positive → send feedback request with public review link
We cover the rule mechanics for both Reviews and Predictions in the next lesson.
Reading prediction quality over time
Predictions point you in the right direction — they don't read minds. The model works from the guest conversation and nothing else, so it's sharpest when guests talk and softest when they don't. Expect the occasional miss when:
A stay was short and the guest barely messaged (not much for Chirp to read)
The guest masked frustration in conversation but vented in the review
A late-stay event (a missed checkout call, a billing dispute) happened after the conversation went quiet
This is exactly where Chirp AI Voice changes the math. Many of the misses above come down to one thing: the guest never put the problem in writing. A frustrated caller who phones about a broken AC instead of messaging is invisible to a text-only model. Chirp AI Voice — our always-on AI guest services agent — captures those conversations too, feeding what guests say out loud back into the same prediction engine. More of the real conversation reaches the model, and the predictions get sharper for it.
Sparrow Intel surfaces predictions alongside the actual review when it eventually posts. Over time you can see prediction accuracy per property and per OTA. Use this to calibrate trust:
A property where predictions consistently match actual reviews → trust the prediction, act on negatives
A property where predictions miss frequently → treat each prediction as a flag, not a verdict; weight it lower in your workflow
This loop is how teams arrive at confident use of Predictions — not by trusting the model day one, but by checking it against reality and adjusting how much weight to put on the signal.
What about correctly-predicted negative reviews that still post?
Even when your recovery outreach works, some negative reviews still post. That's expected. Predictions aren't about preventing every bad review — they're about giving you a head start on the ones you can still influence. Catch a stay heading for 1-star, reach out, give the guest room to cool off, and the review often lands at 3-stars with real praise sitting next to the complaint. That's a win.
Track outcomes over time. Most teams find that often times negative predictions either don't convert into public reviews at all, or convert into a public review that's meaningfully softer than the prediction.
Up next
Rules for Reviews & Predictions — the engine that lets all of this run without you clicking each step.