Star ratings tell you nothing. AI asks follow-up questions on low scores, routes critical feedback before it becomes a public review, and surfaces patterns across all responses. Built with conversational forms. Try it free.
A customer rates you 3 out of 5. What does that mean? You don't know. Was it the price? The service? The parking?
A standard survey gives you a number and maybe a one-line comment box that says "it was fine." You can't act on "it was fine." You need to know WHAT was fine and WHAT wasn't.
That requires follow-up questions. Surveys don't ask follow-ups.
Jake manages a coworking space with 120 members. After each month, members get an ioZen feedback link. Most are quick and positive. But when someone rates the WiFi 2 out of 5, the bot digs in: "What's been happening with the connection?" Submission Intelligence catches the negative sentiment paired with high urgency and flags it Critical. That response lands on Jake's ops board instantly, while a routine "love the coffee" comment goes to the backlog. He fixes the router before the member even thinks about leaving.
AI digs deeper when ratings are low
IntakeBot
How would you rate your overall experience this month? (1-5)
3 out of 5. It's been okay but not great.
IntakeBot
Thanks for being honest. What's been the biggest issue for you lately?
The WiFi keeps dropping during video calls. It's happened three times this week.
IntakeBot
That's frustrating, especially during calls. Is this happening in a specific area of the building?
Mostly in the second floor meeting rooms. The main floor is fine.
IntakeBot
Got it, we'll flag this for the ops team right away. Anything else you'd like to mention?
What changes when feedback is a conversation
Response rate
Actionable responses
Issues caught early
When someone gives a low rating, the AI doesn't just accept it and move on. It asks "What's been the biggest issue for you?" When they say "it's been okay," the AI probes: "What would make it excellent?"
This is Socratic questioning, not a chatbot. The AI follows up until there's something specific enough to act on. A 3-star rating with "it was fine" becomes "the WiFi drops during video calls on the second floor, mostly between 2pm and 4pm."
That's AI Field Intelligence applied to feedback. The AI doesn't accept vague answers. It gets you the details you need to fix what's actually wrong.
When someone reports a known issue, show them "We've seen this and here's what we're doing about it" right in the conversation. They know they've been heard. That alone changes how they feel about your business.
When someone gives a high rating, show them a link to leave a public review. The timing matters. They just told you they're happy. Ask while that feeling is real.
The conversation isn't just collecting data. It's managing the relationship.
From feedback link to action item
Customer responds
Clicks feedback link
Intelligence scored
AI assigns tier: Critical / Notable / Routine
Record created
Full response structured
Card on board
Sorted by priority
Routed by score
Low scores go to manager
Customer responds
Clicks feedback link
Intelligence scored
AI assigns tier: Critical / Notable / Routine
Record created
Full response structured
Card on board
Sorted by priority
Routed by score
Low scores go to manager
Every response routes to the right outcome. See how workflow routing works.
80% of unhappy customers never complain. They just leave. And post a bad review. Sentiment routing gives you a chance to save them before it's public.
Negative feedback lands here. Act fast, save the customer.
WiFi issues on 2nd floor
Rating: 2/5 · Jake M.
CriticalParking too expensive
Rating: 1/5 · Tom R.
CriticalWiFi issues on 2nd floor
Rating: 2/5 · Jake M.
CriticalSlow service complaint
Rating: 2/5 · Lisa K.
NotableWiFi issues on 2nd floor
Rating: 2/5 · Jake M.
CriticalParking too expensive
Rating: 1/5 · Tom R.
CriticalWiFi issues on 2nd floor
Rating: 2/5 · Jake M.
CriticalSlow service complaint
Rating: 2/5 · Lisa K.
Notable360 reviews, quarterly satisfaction surveys, onboarding feedback. Same conversational approach, same AI follow-ups, same routing to the right manager.
The only difference is the audience. An employee who writes "management could be better" gets the same follow-up probing as a customer who writes "service was okay." The AI asks what specifically could improve, when they noticed the issue, and what a good outcome looks like. You get answers you can act on instead of vague complaints that sit in a report.
When someone reports a problem, see their full history: previous feedback, purchases, support tickets. Context changes how you respond.
A first-time complaint from a loyal customer of three years is different from the same complaint from someone who signed up last week. With Contacts, every piece of feedback is linked to a customer profile. You see the full picture, not just a single data point.
Low scores trigger different follow-up questions. A 2-star gets "What went wrong?" A 5-star gets "Would you leave us a review?"
When answers are vague, the AI probes until there's something specific enough to act on.
Critical feedback goes to the manager immediately. You call before they post a review.
Happy customers are asked for a public review right in the conversation, while the positive feeling is fresh.
AI scores sentiment, urgency, and business impact. Configure your own scoring dimensions to match what matters to you.
Every response tied to a customer profile. See their history, previous feedback, and account status in one place.
