AI Survey Phone Agent

The AI survey phone agent is a pre-built Sawy template that conducts phone surveys and collects structured feedback at scale. It handles customer satisfaction surveys, Net Promoter Score calls, post-visit feedback, market research interviews, and event follow-ups — reaching respondents by phone where completion rates are 3–5x higher than email surveys.

Email surveys get a 5–15% response rate. Phone surveys conducted by AI consistently achieve 30–50% completion rates because the conversation feels personal and takes less effort than clicking through a form. This template turns feedback collection from a data gap into a reliable data stream.

Template vs. building from scratch

| Decision factor | Use this template | Build custom | |---|---|---| | You match the role profile cleanly | Use template | — | | Your call mix is unusual or vertical-specific | Partial | Lean toward custom | | You need to launch in days, not weeks | Use template | — | | You have engineering bandwidth for tuning | Either | Custom may pay off | | You're testing the concept | Use template | Don't over-invest |

What This Template Does

The AI calls your contact list, conducts a natural survey conversation, and structures the responses for analysis — all without a human interviewer (see outbound calling for the broader pattern).

Sample Conversation

AI: "Hi, this is Taylor calling from Greenfield Medical. You visited our clinic last week, and we'd love to get your feedback. Do you have about two minutes?"

Caller: "Sure, go ahead."

AI: "Great. On a scale of 0 to 10, how likely are you to recommend Greenfield Medical to a friend or family member?"

Caller: "I'd say an 8."

AI: "Thanks — that's great to hear. What's the main reason you gave us an 8?"

Caller: "The doctor was excellent, but I waited about 40 minutes past my appointment time."

AI: "I appreciate that feedback. On a scale of 1 to 5, how satisfied were you with the check-in process?"

Caller: "3. The paperwork was confusing."

AI: "Thank you for sharing that. Last question — is there anything specific we could improve for your next visit?"

Caller: "Shorter wait times and maybe digital check-in."

AI: "Those are great suggestions. Thank you for your time — your feedback helps us improve. Have a great day!"

NPS score captured, open-ended feedback recorded, actionable insights logged — in under two minutes.

Key Features

Integrations Included

• Google Sheets / Airtable — response data export for analysis
• Salesforce / HubSpot — survey results linked to contact records
• SurveyMonkey / Typeform — sync with existing survey platforms
• Slack — alerts for low NPS scores or flagged negative feedback
• Zapier / Make — connect to any analytics, CRM, or notification tool
• SMS — pre-survey notification texts to improve answer rates

Setup in 3 Steps

1. Select the template — Choose Templates → Survey Caller from your Sawy dashboard. This loads survey conversation flows, rating scales, and branching logic.
2. Customize your survey — Choose a preset (NPS, CSAT, post-visit) or build a custom survey. Add your questions, define branching rules, upload your contact list, and set the calling schedule.
3. Go live — Launch a test campaign to a small group. Review transcripts, check response quality, and adjust questions before scaling to your full list.

Who It's For

• Healthcare providers collecting post-visit patient satisfaction feedback
• Hospitality businesses measuring guest experience after stays
• Retail brands gathering post-purchase and return experience data
• B2B companies running customer health and satisfaction check-ins
• Market research firms conducting phone-based interview studies

How does branching logic work?

If a respondent gives a low NPS score, the AI asks a follow-up like "What could we improve?" If they give a high score, it asks "What did we do well?" You define the branch rules based on score ranges.

Can I use custom rating scales?

Yes. The template supports standard 0–10, 1–5, and yes/no scales, plus fully custom scales. You define the wording and range for each question.

How are results reported?

Responses are structured and exported in real time to Google Sheets, Airtable, your CRM, or any connected tool. The dashboard shows aggregate scores, response rates, and trending open-ended themes.

A Second Sample Call — Detractor Recovery on a Negative NPS

A different and more important survey shape: a customer who gives a low NPS score, where the right move is structured listening and a clean handoff — not defending the brand.

AI: "Hi, this is Taylor calling from Greenfield Medical. You visited our clinic last week, and we'd love your feedback. Do you have about two minutes?"

Caller: "Honestly, no, but go ahead."

AI: "I appreciate it — I'll keep it short. On a scale of 0 to 10, how likely would you be to recommend Greenfield to a friend or family member?"

Caller: "Two. Maybe a one."

AI: "That's significant feedback — thank you for being direct. Without you having to relive the whole visit, can you tell me the single thing that drove the score that low?"

Caller: "Your front desk person was openly rude about my insurance. Like, audibly annoyed when I asked a question about coverage. In front of other patients."

AI: "I'm sorry that happened. That's not how anyone should be treated. I'm not going to ask you to explain more on a call — I'm going to do two things instead. First, I'll flag this directly to Dr. Roberts and the office manager today, with your name and the visit date if you're okay with that. Second, if you'd like a callback from the practice manager personally, I can schedule that for whenever's convenient. Would either of those help?"

Caller: "A callback would actually mean something. Tomorrow afternoon works."

AI: "I've scheduled it for tomorrow at 2 p.m. — the practice manager, Joanne, will call you directly. I'm also tagging this as urgent in our system so it's not just an aggregate data point. Thank you again — I know this took energy you didn't have to spend."

The AI didn't argue, didn't make the caller repeat the story, didn't push for a higher rating, and didn't end with "is there anything else?" It captured the single root-cause comment, offered two concrete next steps, and made one of them a real human callback — not another survey. Slack alert fires to the practice manager immediately; the NPS row in Airtable is tagged "critical recovery" rather than just sitting in the dashboard.

Survey-Caller-Specific Tuning Tips

Three tuning decisions that matter for outbound feedback collection more than for inbound agents:

1. Detractor handling is a separate workflow from promoter handling, and matters more. Most teams configure one survey flow and let it run. The high-leverage move is a branching path: if the respondent gives a score below 6 (NPS) or below 3 (CSAT), the agent stops asking quantitative follow-ups and shifts to one open-ended question plus an offer of human callback. Detractor recovery is worth 10x more than promoter affirmation, and the existing flow treats them the same.
2. Cut the survey when you have the answer, don't grind through all questions. A survey configured with eight questions does not need to ask all eight if the respondent has clearly answered the underlying question in the first two ("the doctor was great, the wait killed me"). Configure stop-criteria — once you've captured a directional rating plus one open-ended root cause, end the call. Completion rate goes up; respondent fatigue and survey fatigue across the customer base go down.
3. Calling windows are regulated; the TCPA matters even for non-sales surveys. Configure default calling windows of 9 a.m. to 8 p.m. in the respondent's local time zone, and respect any internal do-not-call list with the same rigor as a sales outbound. Survey calls that violate TCPA or hit prior opt-outs poison the entire feedback program — and in healthcare and financial services, can trigger regulatory complaints separate from the marketing-call rules most teams know about.

When Not to Use This Template

This template is wrong for sentiment monitoring that should be passive (post-purchase email, in-app NPS surveys, or a CSAT thumbs-up after support tickets — those don't need outbound calls). It's also wrong for compliance-driven surveys that legally require a human surveyor or written response (some clinical research, certain HR investigations, regulated patient outcome studies). Finally, don't use it for sales-disguised-as-survey calls — if the real intent is to surface a buying signal and route to a rep, use the Lead Qualifier template and be transparent about the call's purpose.