AI Voice for Market Research: How Australian Businesses Are Scaling Surveys with AI Calls
Phone surveys aren't dead. They're just too expensive to do the old way.
Market research firms have known for years that phone interviews produce richer data than online surveys. People give longer answers. They reveal nuances. The conversation goes places a Google Form never could.
But at $30-$50 per completed phone interview, most businesses can't afford to do them at scale. So they default to email surveys with 5% response rates and hope for the best.
AI voice call technology changes this equation. An AI agent can conduct a structured phone survey for a fraction of the cost of a human interviewer — while maintaining the conversational quality that makes phone research valuable in the first place.
Why Phone Research Still Matters
Quick answer: AI voice calls enable phone-based market research at 80-90% lower cost than human interviewers, while achieving 3-5x higher response rates than email or online surveys. AI agents can conduct hundreds of interviews simultaneously, with consistent methodology, real-time transcription, and structured data extraction — making phone research accessible to businesses that could never afford traditional methods.
The Response Rate Problem
| Method | Typical Response Rate | Cost Per Complete |
|---|---|---|
| Email survey | 5-10% | $2-$5 |
| Online panel | 15-25% | $5-$15 |
| SMS survey | 10-20% | $3-$8 |
| Human phone interview | 25-40% | $30-$50 |
| AI voice interview | 30-45% | $3-$8 |
Email surveys are cheap but shallow. You get low response rates and short, superficial answers. People click through without thinking.
Phone surveys get real responses. People engage in conversation. They explain their reasoning. They share stories. But at $30-$50 per completed interview, a 500-person study costs $15,000-$25,000 in interviewer fees alone.
AI voice call technology delivers phone-level engagement at email-survey pricing. That's the breakthrough.
What AI Phone Research Actually Sounds Like
Here's a real example of an AI conducting an NPS follow-up:
AI: "Hi Sarah, this is a quick call from [Business]. We recently helped you with [service]. I'd love to get your feedback — it'll only take about two minutes. Is now a good time?"
Sarah: "Sure, go ahead."
AI: "Great. On a scale of 0 to 10, how likely are you to recommend us to a friend or colleague?"
Sarah: "Hmm, probably a 7."
AI: "Thanks Sarah. A 7 — that's good to hear. What would we need to do to turn that into a 9 or 10?"
Sarah: "Honestly, the service itself was great. It was just the wait time to get started. I had to call three times before someone got back to me."
AI: "That's really helpful feedback. The wait time for the initial response — was that by phone, email, or both?"
Sarah: "Phone mostly. I kept getting voicemail."
AI: "Understood. I'll make sure that feedback gets to the right team. Is there anything else you'd like us to know?"
That's a conversation. Not a form. And the AI captured structured data (NPS score: 7, issue: response time, channel: phone) alongside rich qualitative context.
Use Cases for AI Voice Research
Net Promoter Score (NPS) Programs
NPS by phone consistently produces more actionable results than NPS by email:
- Higher response rates mean more representative data
- Follow-up questions reveal why people gave their score
- Detractors are more likely to explain their concerns verbally
- Promoters can be asked for testimonials or referrals
AI advantage: Run NPS calls continuously, not just quarterly. Call every customer after every interaction. Build a real-time NPS dashboard fed by live conversational data.
Customer Satisfaction (CSAT) Surveys
Post-service satisfaction calls are perfect for AI:
- Structured questions with room for open-ended follow-up
- High volume (every customer interaction can be surveyed)
- Consistent methodology (every call asks the same questions the same way)
- Real-time alerts for low scores
Product Development Research
Before building a new feature or product:
- Call existing customers to understand their needs
- Test concepts and get verbal reactions
- Gauge willingness to pay
- Identify use cases you hadn't considered
AI handles the volume; your product team analyses the insights.
Competitive Intelligence
"What other products did you consider before choosing us?" "What do they do that you wish we did?" "If you could change one thing about our product, what would it be?"
These questions work beautifully in conversation. They fall flat in a form.
Brand Perception Studies
AI voice agents can conduct brand awareness and perception research:
- Unaided recall ("Which brands come to mind when you think of [category]?")
- Aided recall ("Have you heard of [brand]?")
- Attribute association ("What three words would you use to describe [brand]?")
- Purchase intent ("How likely are you to consider [brand] next time you need [product]?")
Event and Experience Feedback
After conferences, workshops, or service appointments:
- "What was the most valuable part of the event?"
- "Was there anything that didn't meet your expectations?"
- "Would you attend again next year?"
Phone follow-up within 24-48 hours captures fresh, detailed feedback that post-event email surveys miss.
Employee Engagement Surveys
AI voice agents can conduct anonymous employee surveys:
- Higher engagement than email surveys (people actually answer their phone)
- More honest responses in conversation than in written form
- Follow-up probing reveals root causes
- Anonymous and confidential
How to Set Up AI Research Calls
Step 1: Define Your Research Objectives
What do you want to learn? Be specific:
- Bad: "Find out what customers think"
- Good: "Determine the top three factors influencing purchase decisions among customers who bought in the last 90 days"
Step 2: Design Your Questionnaire
Key principles for AI voice surveys:
Keep it short. 5-8 questions maximum. Respect people's time.
Start easy. Open with a simple question to build rapport before asking anything complex.
Mix question types:
- Scaled (1-10, very satisfied to very dissatisfied)
- Multiple choice (which of these applies to you?)
- Open-ended (what would you change?)
Build in follow-up logic:
- If NPS score < 7, ask "What would we need to improve?"
- If satisfaction = low, ask "What specifically went wrong?"
- If positive response, ask "Would you be willing to share a testimonial?"
Step 3: Configure Your AI Agent
Set up the survey agent:
- Tone: Warm, professional, conversational — not clinical
- Pacing: Allow pauses for thought. Don't rush respondents.
- Probing: Program the AI to ask one follow-up question on open-ended responses
- Recording: Get consent before recording (see our state-by-state guide)
Step 4: Sample and Schedule
Who to call:
- Your entire customer base (census approach)
- A random sample (statistical approach)
- A targeted segment (focused approach)
When to call:
- Business hours (Tuesday-Thursday, 10am-3pm works best in Australia)
- Within 24-48 hours of the interaction for CSAT
- Quarterly for tracking studies
Step 5: Analyse and Act
AI automatically delivers:
- Structured data (scores, choices, classifications)
- Full transcripts of every conversation
- Sentiment analysis of open-ended responses
- Statistical summaries and trends
- Flagged responses requiring human review
The DNCR Exemption for Market Research
Here's an important detail many businesses don't know: genuine market research calls are exempt from the Do Not Call Register.
The DNCR applies to telemarketing — calls that have a commercial purpose. Pure market research (where the purpose is to collect data, not to sell) is exempt, provided:
- The call is genuinely for research purposes
- No selling occurs during or after the call
- The results are used for research, not direct marketing
- The caller identifies themselves as conducting research
This means AI voice surveys have a broader reach than AI sales calls. You can contact people on the DNCR if your purpose is genuine research.
Caution: If your "research" call transitions into a sales pitch, you lose the exemption. ACMA takes a dim view of "sugging" (selling under the guise of research).
The Do Not Call Register: How to Scrub Your AI Dial Lists
AI vs. Traditional Research Methods
AI Voice vs. Human Interviewers
| Factor | Human Interviewer | AI Voice |
|---|---|---|
| Cost per interview | $30-$50 | $3-$8 |
| Interviews per day | 15-25 | Unlimited |
| Consistency | Variable | Perfect |
| Interviewer bias | Present | Absent |
| Availability | Business hours | 24/7 |
| Turnaround for 500 surveys | 4-6 weeks | 2-3 days |
| Deep probing ability | Strong | Good (improving) |
| Emotional sensitivity | High | Moderate |
AI Voice vs. Online Surveys
| Factor | Online Survey | AI Voice |
|---|---|---|
| Response rate | 5-10% | 30-45% |
| Data richness | Low (short text) | High (full conversation) |
| Respondent effort | Low (clicks) | Low (talking) |
| Demographics reached | Skews younger/tech-savvy | Broader (anyone with a phone) |
| Cost per response | $2-$5 | $3-$8 |
| Qualitative depth | Minimal | Significant |
AI Voice vs. Focus Groups
| Factor | Focus Groups | AI Voice |
|---|---|---|
| Cost per participant | $150-$300 | $3-$8 |
| Sample size | 6-12 per group | Hundreds or thousands |
| Group dynamics bias | High | None |
| Geographic reach | Limited to venue | Nationwide |
| Scheduling complexity | High | Automated |
| Statistical validity | Low (small n) | High (large n) |
Data Quality Considerations
Strengths of AI Voice Data
No interviewer bias: AI asks every question the same way, every time. No leading inflection, no unconscious reactions to answers, no fatigue effects.
Complete recording: Every word is captured and transcribed. No reliance on interviewer notes or memory.
Consistent probing: Follow-up questions are triggered by the same rules for every respondent. No variation in how deeply different respondents are probed.
Scale enables statistical power: When research costs $3 per interview instead of $40, you can afford sample sizes that produce statistically significant results.
Limitations to Watch For
Complex topic exploration: AI is less effective at the kind of deep, exploratory probing that skilled qualitative researchers do. It follows rules; it doesn't improvise.
Emotional nuance: AI detects sentiment but may miss subtle emotional cues that a human interviewer would pick up on.
Respondent self-selection: People who answer unknown numbers may differ from those who don't. This sampling bias exists for human phone research too, but it's worth acknowledging.
The AI disclosure question: Some respondents may answer differently knowing they're talking to AI. Evidence is mixed on whether this helps (less social desirability bias) or hurts (less engagement).
Industries Using AI Voice Research in Australia
Healthcare: Patient experience surveys, post-appointment feedback, medication adherence check-ins
Financial services: Customer satisfaction tracking, product feedback, churn risk identification
Retail: Post-purchase feedback, brand perception tracking, loyalty program research
Property: Tenant satisfaction surveys, buyer sentiment tracking, market condition research
Government: Citizen satisfaction surveys, policy feedback, service evaluation
Education: Student experience surveys, alumni engagement, course feedback
Getting Started with AI Voice Research
The barrier to entry is surprisingly low.
- Define your research question — what do you want to learn?
- Write 5-8 survey questions — start simple, include one open-ended question
- Build your AI agent on Voxworks using the survey template
- Upload your contact list — start with 50 calls as a pilot
- Review results — check transcripts, refine questions, scale up
Most businesses can have a pilot running within a day. Full-scale research programs within a week.
The cost of learning is tiny. The insights you gain are huge.
Ready to transform your market research? Start your free trial at voxworks.ai and run your first AI-powered survey today.

