Giving Users a Voice: Avatar Generators for Presenting User Verbatim and Insights

Experiment

Nothing will ever beat talking to human beings. Not even AI. But, lately I’ve been exploring new ways to bring user voices into the room—especially when the room is full of stakeholders. And not just quotes on a slide. I mean really bringing users to life.

User Needs
Voice of the User
Alignment

What I tried…

Give real users a synthetic voice using their actual verbatim
I used direct user verbatim to generate synthetic HeyGen and Hedra avatars. Each quote was associated with a persona (e.g., “Power Buyer with Trade-in” or “Experienced Dealer Agent”).

Video caption.

The upside: why I’ll probably keep using it

Emotional resonance
Synthetic faces + voices transform cold research artifacts into something closer to a documentary. It’s harder to ignore a frustrated “user” who looks you in the eye.
Speed and control
Unlike real video editing (which is time-consuming, redacted, and often restricted by NDA), HeyGen let me compose polished, on-brand user expressions quickly.
Accessibility and consistency
Everyone hears the same tone, pacing, and clarity. That helps stakeholders focus on what is said, not how a participant stumbled through saying it.

The pitfalls: why it still gives me pause

The uncanny valley is real
Synthetic faces + voices transform cold research artifacts into something closer to a documentary. It’s harder to ignore a frustrated “user” who looks you in the eye.
Risks of oversimplification
A 15-second avatar clip can flatten a complex insight into a sound bite. And if you’re not careful, it starts to feel like you’re scripting users, not representing them.
Ethical ambiguity
Even with user consent and paraphrased language, there’s a weirdness to creating a “face” for someone who never appeared on camera. It’s respectful… but also performative. It’s a line I’m still defining.

User verbatim communicated using HeyGen

User verbatim communicated using Hedra

Archetype: Vehicle Buyer (with Trade-in)

Persona: Early Adopter / Frequent Trader
Name: Nick Patterson

Actual user verbatim communicated using HeyGen

Demographics

Age:

62

Occupation:

Personal Wealth Mgmt

Income:

$195K annually

Location:

Savannah, Georgia

Education:

Masters in Finance

Status:

Married

Vehicle Trading Behavior

Frequency:

Every 12-15 months

Motivation:

Enjoys latest tech, vanity

History:

3 vehicles in 3 years

Spends:

$85K – $125K

Prefers:

New and Pre-owned

Primary Frustrations

Trade-in/Purchase Coupling:
Forced to navigate complex dual flows simultaneously

Inaccurate Valuations:
Online estimates don’t match reality (part of 40% who research online, 10% who get actual offers gap)

Information Overload:
40-56 clicks through Carvana-style flows while trying to buy AND trade

Paperwork Burden:
Spends 2+ hours at dealerships on documentation

Memory Gaps:
Can’t recall specific trim details, options, maintenance history for trade-in

Primary Goals

Separation of Concerns:
Trade current vehicle on its merits, buy next vehicle on its merits

Accurate Trade Valuations:
Real-time, data-driven assessments based on actual vehicle condition

Purchase Efficiency:
Complete more transaction steps online, minimize dealership time

Information Transparency:
Access to complete vehicle history and predictive insights

Digital & Technology Profile

Early Adopter:
First to try new automotive apps and connected car features

Device Usage:
Heavy multi-device user (smartphone primary, desktop for research, tablet for comparisons)

Connected Vehicle Enthusiast:
Actively uses vehicle apps, OTA updates, remote features

AI Comfort Level:
High – regularly uses AI tools in work and personal life

Data Sharing Attitude:
Privacy-conscious but pragmatic – willing to share vehicle data for tangible value

Technology Adoption

Vehicle Data Collection:
Willing to share driving patterns, maintenance data, location for accurate valuations

AI-Driven Insights:
Wants predictive analytics on vehicle value, optimal trade timing

Automated Processes:
Interested in “smart car selling itself” concept

IoT Integration:
Uses connected features, comfortable with vehicle data APIs