Audio Opener vs Text Opener — Mentor Mitch

Did the voice-note opener improve conversion? · 11,437 conversations analyzed

Verdict — yes, the audio opener is a clear win on bookings

Opening with a voice note books ~3× more calls per conversation (6.1% vs 2.2%) and converts replying leads ~4× better (12% vs 3%). It trades some top-of-funnel reply volume (51% reply rate vs 75% for text) for far hotter leads — net strongly positive. Recommendation: keep it as the default; monitor inbound volume since the lower reply rate makes the funnel top slightly thinner.

Every Mitch agent conversation classified by the TYPE of the agent's first outbound message — AUDIO (voice note) vs TEXT. Reply rate = lead sent ≥1 message after the opener. Booked = call_booked custom property. Agent-handled convos only (agent_conversation=true). Observational comparison (not a controlled A/B) — the like-for-like and same-month April cuts control for time/seasonality.
6.1%
Booked / convo — Audio
2.2%
Booked / convo — Text
2.7×
Booking uplift
51% vs 75%
Reply rate — Audio vs Text

Head-to-head

CutOpenerConvosReply rate Booked / convoBooked / replied
All-time AUDIO3,22050.7% 6.1%12.0%
TEXT8,21774.8% 2.2%3.0%
Like-for-like
since first audio (2025-02-18)
AUDIO3,22050.7% 6.1%12.0%
TEXT7,94073.9% 2.1%2.8%
April 2026
same month, both at scale
AUDIO1,330 6.2%
TEXT2,797 3.5%

Monthly trend

Volume + booking rate by opener, 2026

Monthly volume + booking rate by opener, 2026. Audio scaled from March 2026 — by May the team had effectively migrated (audio is now the dominant opener). Note: the March text row is a high-volume / low-conversion outlier (a traffic spike with attribution lag) — read April onward for the stable signal.
MonthAudio convosAudio bookedAudio rate Text convosText bookedText rate
2026-01000.0%16995.3%
2026-02500.0%33872.1%
2026-034400.0%4,02790.2%
2026-041,330826.2%2,797993.5%
2026-051,8301116.1%278176.1%

Method & caveats

How this was measured and how to read it

What this is

An observational before/after-style cut, not a randomized A/B. Leads weren't randomly assigned audio vs text — so treat the magnitude as directional, not a precise lift estimate.

How the confound is controlled

Two cuts isolate the opener from time effects: (1) like-for-like — only conversations since the first audio opener; (2) April 2026 — both openers ran at high volume in the same month. Audio wins clearly in both, so the effect isn't seasonality.

Caveats

The March 2026 text row is a volume/attribution artifact and is excluded from the headline. Tiny 2025 audio samples (n=1–3) are noise and not used for conclusions. Reply rate being lower for audio is real and reported honestly — it's a quality filter, not a flaw.

Cross-check

Agus may have a parallel analysis — worth aligning definitions (opener = first agent message type; booked = call_booked) before circulating externally.