higher response rates
Observed across participating beta customers’ own channel comparisons.
Miss Blue ResearchPreprint · August 22, 2026
iMessage vs SMS response ratesDo blue bubbles
get more replies?
A transparent look at Miss Blue beta-customer observations and the broader reported evidence on delivery, response, revenue, and cost per engagement.
First-party observations
What participating Miss Blue beta customers reported.
These figures compare those customers’ iMessage outreach with their SMS outreach. Revenue means revenue attributed by participating customers to the measured conversations.
more attributed revenue
Revenue attribution followed participating customers’ existing measurement practices.
Observational—not causal. These internal beta results have not been independently audited and do not guarantee an outcome. Audience, consent, timing, message, offer, sales coverage, and attribution method vary.
Multi-source synthesis
The published ranges point in the same direction. Their precision does not.
The Miss Blue preprint normalizes reported figures from benchmark compilations, vendor split tests, migration reports, practitioner observations, and a single longitudinal case. It reports ranges instead of pooling incompatible raw data.
Reported metriciMessageA2P SMSHow to read it
Delivery rate>94%≈68%
Reported benchmark range; SMS comparison assumes registered A2P traffic.
Open rate95–98%18–22%
Definitions differ materially across sources; SMS opens are commonly inferred.
Reply rate25–35%2–5%
Warm, consented, iOS-prevalent audiences represented in the reviewed reports.
Cost per send$0.02–$0.04$0.01–$0.015
Historical reported ranges, not Miss Blue plan pricing or a current carrier quote.
Illustrative benchmark model
Follow the losses through the funnel.
This model applies the preprint’s reported midpoints to 10,000 sends. It is an illustration of the published ranges—not a forecast, a randomized Miss Blue result, or a claim that every business should expect these counts.
9,400iMessage6,800SMS
9,212iMessage1,360SMS
2,764iMessage68SMS
Reported field comparisons
Hold the result beside the study design.
The comparison gets more credible when audience, creative, and timing stay constant. Missing sample sizes, uncontrolled pre/post periods, and vendor publication bias weaken several rows.
| Setting | Design | Sample | iMessage outcome | SMS outcome | Reported difference |
|---|---|---|---|---|---|
| Retail promotion | Same-offer split test | 10,000/channel | 97% opens | 19% opens | 5× reported open rate |
| B2B SaaS demo invites | Split test | Not reported | 31% replies; 12% booked | 3% replies; 0.8% booked | ≈10× reported replies |
| Auto appointment reminders | Dual-channel campaign | 5,000/channel | 1,410 confirmations | 102 confirmations | 14× reported confirmations |
| Migration cohort | Pre/post channel switch | 14 teams | Example: 19.5% replies | Example: 10.8% replies | +8–9 percentage points |
Methodology
A structured qualitative synthesis—not a pooled experiment.
The source studies do not expose shared raw microdata or common sampling frames. The report therefore preserves source-level definitions, uses ranges, and treats cross-source directional consistency as evidence rather than calculating a false pooled estimate.
- 01
Benchmark compilations
Vendor and industry analyses aggregating campaign telemetry provide the headline delivery, open, and reply ranges.
- 02
Vendor split tests
Reported comparisons that hold message or offer constant and vary the delivery channel provide the strongest causal clues in the corpus.
- 03
Migration and practitioner reports
Pre/post cohorts and operator observations add real-world context but often lack concurrent controls, raw samples, and complete definitions.
- 04
Miss Blue beta observations
Participating customers compared iMessage and SMS outcomes using their own audiences and attribution practices, producing the 80% response and 37% revenue observations.
Candidate mechanisms
Why might the channel change the answer?
The evidence identifies plausible contributors, but it does not isolate their individual causal weight.
01
Transport and filtering
iMessage uses Apple’s data network rather than carrier SMS infrastructure. The reviewed reports attribute part of the observed delivery gap to differences in filtering and routing.
02
Primary conversation placement
A native thread can be easier to notice and continue than business traffic presented in an unknown-sender or promotional context.
03
Familiarity and trust
Several sources propose that blue-bubble presentation acts as a familiarity heuristic. The synthesis treats this as a candidate mechanism—not a directly proven psychological cause.
04
Richer reply affordances
Read state, reactions, media, link previews, and an existing two-way thread may reduce the effort required to understand and answer a useful message.
Threats to validity
Read the caveats before the headline.
A useful research page should make the evidence easier to challenge, reproduce, and improve.
- Most quantitative inputs come from messaging vendors or practitioner reports with commercial incentives and incomplete sampling disclosure.
- The Miss Blue beta figures are observational customer comparisons. They are not a randomized experiment, universal benchmark, or promise of future performance.
- Open rate, reply rate, qualified response, delivery, and revenue attribution are not defined consistently across the source corpus.
- The evidence concerns warm, consented audiences that skew toward Apple devices. It does not support extrapolation to scraped, purchased, cold, or non-consented lists.
- Audience, offer, copy, timing, reply coverage, novelty, device mix, platform policy, and attribution rules can all change the observed effect.
Replicate it yourself
Run a controlled pilot before forecasting the lift.
The preprint recommends testing 100–500 warm, consented contacts before migrating a larger workflow.
Start with a sandbox or shared line- 01
Define one eligible cohort
Use contacts with the same relationship, consent standard, lead source, device eligibility, and measurement window.
- 02
Hold the workflow constant
Keep copy, offer, timing, follow-up count, routing, and human reply coverage as similar as operations allow.
- 03
Choose one primary outcome
Predefine unique reply rate, qualified reply rate, booking, resolution, or attributed revenue before inspecting results.
- 04
Report the denominator
Preserve eligible recipients, delivered messages, replies, conversions, opt-outs, complaints, missing data, and attribution rules.
Paper and source trail
Read the evidence behind the page.
The external links below are vendor-published sources, not independent validation. Their claims are summarized and challenged in the Miss Blue preprint.
Miss Blue preprintEight-page PDF · August 22, 2026
Tuco AI iMessage Benchmarks 2026Vendor-reported campaign benchmarks and methodology
Sendblue business messaging API comparisonVendor comparison of iMessage, RCS, SMS, and WhatsApp
VoidFix iMessage business communication guideVendor discussion of channel characteristics and use cases
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