Every study is
built on foundational research.
The methodology behind every SweetNote study. Anchored in 40 years of literature; built into one locked instrument.
Patent pending.
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See the full impactIllustrative demo campaign · Aligned with ARF / ESOMAR / MRC standards · Run for real in the Gift Lift Study →
Real scan rates range widely with the gift, the audience, and the occasion: a mailed card can land in the single digits, an in-person gift can reach most recipients.
The channels you have are getting quieter.
Three converging trends pushing brands toward measurement on physical-digital moments instead of digital impressions or surveys.
01
Digital attention is collapsing.
The channels brands have leaned on for a decade are getting quieter every quarter. Email open rates sit at ~21% on paper, but Apple’s Mail Privacy Protection inflated raw opens while the click-through and reply signals advertisers actually optimize for kept softening. Unaided display-ad recall sits in single digits across most categories. Spend keeps climbing while the read on whether anyone noticed gets harder to find, which is why scoping a gifting program against email or display benchmarks has become an increasingly fragile comparison.
Sources: Litmus Email Analytics benchmark data · IAB / Nielsen Display Effectiveness benchmarks · eMarketer Digital Ad Spend reports
02
Physical cuts through.
Physical mail can require materially less cognitive effort to process than digital equivalents and drive higher brand recall. A widely-cited Canada Post / True Impact neuromarketing study (2015) reported roughly 21% less effort and 70% higher recall. Direct-mail response rates run roughly 9% to house lists and 5% to prospect lists (ANA / DMA, 2018); email response is well under 1%. The format earns attention because it shows up where attention isn’t already saturated, which is why the US corporate-gifting market, valued at roughly $258B in 2022, is projected to surpass $312B by 2025 (Coresight / GiftNow). Brands have run out of channels that still cut through.
Sources: Canada Post / True Impact Marketing 2015 · ANA / DMA Response Rate Report 2018 · Coresight Research / GiftNow 2022 ($258B in 2022, projected $312B by 2025)
03
Stated preference doesn’t predict behavior.
The gap between what people say they’ll do and what they actually do is large, structural, and well-documented. A meta-analysis of 47 experiments finds that medium-to-large changes in stated intent produce only small-to-medium changes in behavior. Procurement teams running post-event surveys or stated-preference panels to predict gifting outcomes are measuring intent, not action. The format that closed the gap historically, observed behavior on a real moment of contact, is what SweetNote captures, which is why a gift moment + scan + watch tells you more than a follow-up survey ever will.
Sources: Webb & Sheeran, Psychological Bulletin 2006 · Carrigan & Attalla, Journal of Consumer Marketing 2001 · full citations in the four pillars below
What it adds up to: a behavioral-research platform that captures revealed-preference behavior on a channel that still commands attention, grounded in the literature below.
BBMR™ is the methodology built for the brand moment.
We coined Behavioral Brand Moment Research™, anchored in four settled traditions, for moments the academic literature didn’t have a name for.
We capture observed action: scan, watch, react, click. Recipients never have to predict their own behavior.
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Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
Foundational behavioral-economics work (the basis of Kahneman’s 2002 Nobel Prize) showing that people deviate from rational-choice models in systematic, predictable ways. Why it matters: what people say they’ll do is an unreliable guide to what they actually do.
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Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249–268.
Meta-analysis of 47 experiments: a medium-to-large change in intention (d = 0.66) produced only a small-to-medium change in behavior (d = 0.36). Why it matters: the behavior change is only about half the size of the intention change. What people say they’ll do systematically overstates what they do.
Further reading (2 more)
- Carrigan, M., & Attalla, A. (2001). The myth of the ethical consumer—do ethics matter in purchase behaviour? Journal of Consumer Marketing, 18(7), 560–578.
- Ariely, D. (2008). Predictably Irrational: The Hidden Forces That Shape Our Decisions. HarperCollins.
We measure on your recipient list. Every name is a customer, donor, or employee with a declared relationship. No rented panel, no third-party data.
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Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K., & Wetzels, M. (2015). Unraveling the personalization paradox. Journal of Retailing, 91(1), 34–49.
Personalization on knowingly provided data lifts engagement; covert-tracking personalization produces backlash. Why it matters: the brand-owned recipient list is the “knowingly provided” condition by construction.
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Sahni, N. S., Wheeler, S. C., & Chintagunta, P. (2018). Personalization in email marketing. Marketing Science, 37(2), 236–258.
Field experiment: even minimal personalization (a recipient’s name) produces statistically significant engagement lifts. Why it matters: our per-recipient name token in the greeting is a measurable lever, not a cosmetic flourish.
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Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546–562.
Personalization gets stronger when recipients have visible privacy controls. The two compound; they don’t trade off. Why it matters: the reveal makes its privacy controls visible: what’s counted is stated plainly, the opt-out is one tap, and Global Privacy Control is honored absolutely. That is the kind of control Tucker found amplifies effectiveness.
Further reading (7 more)
- Boerman, S. C., Kruikemeier, S., & Zuiderveen Borgesius, F. J. (2017). Online behavioral advertising: A literature review and research agenda. Journal of Advertising, 46(3), 363–376.
- Direct Marketing Association / ANA. Response Rate Reports. ana.net (annual).
- Vesanen, J. (2007). What is personalization? A conceptual framework. European Journal of Marketing, 41(5/6), 409–418.
- Baker, R., Brick, J. M., Bates, N. A., Battaglia, M., Couper, M. P., Dever, J. A., Gile, K. J., & Tourangeau, R. (2013). Summary report of the AAPOR task force on non-probability sampling. Journal of Survey Statistics and Methodology, 1(2), 90–143.
- Couper, M. P. (2000). Web surveys: A review of issues and approaches. Public Opinion Quarterly, 64(4), 464–494.
- Callegaro, M., Baker, R., Bethlehem, J., Goritz, A. S., Krosnick, J. A., & Lavrakas, P. J. (Eds.). (2014). Online Panel Research: A Data Quality Perspective. Wiley.
- Pew Research Center. Methods for measuring opinion in opt-in panels and probability-based panels. pewresearch.org/methods (ongoing).
We capture the moment a physical object arrives, when attention is concentrated, before the digital-attention-collapse problem applies.
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Krishna, A. (2012). An integrative review of sensory marketing. Journal of Consumer Psychology, 22(3), 332–351.
Two decades of sensory-marketing research: multisensory stimuli affect behavior differently than visual-only or digital. Why it matters: a physical-gift moment is a measurable event, not just a sentimental one.
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Peck, J., & Childers, T. L. (2003). To have and to hold: The influence of haptic information on product judgments. Journal of Marketing, 67(2), 35–48.
Haptic experience of holding a physical product changes consumer judgments distinct from any visual or descriptive information. Why it matters: tactile arrival is a cognitively distinct event from digital-only touchpoints.
Further reading (2 more)
- Spence, C. (2011). Crossmodal correspondences: A tutorial review. Attention, Perception, & Psychophysics, 73(4), 971–995.
- Millward Brown & Bangor University, Centre for Experimental Consumer Psychology. (2009). Using neuroscience to understand the role of direct mail. Commissioned fMRI study for Royal Mail.
Locked instrument, disclosed methodology, aggregate-only output, aligned with ARF, ESOMAR, MRC standards.
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Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5), 557–570.
The canonical privacy model: suppress any data cell too small to hide the individuals inside it. Why it matters: our k ≥ 10-other-brands (leave-one-out) / n ≥ 25 floors are exactly these suppression thresholds, applied at every dashboard, report, and export we publish, so the output we produce never exposes one recipient.
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Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514.
Synthesis of how transparent data handling shapes both trust and measurement validity. Why it matters: the disclosed-methodology, locked-instrument architecture isn’t a procurement nicety. It’s what makes the signal trustworthy.
Further reading (2 more)
- Dwork, C. (2006). Differential privacy. Automata, Languages and Programming (ICALP 2006), Springer LNCS 4052, 1–12.
- Solove, D. J. (2008). Understanding Privacy. Harvard University Press.
Sources are illustrative, not exhaustive. The academic literature in each pillar continues to develop. If you’d like a deeper bibliography for a procurement review, we can send one tailored to your category.
Rigor you can act on.
A signal you can trust
Observed action on a moment you control (scan, watch, react, click), not survey intent or digital guesswork.
Behavioral + MomentComparable, every time
Because the instrument is locked, results compare across your own campaigns today and, once your comparator — the network excluding you — holds 10 other contributing brands, against a cross-brand benchmark no single brand could build alone.
ResearchYours, and procurement-clean
First-party recipients, aggregate-only output, no biometrics. An owned channel and a research instrument in one product.
BrandAnd when you need the dollar answer, the optional outcome layer below connects all of it to revenue, without us ever seeing a single transaction.
See how the outcome layer worksConnecting gifts to outcomes, without seeing per-recipient transactions.
Brands send conversion events in; we report aggregates only. Individual transactions never land in SweetNote’s output.
- Brand processor Your payment or CRM system, sent through the API or a CSV upload Brand-side
- POST /v1/conversions Your handler relays the conversion event Boundary
- Matched to your study Conversion to cohort or variation match 120-day ceiling
- 30-day window Causal verdict, Wilson confidence Causal
- 31–90 day tail Extended capture, descriptive context Descriptive
- Aggregated readout Per cohort or variation, no per-recipient data k ≥ 10 other · n ≥ 25
The dollar verdict is causal for randomized studies (A/B, Gift A/B, Gift Lift) and an associative floor for Snapshot and Cohort, which observe rather than randomize.
We follow the standards that define research-grade measurement.
Behavioral research isn’t a regulated industry. We still hold the methodology to the disclosure and measurement standards that define credible research, set by the four institutions below.
Standards we follow. Not accreditations. None of these bodies has certified, audited or endorsed SweetNote.
Cross-platform measurement standards. We follow ARF’s disclosure conventions for sample frames, sample sizes, and methodology stamps.
International code of conduct on consent, transparency, and methodology disclosure for research touching individual respondents.
Minimum standards for media rating research. Our reveal-page video and reaction signal are informed by MRC’s viewability and counted-engagement principles.
Non-probability sampling task-force guidance. Our brand-owned recipient frame is a declared closed population, described using the terminology of AAPOR’s non-probability-sampling task-force guidance.