Shared research study link

McDonald's - Weekly Sentiment Tracker

Consumer sentiment evaluation for McDonald's

Study Overview Updated Jan 11, 2026

Research question: The study evaluated consumer sentiment for McDonald’s across four prompts-recommendation (NPS), 6‑month sentiment change, last interaction, and 12‑month outlook.
Who was in the research group: Six US adults (ages 26–49) spanning data analysts, a restaurant manager, an emergency management director, and a sewing machine operator, with rural and bilingual Spanish representation.
What they said: Participants refused to score a “blank brand,” insisting on brand+product+recent-use context before giving NPS, interaction timing, or forecasts.
Directionally, they defaulted to neutral with a slight negative tilt driven by fee/subscription creep, delivery/inventory misses, privacy and labor concerns, and inconsistent support.

Main insights: Decision drivers center on operational basics-reliable products, transparent total cost, honest inventory and delivery (especially rural ETAs), and fast human support with easy returns/warranty-modulated by ethics/privacy, worker treatment, language access, and values fit.
For McDonald’s, near-term success is tied to proving order accuracy and speed by channel (drive‑thru, app, kiosk), clear pricing with no “junk fees” or shrinkflation, respectful worker practices and app privacy, and Spanish-language support plus rural service reliability.
Clear takeaways: Surface verifiable proof in post‑purchase touchpoints (e.g., ~95% on‑time/accuracy and most issues resolved within 24 hours), gate NPS behind last‑interaction details, publish and improve operational KPIs, localize support (Spanish) and rural ETA transparency, and emphasize consistency and transparent remediation over promos.

Participant Snapshots

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6 profiles

Ariya Ortega

26-year-old married mother in Beaverton, OR, working full-time in apparel manufacturing. Spanish-first, budget-focused, faith-oriented, and risk-averse. Values durability, transparent pricing, bilingual support, and routines that protect family time and sta…

Christopher Lim

Christopher Lim, Filipino senior retail merchandiser in rural Maryland, 35, single, high-earning, faith-driven and pragmatic. Road-warrior, style-savvy, data-led, cooks adobo, rides a cafe racer, supports family abroad, values durability, transparency, and…

Dean Deanda

Bilingual emergency operations coordinator in Sacramento, married with one child. Reliable, pragmatic, and family-centered. Values safety, transparency, and community. Commutes by motorcycle, loves soccer and grilling, and balances demanding on-call work wi…

Patricia Leppek

1) Basic Demographics

Patricia Leppek is a 49-year-old White woman living in Sandy City, Utah, USA. She uses she/her pronouns. Born in the United States, she grew up in the Mountain West and speaks English at home. She identifies as Buddhist and…

Derek Tsang

Derek Tsang is a Filipino American hospital IT professional in Enterprise, NV. Single homeowner with a dog, points-savvy traveler, motorcycle commuter, church volunteer, and Red Rock hiker. Pragmatic, tech-forward, budget-conscious, values reliability, safe…

Nicole Fowler

1) Basic Demographics

Nicole Fowler is a 30-year-old white, U.S.-born woman living in rural Iowa. She identifies as Muslim and speaks English at home. She is single with no children. She works full-time in food prep/serving and is known locally f…

Ariya Ortega

Christopher Lim

Dean Deanda

Patricia Leppek

1) Basic Demographics

Derek Tsang

Nicole Fowler

1) Basic Demographics

Overview 0 participants

Sex / Gender

Race / Ethnicity

Locale (Top)

Occupations (Top)

Demographic Overview No agents selected

Age bucket	Male count	Female count

Participant locations No agents selected

Participant Incomes US benchmark scaled to group size

Income bucket	Participants	US households

Source: U.S. Census Bureau, 2022 ACS 1-year (Table B19001; >$200k evenly distributed for comparison)

Media Ingestion

Connections appear when personas follow many of the same sources, highlighting overlapping media diets.

Questions and Responses

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4 questions

Response Summaries

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4 questions

Word Cloud

Persona Correlations

Overview

Respondents consistently refuse to provide blind sentiment scores and instead anchor judgments to concrete, recent experience and measurable proof points. Across demographic groups the primary drivers of recommendation are operational fundamentals (on-time fulfillment, truthful inventory, easy returns/repairs), transparent total cost, and rapid human support. Distinct segments layer secondary but decisive modifiers on top of those fundamentals: tech/data occupations demand provenance and privacy/ethics assurances; apparel/hands-on product users emphasize fit, durability and return friction; rural respondents prioritize reliable delivery and honest ETAs; bilingual respondents require language access and local social proof; values-driven respondents treat cultural fit as a first-order requirement. High-income, operations-focused evaluators translate preferences into prescriptive KPIs and tolerate less uncertainty. In short, willingness to recommend McDonald's-themed prompts (and brands more generally) is a two-step process for most respondents: 1) satisfy operational, pricing and support baselines; 2) meet persona-specific governance, language or cultural modifiers with demonstrable evidence.

Total responses: 24

Key Segments

Segment	Attributes	Insight	Supporting Agents
Tech / Data-oriented occupations	Data analysts, health informatics; ages ~35–49; homeowners; bachelor+ education	These respondents will not accept narrative claims - they require provenance (logs, retention policies), metrics and clear privacy/data-ethics posture before recommending. Governance failures equal operational failures in their scoring.	Patricia Leppek, Christopher Lim, Derek Tsang
Apparel / hands-on product users	Apparel industry and production roles; ages ~26–35; frequent buyer of physical goods	Fit consistency, construction quality (seams, hardware) and low-friction returns drive loyalty. ‘Ghost sizing’ and SKU drift are immediate loyalty killers; marketing claims are discounted without reproducible product proof.	Christopher Lim, Ariya Ortega
Rural / non-metro residents	Rural ZIPs or non-metro locations; mixed incomes; dependent on reliable shipping	Delivery reliability, honest ETAs and accurate inventory availability are gating factors for recommendation - rural users explicitly condition positive sentiment on demonstrated last-mile performance.	Nicole Fowler, Christopher Lim, Ariya Ortega
Bilingual / Spanish-speaking respondents	Spanish preference or bilingual; working-class occupations; community-oriented	Language accessibility (support in Spanish) and local, peer-sourced social proof materially affect perceived service quality and willingness to recommend - speed and cultural fluency in replies are expected.	Ariya Ortega, Dean Deanda
Values-driven / religious minority	Explicit religious identity (e.g., Muslim); rural or service roles; culturally sensitive purchasing	Cultural fit (e.g., halal considerations, non-alcohol marketing, ingredient transparency) functions as a primary scoring dimension for this group and can shift NPS substantially if unmet.	Nicole Fowler
High-income operational evaluators	Higher incomes ($150k+); managerial/analyst roles; KPI-oriented	These evaluators convert preferences into explicit thresholds (e.g., % on-time, SLA timing) and evaluate brands by operational KPIs rather than sentiment - they are unlikely to be promoters without clear SLA adherence and measurable outcomes.	Dean Deanda, Christopher Lim

Shared Mindsets

Trait	Signal	Agents
Demand for concrete stimuli	All respondents require specific product/interaction context - receipts, screenshots, model numbers or recent-use examples - to provide reliable sentiment ratings; blind logos or vague prompts are rejected.	Patricia Leppek, Nicole Fowler, Dean Deanda, Ariya Ortega, Christopher Lim, Derek Tsang
Operational fundamentals over marketing	On-time delivery, honest inventory, easy returns/repairs and product durability are prioritized above promotional messaging; failures on these fronts immediately suppress recommendation likelihood.	Nicole Fowler, Christopher Lim, Ariya Ortega, Dean Deanda, Patricia Leppek
Pricing and total cost sensitivity	Transparent pricing, absence of hidden fees and consideration of total cost of ownership (repairs, parts availability) are consistent determinants of loyalty across segments.	Patricia Leppek, Nicole Fowler, Dean Deanda
Human, effective support as trust lever	Quick, competent human support and measurable SLAs (fast first replies, swift resolution) elevate neutral users into promoters; automated-only or slow support erodes trust.	Derek Tsang, Nicole Fowler, Dean Deanda
Governance & ethics matter for a subset	Privacy, data-ethics, labor treatment and anti-greenwash evidence are decisive for tech/health and some civic-minded respondents - demonstrated policies and remediation matter.	Derek Tsang, Patricia Leppek, Christopher Lim
Language and local social proof importance	Availability of Spanish-language support and local peer reviews are concrete inputs to perceived quality for bilingual and community-oriented respondents.	Ariya Ortega, Dean Deanda, Nicole Fowler

Divergences

Segment	Contrast	Agents
Tech / Data-oriented vs. High-income operational evaluators	Both groups demand metrics, but tech respondents foreground data provenance and privacy/ethics as primary trust drivers, while high-income operational evaluators focus on prescriptive SLAs and business KPIs; tech roles treat governance failures as equally disqualifying.	Patricia Leppek, Derek Tsang, Dean Deanda, Christopher Lim
Apparel / hands-on users vs. Rural residents	Apparel users prioritize product construction and fit repeatability, whereas rural residents prioritize delivery reliability and truthful inventory - the former penalizes SKU inconsistency, the latter penalizes last-mile failure.	Christopher Lim, Ariya Ortega, Nicole Fowler
Bilingual / Spanish-speaking vs. General population	Bilingual respondents elevate language-access and local social proof to first-order decision rules; neutral/general respondents emphasize operational baselines without explicit language expectations.	Ariya Ortega, Dean Deanda
Values-driven / religious minority vs. Mainstream customers	Values-driven respondents integrate cultural/ingredient sensitivities directly into their scoring, making cultural fit a gating factor rather than a peripheral preference as it often is for mainstream respondents.	Nicole Fowler
Methodological purists vs. Typical raters	A subset (notably Patricia Leppek) refuses all blind ratings and demands exhaustive provenance; most others will provide directional feedback given minimal concrete context - this signals scoring-calibration variance across the panel.	Patricia Leppek, Christopher Lim

Recommendations & Next Steps

Overview

Panelists refused to rate a blank brand and instead revealed rule-based criteria for recommendation: operational reliability, transparent pricing/total cost, honest inventory & delivery, and fast, human support with easy returns/warranty. Ethics/privacy and language/cultural fit are decisive for segments. For Claude’s Ditto-connected research flow, the action is to enforce stimulus specificity, capture evidence, branch by persona needs, and map insights to McDonald’s QSR proof points (order accuracy, speed, app privacy, worker treatment).

Quick Wins (next 2–4 weeks)

#	Action	Why	Owner	Effort	Impact
1	Gate NPS behind brand + last-interaction context	Respondents won’t score without specifics; enforcing context will raise completion quality and reduce noise.	Research Ops	Low	High
2	Add evidence upload with prompts (receipt/screenshot)	Panelists asked for provenance; attachments enable time/channel precision and trustable insights.	Backend Eng	Med	High
3	Interaction checklist module	Defines what counts (order, delivery, support, in-store) to standardize responses and reduce ambiguity.	Survey PM	Low	High
4	Spanish toggle + rural ZIP capture	Language access and rural delivery realities materially affect sentiment; easy to add and segment.	Localization Lead	Low	Med
5	Values/Ethics screener	A subset requires privacy, labor, and cultural fit; capturing this upfront improves branching and analysis.	CX Researcher	Low	Med
6	QSR proof-point items for McDonald’s	Surface measurable cues respondents value: order accuracy, speed, pricing clarity, app privacy, worker treatment.	Client Partner	Low	High

Initiatives (30–90 days)

#	Initiative	Description	Owner	Timeline	Dependencies
1	Evidence-first NPS framework	Implement required fields for brand, product/order, time, and channel before NPS. Dynamic follow-ups to capture delivery honesty, inventory truth, support experience, and total cost. For McDonald’s, map to QSR contexts (drive-thru, kiosk, app, dine-in).	Research Ops	2 weeks to MVP	Survey logic builder in Ditto, Design updates for gating screens
2	Provenance & attachment pipeline	Enable receipt/screenshot uploads with OCR to prefill date, store/ZIP, order type; store securely with PII redaction and consent capture.	Backend Eng	3-4 weeks	Secure file storage, OCR service, Legal/privacy review
3	Persona-driven branching	Branch question banks based on rural ZIP, Spanish preference, and values/ethics importance. Include Spanish-language flows and localized examples.	Survey PM	2 weeks	Localization team, Segment flags in respondent profile
4	McDonald’s proof-point library	Curate and test proof elements respondents said they trust: Order accuracy and service time by channel Transparent pricing and fees App privacy commitments Worker treatment statements Language access availability Use concept tests to measure lift in sentiment.	CX Researcher	3 weeks	Client-approved claims & data, Creative/prototyping support
5	Ops KPI dashboard (QSR-focused)	Build a Ditto-connected dashboard showing respondent-verified KPIs: order accuracy, time-to-serve, refund rate, support SLA, Spanish coverage, rural experience deltas.	Data Analyst	3 weeks	Data model updates, BI tooling, Provenance fields
6	Rerun McDonald’s sentiment pilot	Field improved study (n=100-150; include rural and Spanish cohorts), collect attachments, and deliver an ROI-focused readout with prioritized fixes and message tests.	Research Lead	2 weeks fielding + 1 week analysis	Recruitment vendor, Updated survey, Attachment pipeline live

KPIs to Track

#	KPI	Definition	Target	Frequency
1	Stimulus completeness rate	% of responses with brand + last-interaction time + channel captured	>= 90%	Weekly
2	Evidence attachment rate	% of completes with a valid receipt/screenshot attached	>= 50%	Weekly
3	Actionable NPS yield	% of NPS responses that meet evidence and interaction criteria	>= 80%	Weekly
4	Spanish/rural coverage	% of completes from Spanish-pref respondents and rural ZIPs	>= 15% Spanish; >= 25% rural	Weekly
5	Proof-point lift	Delta in sentiment when verified proof elements are shown (concept tests)	+5-10 pts favorability on tested proof	Per study
6	Time to insight	Median hours from field close to dashboard-ready analysis	<= 48 hours	Per study

Risks & Mitigations

#	Risk	Mitigation	Owner
1	PII exposure via receipts/screenshots	Enable redaction, explicit consent, encrypted storage, and retention limits; legal review before launch.	Privacy Counsel
2	Low attachment compliance	Incentivize uploads, provide clear examples, allow delayed upload links, and make it optional but rewarded.	Research Ops
3	Segment misclassification (rural/language/values)	Self-identification questions, editable profile flags, and validation prompts.	Survey PM
4	Integration delays with Ditto/OCR	Use fallback manual fields, staged rollout, and vendor SLAs.	Backend Eng
5	Client proof-point data unavailable or unverifiable	Use respondent-verified metrics and third-party benchmarks; clearly label claims.	Client Partner

Timeline

Weeks 0-1: Design gating, checklists, Spanish/rural fields.
Weeks 1-3: Build attachment pipeline + persona branching; legal/privacy review.
Weeks 3-4: Proof-point library creation and dashboard setup; translations QA.
Weeks 4-6: Pilot fielding (n=100-150), ongoing KPI tracking, and readout with prioritized fixes.

Research Study Narrative

Objective and context

The McDonald’s Weekly Sentiment Tracker sought to evaluate consumer sentiment and drivers of recommendation. In this wave, respondents uniformly refused to rate a “blank brand,” instead revealing rule-based decision frameworks they use to translate lived experience into a 0–10 recommendation. This constraint is itself a key finding: without brand + product + recent-use context, participants default to heuristics rather than numeric NPS.

What we heard across questions

Demand for specificity: All six respondents declined to score or time-stamp interactions without a named brand and recent-use proof. As Patricia Leppek put it, “I can’t rate a blank logo. Which brand and what exactly have I used from them, recently?”
Operational fundamentals drive sentiment: Across questions, the same levers moved scores: product reliability/durability, transparent total cost (no hidden fees), honest inventory and delivery performance, and fast, human support with easy returns/warranty. Dean Deanda: “Field-tested beats flashy. I read the one-star reviews first.”
Default neutral to slightly negative without proof: Absent specifics, most stayed neutral; where directional views emerged, they skewed negative due to broader trends like fee/subscription creep, ghost inventory and slipped ETAs, and privacy/ethics doubts. Christopher Lim: “Net-net… I’m a shade more negative… rural ETAs slipped… sneaky restocking fee.”
Clear definitions of “interaction” matter: Participants distinguish orders, deliveries, support and returns from passive ad exposure. They volunteered structured detail they would provide with evidence (date, channel, what worked/broke, resolution, repeat intent).
Trust and governance for a subset: Data privacy, worker treatment, and honest sustainability claims are decisive for several. Derek Tsang flagged “data ethics shadiness” as a trust breaker.

Persona correlations and nuances

Tech/Data-oriented roles (Patricia, Derek, Christopher): Require provenance (logs/receipts), privacy posture, and measurable SLAs; treat governance failures as operational failures.
Rural/non-metro (Nicole, Christopher, Ariya): Honest ETAs and reliable last-mile are gating; missed rural delivery promises depress sentiment.
Bilingual/Spanish-speaking (Ariya, Dean): Language access and local peer proof shape perceived quality; expect fast replies “Spanish ok.”
Values-driven/religious (Nicole): Cultural fit (e.g., halal considerations, non-alcohol marketing) is a first-order scoring dimension.
High-income operational evaluators (Dean, Christopher): Translate preferences into KPIs (e.g., 95% on-time, 24-hour fixes) and calibrate scores conservatively (rare 10s).

Implications for McDonald’s (QSR mapping)

Respondents’ frameworks map cleanly to QSR proof points: order accuracy and speed (drive-thru, kiosk, app, dine-in), transparent pricing (no surprise fees), honest prep and pickup ETAs (especially rural), low-friction refunds/make-goods, fast human support, app privacy clarity, worker treatment, and Spanish-language access. Participants asked for verifiable evidence over narrative.

Recommendations

Gate NPS behind context: Require brand + last interaction time + channel before scoring; offer an interaction checklist (order, delivery, support, in-store).
Enable evidence uploads: Receipts/screenshots with OCR to prefill date, store/ZIP, order type; participants explicitly requested provenance.
Persona-driven branching: Capture rural ZIP and Spanish preference; add values/ethics screener to tailor probes on privacy, worker treatment, and cultural fit.
Build a McDonald’s proof-point library: Test respondent-trusted elements: order accuracy/time by channel, transparent pricing, app privacy commitments, worker treatment statements, Spanish availability.
Dashboard the ops KPIs that drive sentiment: Track respondent-verified order accuracy, time-to-serve, refund rate, support SLA, Spanish coverage, rural deltas.

Risks and mitigations

PII exposure via uploads: Redaction, consent, encrypted storage, short retention (legal review).
Low attachment compliance: Incentivize uploads, provide examples, allow delayed submission.
Segment misclassification: Self-ID questions with editable profile flags.
Integration delays (OCR/flows): Fallback manual fields; staged rollout with vendor SLAs.
Unavailable proof data: Use respondent-verified metrics and third-party benchmarks; label claims clearly.

Next steps and measurement

Weeks 0–1: Add gating screens, interaction checklist, Spanish toggle, rural ZIP capture.
Weeks 1–3: Launch attachment pipeline with OCR; legal/privacy review; enable persona branching.
Weeks 3–4: Curate McDonald’s proof-point library; set up QSR KPI dashboard; QA translations.
Weeks 4–6: Pilot (n=100–150) with ongoing KPI tracking; readout and prioritize fixes.

KPIs: Stimulus completeness ≥90%; evidence attachment ≥50%; actionable NPS yield ≥80%; Spanish coverage ≥15% and rural ≥25%; proof-point lift +5–10pts favorability when verified elements are shown.

Recommended Follow-up Questions Updated Jan 11, 2026

In the past 4 weeks, how many times have you purchased food or beverages from McDonald’s?

numeric Quantifies current engagement to benchmark sentiment and track shifts tied to campaigns, seasonality, or operations changes.
Which ordering or fulfillment channels have you used with McDonald’s in the past 4 weeks? (e.g., drive‑thru, dine‑in counter, self‑order kiosk, mobile app pickup, mobile app delivery, third‑party delivery, curbside, walk‑up window)

multi select Identifies channel mix to prioritize improvements, staffing, and channel-specific messaging.
Based on your recent McDonald’s experiences (past 3 months), rate your satisfaction with each: order accuracy; speed of service; food quality consistency; value for money; staff helpfulness; restaurant cleanliness; mobile app ease of use; delivery ETA accuracy.

matrix Surfaces operational drivers of sentiment to set KPIs and track progress by attribute.
When choosing where to get fast food, which factors are most and least important to you? Consider: order accuracy; speed; taste/food quality; total price fairness; convenient locations; menu variety; healthier options; mobile app ease; delivery reliability/ETA; loyalty rewards value; late‑night hours; data privacy trust; perceived worker treatment.

maxdiff Reveals trade‑off priorities to focus investments that most influence choice.
Which issues have you personally experienced with McDonald’s in the past 3 months, if any? (e.g., wrong/missing items; long wait time; item unavailable after ordering; app crash/payment failure; unexpected fees; delivery later than quoted; delivery food cold; hard to get refunds; language barrier; accessibility barrier; cleanliness issues; none)

multi select Quantifies defect hotspots to target fixes that will lift sentiment fastest.
What is the single most important improvement McDonald’s could make to increase your likelihood to visit more often?

open text Generates concise, high‑impact actions to increase frequency and advocacy.

Include the brand stimulus (“McDonald’s”) and the listed items/options to avoid prior refusals and enable tracking. Use consistent scales in the matrix (e.g., 1–5 satisfaction).

Study Overview Updated Jan 11, 2026

News & Media

Source	Weight

McDonald's - Weekly Sentiment Tracker

Ariya Ortega

Christopher Lim

Dean Deanda

Patricia Leppek

1) Basic Demographics

Derek Tsang

Nicole Fowler

1) Basic Demographics

Ariya Ortega

Christopher Lim

Dean Deanda

Patricia Leppek

1) Basic Demographics

Derek Tsang

Nicole Fowler

1) Basic Demographics

Sex / Gender

Race / Ethnicity

Locale (Top)

Occupations (Top)

Summary

Themes

Outliers

Overview

Key Segments

Shared Mindsets

Divergences

Overview

Quick Wins (next 2–4 weeks)

Initiatives (30–90 days)

KPIs to Track

Risks & Mitigations

Timeline

Objective and context

What we heard across questions

Persona correlations and nuances

Implications for McDonald’s (QSR mapping)

Recommendations

Risks and mitigations

Next steps and measurement

Distinct Market Research Groups

Marketing Reports Generated

Digital Twin Synthetic Personas

Questions Answered