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Hopper Travel App Price Prediction Feedback

Understand budget-conscious traveler perceptions of AI price prediction tools, trust in booking apps, and attitudes toward price freeze features

Study Overview Updated Jan 26, 2026

Research question: Understand budget-conscious traveler perceptions of AI price prediction tools, trust in booking apps, and attitudes toward price‑freeze features across three booking‑timing prompts.
Research group: six US budget travelers (ages 33–46; CA/PA/NC; mixed incomes/roles incl. warehouse, admin, maintenance, creative), including Spanish‑preferring and debit‑first users.
What they said: they overwhelmingly distrust opaque price‑prediction claims (e.g., “95% accurate”), rely on simple rules and free alerts, prioritize schedule and out‑the‑door costs over marginal savings, and view Price Freeze as rarely worth paying for except in narrow, high‑stakes windows due to fine print, nonbinding inventory, and sunk‑fee risk. Main insights: trust requires transparent route‑level evidence (including misses), cash‑backed remedies when guidance is wrong, booking integrity (instant PNR/fare‑class), true total pricing (bags/seats/taxes), and fast human support (incl. Spanish/WhatsApp) with debit‑friendly flows.

Retire the “95% accurate” headline; publish a concise methodology and route/season scoreboard with audited backtests and misses.
Ship target‑price controls with quiet alerts and a total‑cost calculator (bags, seats, parking, alternate airports) to match how they buy.
Reframe Price Freeze as a credited, low‑fee time option (≤2–3% of fare) with 5–7‑day windows, explicit coverage caps incl. taxes, fare‑class/seat integrity, and no card holds; debit payouts/refunds ≤3 days.
Harden booking integrity and support: instant airline PNR, clear fare rules, family‑seating warnings, published SLAs, and bilingual human channels.

Participant Snapshots

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6 profiles

Jessica Pena

1) Basic Demographics

Jessica Pena is a 44-year-old woman living in urban Raleigh, North Carolina. She uses she/her pronouns, is married, and has one child. She earned a Bachelor’s degree and works full-time. She was born in Spain and is a non-U.…

Nickalous Dias

Nickalous Dias, 44, is a married, bilingual San Diego homeowner and dad of one. A Sales Operations Coordinator at an automotive parts distributor, he’s pragmatic, budget-conscious, and car-loving—favoring reliable, no-drama brands, community volunteering, a…

Caleb Rawlings

Basic Demographics

Caleb Rawlings is a 33-year-old male living in Los Angeles city, CA, USA. He’s Czech by citizenship and ethnicity (White), speaks Czech at home, and identifies as unaffiliated religiously. He’s single, has no children, uses he/…

Daniel Villarreal

Dependable 40-year-old Dominican father in Reading, PA, working full-time in warehousing. Spanish-first, budget-conscious, community-oriented, and uninsured. Values clear pricing, durability, and bilingual service that fits around shift work, church, and pa…

Shimon Berry

Shimon Berry, 40, is a Navy Reservist and family-focused homeowner in rural California. Budget-conscious and practical, he values durability, clear terms, and community. He is completing HVAC certification while balancing faith, parenting, and DIY projects.

Maren Hughes

Maren Hughes, rural Michigan caregiver, Hindu by marriage, bilingual home. Frugal, family-first, and reliable. Drives client-to-client, cooks vegetarian, and favors durable, transparent brands. Practical, warm, and gently witty with a community-centered, mo…

Jessica Pena

1) Basic Demographics

Nickalous Dias

Caleb Rawlings

Basic Demographics

Daniel Villarreal

Shimon Berry

Maren Hughes

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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3 questions

Response Summaries

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3 questions

Word Cloud

Persona Correlations

Overview

Across 18 respondents, attitudes toward AI price‑prediction and paid Price Freeze features cluster around practical risk management and operational friction rather than abstract trust in algorithms. Lower‑income and Spanish‑language respondents prioritize payment mechanics, bilingual human support, and low‑friction flows; parents and calendar‑bound travelers prioritize schedule certainty and seat/baggage integrity; higher‑income and frequent travelers demand technical transparency (route/fare‑class evidence, backtests) and guarantees before paying. Most users prefer simple, rule‑based decision processes (personal price cap, a few monitored sources, alerts) and view Price Freeze as an insurance product they would only buy in narrow, high‑stake windows - and only if fees, coverage and support are explicit and low‑friction.

Total responses: 18

Key Segments

Segment	Attributes	Insight	Supporting Agents
Spanish‑language, lower‑income frontline workers	Language: Spanish Income: $25–49k Occupation: warehouse / frontline roles Education: <High school / some college	Operational constraints (debit cards, fear of long card holds, spotty connectivity) and need for Spanish human support make fee‑based holds unattractive; these travelers prefer immediate, low‑friction purchases when funds align and simple alerts over paid insurance features.	Daniel Villarreal, Nickalous Dias, Jessica Pena
Parents and calendar‑bound travelers (mid‑40s, family responsibilities)	Age: ~40–46 Marital status: married / family caregivers Primary constraint: school calendar, PTO, coordinating multiple travelers	Decisions are driven by fixed dates and family logistics; Price Freeze might be considered for short windows (24–72 hours) while waiting on PTO or group coordination, but only if the product guarantees seats, baggage and schedule integrity.	Nickalous Dias, Jessica Pena, Maren Hughes, Shimon Berry
Higher‑income, frequent or business travelers	Income: $150k+ Occupation: creative / professional (video editor, sales ops) Travel behavior: frequent / business travel	Willing to pay modest fees if value is proven; demand route‑level evidence, fare‑class transparency, backtests and clear guarantees (money‑back or auto‑comp) to accept predictive claims or paid holds.	Caleb Rawlings, Jessica Pena
Rural / limited‑connectivity travelers	Location: rural / limited internet Device constraints: older/cracked phones, app fatigue Concern: unreliable push/real‑time UI	Time‑limited UI elements and hold windows are risky when connectivity is poor; these respondents need offline‑robust flows, non‑time‑sensitive confirmation channels, and accessible human support - otherwise they avoid paid holds.	Shimon Berry, Maren Hughes, Daniel Villarreal
Pragmatic, budget‑conscious mid‑earners	Income: $50–150k Education: some college / bachelor's Occupation: maintenance / administrative / sales ops / home health	Favor simple, defensible decision rules (price caps, monitor 2–3 sources, include door‑to‑door costs) and treat prediction flags as nudges; unlikely to pay for a freeze unless fee is small/credited and coverage is explicit.	Shimon Berry, Maren Hughes, Jessica Pena, Nickalous Dias

Shared Mindsets

Trait	Signal	Agents
Distrust of opaque AI price predictions	Across incomes and roles respondents label algorithmic predictions as marketing or guesswork and treat them as non‑binding nudges rather than prescriptive advice.	Shimon Berry, Nickalous Dias, Daniel Villarreal, Caleb Rawlings, Jessica Pena, Maren Hughes
Rule‑based purchase behavior	Most use simple heuristics (personal price cap, monitor a few sources, 24‑hour cancel windows) and prefer to act when self‑defined conditions are met rather than rely on predictive certainty.	Shimon Berry, Jessica Pena, Daniel Villarreal, Maren Hughes, Nickalous Dias, Caleb Rawlings
Priority on schedule and total out‑the‑door cost	Travelers consistently factor in arrival time, nonstops, seats together and ancillary fees (bags, parking, transfers) into purchase decisions - sometimes outweighing marginal fare savings.	Nickalous Dias, Jessica Pena, Caleb Rawlings, Shimon Berry, Maren Hughes
Price Freeze seen as insurance used only in narrow cases	Most view freeze as a paid insurance product and would use it only for high‑stake scenarios (PTO uncertainty, group coordination, peak holiday travel) and only with clear low‑friction terms.	Nickalous Dias, Jessica Pena, Daniel Villarreal, Caleb Rawlings, Maren Hughes, Shimon Berry
Product requirements to build trust	Respondents want plain‑language terms, coverage of out‑the‑door costs, route/fare transparency, refunds/auto‑comp, and fast human support (including Spanish) before paying for predictive features.	Jessica Pena, Caleb Rawlings, Daniel Villarreal, Nickalous Dias, Maren Hughes, Shimon Berry
Preference for free or low‑tech alternatives	People default to price alerts, lightweight monitoring (spreadsheets, notes) and personal rules instead of paid holds - especially when app/device overhead or payment risk is a concern.	Daniel Villarreal, Jessica Pena, Shimon Berry, Maren Hughes

Divergences

Segment	Contrast	Agents
Spanish‑language, lower‑income vs Higher‑income frequent travelers	Lower‑income respondents reject feeed holds due to debit/card hold risk, operational friction and need for Spanish human support; higher‑income travelers are more open to paying if the product provides technical evidence, fare‑class guarantees and clear compensation.	Daniel Villarreal, Nickalous Dias, Caleb Rawlings, Jessica Pena
Parents / calendar‑bound vs Pragmatic budget‑conscious mid‑earners	Parents prioritize schedule integrity and will tolerate paying for certainty in narrow windows; budget‑conscious mid‑earners prioritize minimal fees and follow strict price caps, often preferring to skip paid insurance unless very cheap and comprehensive.	Nickalous Dias, Jessica Pena, Shimon Berry, Maren Hughes
Rural / limited‑connectivity vs App‑comfortable urban users	Those with poor connectivity or device fragility distrust time‑sensitive UI and rely on phone support or offline methods, reducing willingness to engage with app‑centric paid holds that assume reliable real‑time updates.	Shimon Berry, Maren Hughes, Daniel Villarreal

Recommendations & Next Steps

Overview

Budget-conscious travelers distrust opaque price predictions and treat paid holds as insurance they rarely need. They prefer simple controls (target price, quiet alerts), clear out-the-door totals (bags, seats, transfers), and operational guarantees (instant PNR, fare-class integrity, fast human support, Spanish language). A headline like “95% accurate” hurts trust unless backed by route-level proof and cash-backed remedies. Action: pivot from magic predictions to user control + audited transparency, reframe Price Freeze as a low-friction, credited option for narrow high-stakes windows, and harden booking integrity and support.

Quick Wins (next 2–4 weeks)

#	Action	Why	Owner	Effort	Impact
1	Retire the “95% accurate” headline and add a transparent methodology stub	Unqualified claims reduce trust; users want definitions and proof, not slogans.	Marketing + Data	Low	High
2	Add target-price control with quiet alerts (no “buy now” nudges)	Matches rule-based behavior; lowers anxiety and drives conversion without pushiness.	Product	Low	High
3	Show true total price toggles (bags, seats) and simple airport/parking notes	Users decide on total trip cost and convenience, not sticker fare.	Product/UX	Med	High
4	Price Freeze clarity card (caps, what’s covered, fee credit) in EN/ES	Plain-language terms and fee credit address top objections and increase trial.	Product + CX	Low	Med
5	Publish support channels and SLAs (incl. Spanish, WhatsApp option)	Fast human help is a core trust driver, especially for debit-focused users.	CX	Low	Med
6	Surface booking integrity status (instant PNR ETA, fare-class lock notice)	Visible confirmation and fare-class clarity reduce bait-and-switch fear.	Engineering	Low	Med

Initiatives (30–90 days)

#	Initiative	Description	Owner	Timeline	Dependencies
1	Transparent Prediction Program + Public Scoreboard	Define accuracy (direction, magnitude, savings vs simple baselines), publish route/season breakdowns and misses, and add cash-backed auto-comp when guidance causes loss. Include a simple, audited methodology page and per-route confidence labels.	Data Science + Risk	MVP scoreboard in 8–10 weeks; auto-comp pilot 12–16 weeks	Historical + real-time fare pipelines, Legal review of guarantees and caps, Finance reserve modeling and payouts, Analytics event taxonomy and storage
2	Price Freeze 2.0 - Credited, Clear, and Debit-Friendly	Reframe freeze as a time option for high-stakes windows. Credit the fee toward the ticket, show explicit coverage caps, define base fare + taxes scope, warn on bag/seat add-ons, support 5–7 day windows where possible, and enable debit-friendly flows (no large holds, 3-day refunds).	Product	Spec in 3 weeks; pilot on top 20 routes in 8–10 weeks	GDS/NDC partner capabilities for fare-class linking, Payments processor instant payout rails, Legal terms (EN/ES) and disclosures, CX training + refund tooling
3	Booking Integrity: Instant PNR + Fare-Class/Seat Integrity	Guarantee instant airline PNR issuance and attempt short fare-class holds where supported; display hold status, cabin rules, and seat selection warnings (e.g., family may be split) before purchase.	Partnerships + Engineering	Route coverage matrix in 4 weeks; phased airline integrations 12–20 weeks	Airline/GDS/NDC agreements, Queueing + retry infrastructure, Seat-map and fare-rule data providers, Escalation playbooks for failures
4	Total-Cost Calculator + Alternate Airport Comparator	Add toggles for bags, seats, parking, ground transfer, and compare alternate airports (incl. cross-border like CBX) with time/cost trade-offs and user-set thresholds (drive up to 2 hours, no red-eyes).	Product/UX	Prototype in 4 weeks; GA in 6–8 weeks	Ancillary fee datasets (bags/seats), Parking/transfer cost APIs, Itinerary time-cost modeling, UX research on threshold defaults
5	Support & Reliability: Spanish Humans, WhatsApp, IRROPS	Stand up Spanish-language phone/chat, WhatsApp channel, and day-of-travel (IRROPS) escalation with published SLAs; add offline-friendly receipts/exports and clear refund timelines.	CX	Hiring/training in 3–4 weeks; full rollout in 6 weeks	Vendor selection or in-house hiring, Training on fare rules and guarantees, Telephony/WhatsApp integration, Refund automation tooling
6	Privacy & Upsell Restraint + Quiet Mode	Create a Quiet Mode with opt-in alerts only, remove dark patterns, simplify consent, and publish a concise privacy summary. Reduce subscription creep; no forced add-ons.	Product + Legal	Policy + UI updates in 4–6 weeks	Legal/privacy review, Notification preference center, Experimentation to measure impact on conversion, Analytics for opt-in behavior

KPIs to Track

#	KPI	Definition	Target	Frequency
1	Prediction Trust & Usage	Share of users who view the methodology/scoreboard and then enable prediction guidance; post-session trust score (0–10).	40% view-to-enable; +20 pt lift in trust vs baseline	Monthly
2	PNR Speed & Fare Integrity	Bookings with airline PNR issued <2 minutes and correct fare-class/seat as selected.	PNR <2 min in 98% of cases; <0.5% fare-class mismatches	Weekly
3	Price Freeze 2.0 Efficacy	Freeze-to-booking conversion, average user value (fare delta minus fee), and refund/credit utilization.	≥60% conversion; ≥$25 net value per use; >90% fee credit utilization	Monthly
4	Total-Cost Calculator Adoption	Share of shoppers who toggle bags/seats and alternate-airport comparison; impact on checkout rate.	≥50% toggle usage; +5% checkout lift on eligible routes	Monthly
5	Support SLA & CSAT (EN/ES)	Median first-response time, resolution time for IRROPS, CSAT for Spanish channels.	FRT ≤60s chat/≤90s phone; IRROPS resolution ≤20 min; CSAT ≥4.6/5	Weekly
6	Guarantee Auto-Comp Performance	Percent of eligible cases auto-comped at checkout and payout time to debit.	≥95% auto-comped; payouts ≤3 business days	Monthly

Risks & Mitigations

#	Risk	Mitigation	Owner
1	Guarantee/auto-comp increases financial exposure and potential regulatory scrutiny.	Set dynamic caps by route/season, exclude extreme anomalies with disclosures, maintain reserves, and run limited pilots before scaling.	Legal + Finance + Risk
2	Airline/GDS constraints limit fare-class holds and instant PNR issuance on some routes.	Publish a route coverage matrix, prefer NDC-enabled partners, implement graceful fallbacks, and notify users when a hold is not possible.	Partnerships + Engineering
3	Abuse/gaming of guarantees and fee credits.	Fraud rules (identity, velocity, device), per-user caps, and anomaly detection on freeze/prediction usage.	Risk/Trust & Safety
4	Support costs rise with Spanish human channels and IRROPS coverage.	Tiered routing, knowledge base, proactive alerts, and staffing models aligned to seasonality; measure CSAT and deflection quality.	CX
5	Privacy backlash from tracking and upsell patterns.	Implement privacy-by-default settings, remove dark patterns, clear consent, and publish a simple privacy summary.	Legal + Product
6	FX and tax slippage on international itineraries creates mismatch in coverage.	Quote coverage in booking currency with explicit FX rules; hedge or set buffers; disclose clearly in EN/ES.	Finance + Legal

Timeline

0–30 days: Quick wins (copy changes, target-price alerts, EN/ES clarity, SLA publish), scoreboard spec, support hiring kickoff.
31–60 days: Scoreboard MVP live, Total-Cost Calculator GA, Spanish/WhatsApp support live, Quiet Mode privacy/upsell updates.
61–120 days: Price Freeze 2.0 pilot on top routes, instant PNR/fare-class integrations phased, auto-comp guarantee pilot, route coverage matrix published.
120+ days: Scale Price Freeze 2.0 and guarantee, expand airline/NDC coverage, optimize caps/fees based on KPI performance.

Research Study Narrative

Objective and context

We set out to understand budget-conscious traveler perceptions of AI price prediction tools, trust in booking apps, and attitudes toward price freeze features. Findings span three lines of inquiry: how people decide when to buy, whether they’d pay to lock a fare, and what it would take to trust “95% accurate” AI claims.

What we heard across questions

Opaque predictions erode trust; simple rules win. Respondents overwhelmingly distrust “buy now” style predictions, favoring defensible heuristics and light alerts. As Shimon Berry put it, “I don’t trust those ‘price predictor’ apps. Feels like a slot machine with push alerts.” Jessica Pena exemplified the dominant behavior: “Set a target price… If I see close to that number again, I buy.”
Total trip cost and convenience outweigh marginal fare savings. Travelers trade small savings for better schedules, nonstop options, and baggage/seat integrity. Nickalous Dias: “I care about total trip hassle, not squeezing the last 20 bucks.” Several do full door-to-door math, including bags, parking, and transfers, before acting.
Low-tech safeguards reduce anxiety. To avoid endless monitoring, some use tangible aids (spreadsheets, fridge notes) and 24-hour cancellation windows. Daniel Villarreal: “I write the prices on a paper on the fridge.”
Price Freeze is seen as insurance-useful only in narrow, high‑stakes windows. Most would “almost never” pay a fee (Nickalous) except for PTO/school coordination, group trips, peak weeks, or big long-haul (Jessica). Core anxieties: fine print (caps; base fare vs taxes/fees/bags), no guarantee of seat/fare-class inventory (Caleb), and sunk-fee regret if fares don’t move. Practical demands included small, transparent, credited fees; clear coverage boundaries; and bilingual human help. Daniel: “Debit only… Money in limbo makes me not sleep. Bilingual help. I want real Spanish support, not a bot.”
“95% accurate” claims backfire without proof and remedies. A headline figure reduces trust: “A 95% claim smells like marketing” (Jessica). Users want route/season breakdowns, public backtests (including misses), and “skin in the game” via cash difference refunds (Daniel). Booking integrity (instant PNR, fare-class/seat holds) and true out-the-door pricing are non-negotiable.

Persona nuances

Spanish‑language, lower‑income/frontline users (e.g., Daniel, Nickalous): prioritize debit-friendly flows, fear card holds, and need Spanish human support; prefer immediate purchases and free alerts over paid freezes.
Parents/calendar‑bound travelers (e.g., Nickalous, Jessica, Maren, Shimon): may consider a short window freeze for PTO/school coordination if it protects seats, bags, and schedules.
Higher‑income/frequent travelers (e.g., Caleb, Jessica): open to fees only with route‑level evidence, fare-class transparency, and money‑back guarantees.
Rural/limited‑connectivity users (e.g., Shimon, Maren, Daniel): need offline‑robust flows and reliable human support; time‑sensitive holds feel risky.

Recommendations

Retire the “95% accurate” headline; publish a transparent methodology and route‑level scoreboard. Define accuracy, show wins and misses, and benchmark against simple rules.
Add user‑set target‑price controls with quiet alerts. Align with rule‑based behavior; remove pushy “buy now” nudges.
Expose out‑the‑door costs. Toggles for bags, seats, parking, and alternate airports with time/cost trade‑offs.
Price Freeze 2.0: credited, clear, and debit‑friendly. Small, transparent, EN/ES terms; explicit caps/coverage scope; 5–7 day windows where viable; fees credited to the ticket; no large holds; fast refunds.
Booking integrity and support. Instant airline PNR, fare‑class/seat warnings (e.g., family seating risks), published SLAs, Spanish and WhatsApp support.

Risks and mitigations

Financial exposure from guarantees. Use dynamic caps, exclusions for anomalies, reserves, and limited pilots.
Airline/GDS constraints on holds/PNR speed. Publish coverage matrix, prioritize NDC partners, and provide graceful fallbacks.
Abuse of guarantees/credits. Apply fraud rules, per‑user caps, and anomaly detection.
Support cost growth. Tiered routing, knowledge base, proactive alerts, and seasonal staffing.

Next steps and measurement

0–30 days: Replace headline claim with methodology stub; launch target‑price alerts; EN/ES Price Freeze clarity card; publish support channels/SLAs.
31–60 days: Ship scoreboard MVP; release total‑cost calculator; stand up Spanish/WhatsApp support.
61–120 days: Pilot Price Freeze 2.0 on top routes; phase in instant PNR/fare‑class integrations; test cash auto‑comp on defined routes.

Measurement guardrails:

Prediction trust & usage: 40% scoreboard view‑to‑enable; +20pt trust lift.
PNR speed & fare integrity: PNR <2 min in 98%; <0.5% fare‑class mismatches.
Freeze efficacy: ≥60% freeze‑to‑booking; ≥$25 net value/use; >90% fee credit utilization.
Total‑cost adoption: ≥50% toggle usage; +5% checkout lift on eligible routes.
Support SLAs/CSAT (EN/ES): FRT ≤60s chat/≤90s phone; IRROPS ≤20 min; CSAT ≥4.6/5.

Recommended Follow-up Questions Updated Jan 26, 2026

For each scenario, what is the maximum fee you would pay to lock a fare price for 7 days? Scenarios: (a) $400 domestic roundtrip, (b) $600 peak‑holiday domestic, (c) $900 international roundtrip, (d) $1,200 long‑haul with fixed PTO dates.

matrix Sets scenario‑based willingness‑to‑pay, informing Price Freeze fee tiers, caps, and promo thresholds.
Which potential Price Freeze change would most increase your likelihood to use it, and which least? Options: guarantee of seat/fare‑class hold (airline PNR); unused fee credited to future booking; coverage includes taxes/mandatory fees; clear payout cap with examples; ability to transfer to new dates once; instant refund if freeze fails; Spanish terms/support; debit‑friendly (no credit hold).

maxdiff Prioritizes the most persuasive product changes to drive adoption and reduce perceived risk.
In a typical year, how often do you face a situation where locking a fare price would be valuable (e.g., peak weeks, group trips, fixed PTO)?

frequency Quantifies frequency of high‑stakes use cases to size demand and marketing focus for Price Freeze.
What minimum expected savings (in dollars) would make you wait 7 days before booking a $400 roundtrip you could purchase today?

numeric Calibrates ‘wait vs buy’ thresholds to tune AI guidance and notification messaging.
Which price‑tracking controls would you actually use? Select all that apply: set my own target price; quiet digest alerts (no “buy now” pushes); alert only if change exceeds X%; snooze/mute alerts; watch specific flights only; total trip cost alerts incl. bags/seats; calendar‑based ‘buy by’ reminders.

multi select Guides alert and control design toward features users will adopt, reducing alert fatigue.
When choosing a booking app, how important are the following? Items: instant airline PNR/ticket issuance; fare class shown pre‑purchase; true total price incl. bags/seats/taxes; 24‑hour free cancellation; refund speed; fast human support (<5 min); Spanish support; WhatsApp chat; multiple payment methods incl. debit/PayPal; third‑party‑audited accuracy metrics for price guidance.

matrix Ranks operational trust signals and support needs to prioritize roadmap and partner requirements.

Use currency formatting for numeric inputs and define clear scales for matrix/frequency. Consider randomizing option orders to mitigate order bias.

Study Overview Updated Jan 26, 2026

Retire the “95% accurate” headline; publish a concise methodology and route/season scoreboard with audited backtests and misses.
Ship target‑price controls with quiet alerts and a total‑cost calculator (bags, seats, parking, alternate airports) to match how they buy.
Reframe Price Freeze as a credited, low‑fee time option (≤2–3% of fare) with 5–7‑day windows, explicit coverage caps incl. taxes, fare‑class/seat integrity, and no card holds; debit payouts/refunds ≤3 days.
Harden booking integrity and support: instant airline PNR, clear fare rules, family‑seating warnings, published SLAs, and bilingual human channels.

News & Media

Source	Weight

Hopper Travel App Price Prediction Feedback

Jessica Pena

1) Basic Demographics

Nickalous Dias

Caleb Rawlings

Basic Demographics

Daniel Villarreal

Shimon Berry

Maren Hughes

Jessica Pena

1) Basic Demographics

Nickalous Dias

Caleb Rawlings

Basic Demographics

Daniel Villarreal

Shimon Berry

Maren Hughes

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 nuances

Recommendations

Risks and mitigations

Next steps and measurement

Distinct Market Research Groups

Marketing Reports Generated

Digital Twin Synthetic Personas

Questions Answered