Food Delivery App Preference & Restaurant Discovery Survey
Map how urban food delivery users evaluate app interfaces, compare restaurant options, and choose between platforms, so you can sharpen acquisition targeting, fix retention drop-offs, and benchmark your positioning against competing apps.
CONTEXT & RELEVANCE
Why run this survey now
Most food delivery platforms don't lose active users purely on delivery speed. They lose them due to weak restaurant discovery, irrelevant curation, opaque fee structures, poor reorder prompts, and misaligned cuisine recommendations, none of which fully show up in session analytics or order completion dashboards.
If you are...
- Food delivery platform, growth stage
- Restaurant aggregator, tier-2 expansion
- Category or menu strategy lead
- Retention and lifecycle marketing head
- Restaurant partner acquisition team
You're likely facing...
- Discovery drop-off: search vs browse
- Platform switching on fee perception
- New restaurant visibility gap
- Reorder rate below category benchmark
- Cuisine fit: local vs chain preference
This will help answer...
- Primary app selection drivers
- Discovery funnel drop-off stage
- Segment preference by cuisine type
- Fee sensitivity vs delivery speed
- Switching triggers and loyalty signals
RESEARCH THEMES
What This Survey Investigates
Eight interconnected research themes that map the complete food delivery journey from app discovery to repeat ordering.
App Discovery & Adoption
- First-install trigger channels
- Peer referral vs. paid acquisition
Restaurant Discovery
- In-app browse vs. search intent
- Cuisine filters, ratings, curation
Platform Preference Drivers
- Primary app loyalty triggers
- Multi-app switching frequency
Pricing & Fee Sensitivity
- Delivery fee tolerance thresholds
- Surge pricing abandonment rates
Order Journey Friction
- Checkout drop-off triggers
- Payment failure, address errors
Loyalty & Retention
- Subscription plan uptake drivers
- Reward redemption vs. churn risk
Trust & Ratings
- Review credibility, fake rating concern
- Food safety, hygiene badge reliance
Competitive Switching
- Rival app trial triggers
- Win-back offer effectiveness
SAMPLING STRATEGY
Tell us about your ideal sample
Help us understand your target respondent profile. Select what applies, we'll design the optimal sample plan based on your inputs.
METHODOLOGY
Survey approach
For the Food Delivery App Preference and Restaurant Discovery Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification.
EXECUTION PROCESS
How we execute
A proven 9-step process from scoping to delivery, designed to ensure quality, speed, and actionable insights.
Define the decision frame
Confirm objectives, target cohorts, geographies, and reporting cuts
Define the decision frame
Design the instrument
Build workstream modules mapped to outputs (drivers, friction, pricing, retention, trust)
Design the instrument
Lock the questionnaire
Review wording, sequencing, LOI, and competitive context; approve final version
Lock the questionnaire
Pilot and calibrate
Test comprehension and ease quality; refine quotas and remove friction where needed
Pilot and calibrate
Run fieldwork
Execute collection with active quota management and feasibility controls
Run fieldwork
Assure quality
Dedupe, attention checks, speed/consistency rules, removals with audit trail
Assure quality
Prepare the dataset
Clean data and deliver codebook/variable definitions
Prepare the dataset
Analyse and synthesise
Driver ranking, leakage diagnostics, pricing bands, segment insights
Analyse and synthesise
Deliver and align
Executive deck (optional dashboard) and leadership readout with recommendations
Deliver and align
COMMERCIAL TERMS
Request a Commercial Proposal
Pricing depends on cohort, geography, sample size, approach, LOI, and deliverables. Configure below for an indicative estimate.
Select Sample Size
Geography
Select Mode of Survey
Length of the Interview
$0.00
+ applicable taxes
Cost Breakdown
Note: Estimate is indicative only. Final pricing is subject to scope finalization after discovery call.
REFERENCE CASELETS
Reference
Real-world examples of survey work in the food delivery and restaurant discovery space.
Restaurant listing visibility & cuisine preference mapping (Urban India)
OBJECTIVE
WHAT WE DID
DELIVERED
Restaurant listing visibility & cuisine preference mapping (Urban India)
Delivery platform messaging & reorder friction audit (Tier 2 Cities)
Restaurant listing visibility & cuisine preference mapping (Urban India)
OBJECTIVE
A quick-service restaurant chain needed to identify how frequent app-based orderers and occasional dine-in converters shortlist restaurants, and which listing attributes drive first-order selection versus repeat ordering behaviour.
WHAT WE DID
Ran a structured quant survey across 6 metros with 480 respondents, capturing cuisine preference rank, listing cue hierarchy, rating threshold, delivery time sensitivity, and platform switching triggers by order frequency segment.
DELIVERED
A cuisine-by-segment preference map , ranked listing attribute framework by orderer type, and a set of platform switching levers tied to specific listing gaps that suppress first-order conversion.
FREQUENTLY ASKED QUESTIONS
Common Questions
Answers to frequently asked questions about this survey mandate.
What decisions will this survey enable?
Who is the buyer vs who are the respondents?
Can we see differences between casual orderers, frequent subscribers and lapsed users?
How will you measure app preference beyond simple ratings?
Will the survey map the full restaurant discovery journey and drop-offs?
Can this survey inform product and pricing strategy?
How will findings improve our restaurant partner acquisition and retention strategy?
Still have questions?
Schedule a discovery call to discuss your specific needs and get a custom quote.