Quick Commerce Usage & Delivery Expectation Survey
Measure how urban shoppers evaluate speed, reliability, and platform choice when placing quick commerce orders, so you can sharpen acquisition targeting, fix delivery promise positioning, and benchmark retention by order frequency segment.
CONTEXT & RELEVANCE
Why run this survey now
Most quick commerce platforms don't lose repeat orders purely on delivery speed. They lose them due to inconsistent slot reliability, SKU availability gaps, pricing opacity, post-order communication failures, and mismatched category expectations, none of which fully show up in app analytics or NPS dashboards.
If you are...
- Quick commerce category head
- Dark store network planner
- Retention and lifecycle lead
- Pricing and margins strategist
- Platform growth and GTM lead
You're likely facing...
- Repeat order drop-off post first use
- Slot promise vs actual delivery gap
- Speed vs assortment trade-off tension
- Surge fee resistance at checkout
- Platform switching on single failures
This will help answer...
- Delivery speed threshold by segment
- Drop-off trigger by order stage
- Category-level assortment priority gaps
- Fee tolerance vs loyalty trade-off
- Platform switch triggers and retention levers
RESEARCH THEMES
What This Survey Investigates
Eight interconnected research themes that map the complete quick commerce journey from first-order trigger to platform loyalty.
Discovery & Adoption
- First-platform trigger, category entry
- Referral, ad, and storefront influence
Platform Preference
- Primary platform, multi-app switching
- Preference rank across active platforms
Delivery Expectations
- Promised vs. acceptable delivery windows
- Late delivery tolerance, re-order intent
Order Frequency
- Weekly order cadence by category
- Basket size, top-up vs. stock-up split
Pricing & Fees
- Delivery fee sensitivity, surge tolerance
- Subscription plan uptake, fee trade-offs
Assortment & Stock
- SKU depth, out-of-stock substitution
- Private label vs. branded preference
Trust & Reliability
- Product freshness confidence, quality complaints
- Refund resolution speed, support experience
Loyalty & Advocacy
- Retention triggers, churn risk signals
- Referral behaviour, word-of-mouth drivers
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 Quick Commerce Usage and Delivery Expectation Survey, we recommend a quant-first design with flexible data-collection modes to balance reach, depth, and verification across urban and peri-urban quick commerce users.
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 quick commerce and on-demand delivery space.
Delivery slot preference & friction mapping among urban grocery shoppers (India)
OBJECTIVE
WHAT WE DID
DELIVERED
Delivery slot preference & friction mapping among urban grocery shoppers (India)
Category messaging & platform positioning for rapid delivery apps (West India)
Delivery slot preference & friction mapping among urban grocery shoppers (India)
OBJECTIVE
A grocery delivery platform needed to isolate how daily essentials shoppers and top-up buyers ranked slot flexibility against delivery fee tolerance , and where friction in the post-order experience drove category abandonment.
WHAT WE DID
Ran a structured quant survey across 600 respondents in 6 metros, capturing preferred delivery windows , fee thresholds by basket size , substitution acceptance rates , and re-order triggers segmented by order frequency and household size.
DELIVERED
A slot preference corridor by shopper segment, a ranked friction list across the post-checkout journey, and a fee sensitivity framework mapping willingness to pay against basket value and delivery urgency.
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 occasional buyers, weekly regulars and daily power users?
How will you measure delivery expectation beyond simple ratings?
Will the survey map the full quick commerce order journey and drop-offs?
Can this survey inform product and pricing strategy?
How will findings improve our dark store expansion and slot monetisation decisions?
Still have questions?
Schedule a discovery call to discuss your specific needs and get a custom quote.