About DeliverEasy
Our Mission and Approach
DeliverEasy exists to solve a fundamental problem in the food delivery industry: pricing opacity. When delivery platforms first gained mainstream adoption in 2018-2020, fee structures were relatively simple. By 2024, the average consumer faces seven different fee types, menu price markups, dynamic pricing, and subscription options that make cost comparison nearly impossible without dedicated analysis.
We started this project in late 2022 after conducting a personal experiment comparing 50 identical orders across DoorDash, Uber Eats, and Grubhub. The price variation for the same food from the same restaurant delivered to the same address ranged from $38 to $54 - a 42% difference. That discrepancy existed not due to service quality differences, but purely from fee structures, promotional timing, and subscription status.
Our approach combines data analysis, platform policy research, and real-world testing. We place actual orders across different platforms, locations, and times to understand how pricing works in practice rather than theory. We track fee changes, subscription benefits, and promotional patterns across 15 major US metro areas. This hands-on methodology ensures our recommendations reflect actual user experiences rather than marketing claims.
The food delivery market has matured significantly since the pandemic-driven boom of 2020-2021. According to data from the National Restaurant Association, delivery now represents 15% of total restaurant sales, up from 8% in 2019. This permanent shift means delivery pricing affects household budgets substantially - the average American household spending $804 annually on delivery fees alone, separate from food costs. We believe consumers deserve clear, actionable information to make informed decisions about this significant expense category. Our home page provides the core comparison data, while our FAQ section addresses specific scenarios users encounter.
| Year | Market Size (Billions) | Active Users (Millions) | Avg Annual Spend/User | % of Restaurant Sales |
|---|---|---|---|---|
| 2019 | $26.5 | 44 | $602 | 8% |
| 2020 | $58.2 | 78 | $746 | 13% |
| 2021 | $89.7 | 96 | $934 | 16% |
| 2022 | $112.3 | 103 | $1,090 | 15% |
| 2023 | $138.4 | 108 | $1,281 | 15% |
| 2024 | $151.5 | 112 | $1,353 | 15% |
How We Research and Test
Our research methodology combines quantitative data collection with qualitative user experience assessment. We maintain active accounts across all major delivery platforms in 15 cities representing diverse geographic and demographic profiles: New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas, San Jose, Austin, Jacksonville, Fort Worth, Columbus, and Charlotte. This geographic distribution captures urban density variations, regional pricing differences, and restaurant availability patterns.
We conduct monthly pricing audits involving 200 standardized orders. These orders span different cuisine types, price points, distances, and times of day. We document every fee component, compare total costs, track delivery times, and assess order accuracy. This systematic approach has generated over 4,800 data points since we began tracking in January 2023, creating a robust dataset for identifying patterns and anomalies.
Platform policy analysis forms another research pillar. We review terms of service updates, commission structure changes, and regulatory compliance adjustments across all major platforms. We monitor Federal Trade Commission actions, state-level delivery fee regulations, and labor law changes affecting gig economy workers. This policy tracking helps us understand why pricing changes occur and predict future trends that affect consumer costs.
User feedback integration ensures our analysis addresses real-world concerns. We collect anonymized data from users about their ordering patterns, pain points, and cost-saving strategies. This qualitative input identifies gaps in our quantitative analysis and highlights emerging issues before they become widespread. We validate user-reported problems through our own testing before including them in our recommendations. This multi-source approach creates comprehensive coverage of the delivery landscape that pure data analysis or anecdotal evidence alone cannot achieve.
| Research Component | Frequency | Data Points/Month | Cities Covered |
|---|---|---|---|
| Price Comparison Orders | Weekly | 200 | 15 |
| Policy Documentation Review | Monthly | 50 | National |
| Delivery Time Testing | Bi-weekly | 100 | 15 |
| User Experience Surveys | Quarterly | 500 | National |
| Fee Structure Analysis | Weekly | 75 | 15 |
| Restaurant Partnership Tracking | Monthly | 300 | 15 |
Transparency and Limitations
We maintain complete independence from food delivery platforms. DeliverEasy receives no compensation, affiliate payments, or promotional consideration from DoorDash, Uber Eats, Grubhub, or any other delivery service. Our recommendations stem purely from data analysis and testing results. This independence allows us to critique platform practices honestly and advocate for consumer interests without conflicts of interest.
Our analysis has inherent limitations users should understand. Pricing data reflects conditions at specific times in specific locations. Your individual experience may vary based on your exact address, local restaurant partnerships, and temporal factors like weather or special events. We provide general patterns and averages, but cannot account for every variable affecting individual orders. Dynamic pricing algorithms change constantly, making real-time price prediction impossible.
Geographic coverage focuses on major metro areas where delivery services operate most extensively. Rural and small-town delivery experiences differ significantly from our urban-focused testing. Restaurant availability, driver density, and fee structures in areas under 50,000 population may not match our documented patterns. We acknowledge this limitation and encourage users in smaller markets to conduct their own platform comparisons for their specific location.
We update our data monthly, but the delivery industry changes weekly. New promotions, fee adjustments, and policy changes occur faster than we can document them comprehensively. Our information represents accurate snapshots at publication time but may not reflect the absolute current state when you read it. We date all data points and recommendations to help users assess information currency. Users should verify current pricing and policies directly with platforms before making subscription commitments or significant ordering decisions based on our analysis.