Menu modifier data captures the add-ons, options, and combo choices attached to each item on a delivery app menu. For example, it shows that extra cheese costs more, or that a large size adds to the price.

Many menu datasets stop at the base price. However, customers often pick sizes, sides, and extras, so the base price tells only part of the story.

This guide explains how DoorDash, Uber Eats, Deliveroo and Swiggy structure modifiers. It also shows how to capture them cleanly.

What Is Menu Modifier Data?

Menu modifier data describes how customers can change an item before they order it. DoorDash defines a modifier as “an option for customers to add to or change a specific item”.

Modifiers sit inside groups. For instance, Uber Eats says a modifier group holds items chosen “as a customization under a parent item”.

Each group then carries its own rules:

• Required or optional: a burger may need a side, while toppings stay optional.
• Minimum and maximum: a customer might pick exactly one size, or up to five toppings.
• Repeatable options: some apps let customers add the same option more than once.
• Free quantity: a meal may include two free sauces, and then charge for more.
• Defaults: some options come pre-selected, such as a regular size.
• Price change: each option adds a set amount, or nothing at all.

In short, menu modifier data turns a flat menu into a set of rules and prices. That’s what lets you work out what a real order costs.

Why Menu Modifier Data Matters

Base prices can mislead. For example, a “build your own bowl” may list a low base price but require a paid protein. So the cheapest real order costs more than the listed price.

Modifiers also drive revenue. DoorDash calls them a chance to “up-charge, add additional items and increase ticket size”. In fact, DoorDash says french fries were its top US item in 2023.

Pricing on apps matters, too. DoorDash studied more than 4,500 restaurants in 2023. It found that restaurants with marked-up menus saw up to 37% fewer sales and 78% lower reorder rates. So when you compare app and in-store prices, check add-ons as well as main items.

As a result, menu modifier data helps teams answer questions that base prices can’t:

• What does a typical order really cost on each app?
• Which add-ons do rivals charge for, and how much?
• Which brands use combos to raise order value?

How Delivery Apps Structure Menu Modifier Data

Each app uses a similar idea but different rules. Therefore, a scraper built for one app rarely fits another without changes.

Uber Eats

Uber Eats organises menus into categories, items, and modifier groups. Its example menus set minimum and maximum selections for each group. They also show a “charge above” rule, where extra sauces cost money after a free amount. Modifier groups can nest up to six levels deep, and combos can list bundled items.

DoorDash

DoorDash calls groups “extras” and choices “options”. In its pizza menu guide, crust sits under each size, and toppings sit under the crust. Options can also be marked as defaults. In addition, restaurants can set separate modifier prices for pickup and delivery.

Deliveroo

Deliveroo’s Menu API gives each modifier group a minimum and maximum selection. It also includes a repeatable flag and group types, such as “add-ingredient” and “bundle-item”.

Swiggy

Swiggy uses variant groups and add-ons. According to UrbanPiper’s integration guide, prices can depend on a combination of variants. So an item’s listed price can be zero when its variants carry the price.

Finally, POS systems shape what apps show. For example, Toast advises keeping nested modifiers only two layers deep for delivery app syncs.

How to Capture Menu Modifier Data, Step by Step

Follow these steps to capture menu modifier data that you can trust:

1. Choose apps and stores. Pick the brands, cities, and store addresses you need.
2. Collect the full menu. Capture every category and item, plus its base price.
3. Open each item’s options. Then record every modifier group, option, and price change.
4. Keep the rules. Save whether each group is required, plus its minimum and maximum.
5. Follow nested paths. Record each child group under its parent option, level by level.
6. Capture combos separately. Store the combo price and every included item.
7. Note stock status. Flag options that are sold out or hidden at that store.
8. Repeat on a schedule. Finally, re-collect weekly or daily, and flag what changed.

Store-level collection matters here. That’s because the same brand can price add-ons differently by city or even by store.

Fields to Capture in Menu Modifier Data

A clean data model keeps menu modifier data easy to query. For example, these fields work across most apps:

In addition, keep pickup and delivery prices in separate fields where an app shows both. Then you can compare channels without mixing them up.

Pitfalls When Collecting Menu Modifier Data

Menu modifier data is harder to collect than base prices. So watch for these common problems:

• Deep nesting: Uber Eats allows up to six levels. As a result, flat tables lose the link between parent and child options.
• Size-based prices: an add-on may cost more on a large item than a small one. Therefore, store the price for each size.
• Free allowances: two sauces may be free, and the third costs extra. So capture the free quantity as well as the price.
• Zero base prices: on some apps, variants carry the price. Instead of reading the item price alone, combine it with the cheapest required choice.
• Combos versus singles: a combo may include a drink that also sells alone. Keep both, and link them by item ID.
• Hidden or sold-out options: some options disappear at certain stores or times. So record availability with each snapshot.

Also, collect only public menu information. Don’t log in as a customer or collect personal details.

Who Uses Menu Modifier Data

For example, these teams rely on menu modifier data:

• Restaurant brands: benchmark add-on prices and combos against rivals by city.
• Pricing and revenue teams: check whether add-on prices match across apps and in-store menus.
• Delivery and ordering platforms: spot missing or broken modifier groups on partner menus.
• Food-tech and AI ordering apps: need full option trees to place correct orders.
• CPG and beverage brands: see how often their drinks and sides appear in combos.
• Analysts and investors: compare real order costs across apps and markets.

How FoodSpark Helps

FoodSpark collects public menu data from delivery apps, including add-ons and customisation options where apps show them. So you can track menu modifier data without building your own scrapers.

• Sources: apps such as DoorDash, Uber Eats, Grubhub, Deliveroo, Swiggy and Zomato.
• Coverage: by city, postcode, or radius.
• Refresh: one-time, weekly, daily, or hourly.
• Formats: CSV, JSON, XML, REST API or webhook, with delivery to AWS S3, BigQuery, Snowflake or Google Sheets.
• Compliance: public data only, with no personal user data, payment information, or non-public content.

To learn more, see our restaurant menu data scraping service and USA restaurant menu dataset. You can also explore our DoorDash API and Uber Eats API pages. Then check food data API pricing for a free 100-row sample.

Menu Modifier Data: FAQs

What is menu modifier data?

It is the data on how customers can change a menu item, such as sizes, add-ons, and combos. It also includes each option’s price and selection rules.

What is the difference between a modifier and a variant?

A variant usually replaces the item’s base form, such as a size. By contrast, a modifier adds to or changes the item, such as extra cheese.

How deep can modifiers be nested?

It depends on the app. Uber Eats’ example menus go six levels deep. However, Toast advises two layers for delivery app syncs.

Why is a base price not enough for price comparison?

Many items need paid choices, such as a protein or a size. So the real price of an order is often higher than the base price.

How often should I collect menu modifier data?

Weekly suits most brands. However, daily checks help during menu launches and promotions, because add-on prices can change quickly.

How can I get menu modifier data from FoodSpark?

FoodSpark offers a free 100-row sample in CSV or JSON. Just tell us the apps, brands, and cities you need.