An AI analyst for food delivery data: ask, get the list
An AI analyst for food delivery data lets you ask a question in plain words and get back a table, a chart or an outlet list you can export; in DashONE it answers from 15M+ delivery storefronts on 100+ platforms in 60+ countries. You ask the question you would ask a colleague, and the answer shows the outlets behind it. No dashboard to learn: ask today and export the list.
The trusted intelligence layer for teams selling to Hospitality & Retail
Ask the question the way you would ask a colleague
Dashboards answer the questions someone thought of when they built them. The question your manager asks on a Tuesday afternoon is usually a different one: a specific city, a specific competitor, one type of outlet, one app. Answering it means building a filter set, exporting, pivoting and checking, or asking an analyst and waiting.
The AI analyst takes the question as you would say it:
- 'Which quick service restaurants in our biggest city miss combos with our brand?'
- 'Where is our main competitor listed but we are not, on one app, in our second city?'
- 'How did our availability in burger outlets change over the last three refreshes?'
- 'Which outlets of our largest burger chain account show our product without an image?'
It works on the same delivery data as the dashboards, so the answer uses the same outlets, brands and definitions your reports do.
What comes back
The form of the answer follows the question:
| You ask for | You get |
|---|---|
| A list ('which outlets...') | A table of outlets with city, app and the fields that matter, ready to export |
| A comparison ('us versus our main competitor') | A table or bar chart by city, app or outlet type |
| A trend ('how did it change') | A line chart across refreshes, with the dates on the axis |
| A count or share ('how many', 'what share') | The number, with the outlets behind it one click away |
Every answer states what it was computed on: the brand, the cities, the apps, the outlet types and the refresh date. If the question needs something the data does not hold, the answer says so rather than guessing.
How eCommerce teams use it in a week
Three moments come up again and again:
- Before an account call. 'Show me the burger chain's locations in our biggest city that miss our Zero SKU' gives the list to bring to the call.
- During the monthly report. Follow-up questions on a number that moved, such as 'which outlets explain the drop in our second city?', without rebuilding the report.
- When leadership asks. A one-off question about a competitor or a new city gets a dated answer the same day, not a ticket in an analyst's queue.
Answers can be refined in the conversation: narrow to one app, add a competitor, split by outlet type, then export. For the standard views behind these questions, see listing and availability tracking and combo and meal deal tracking.
Worked example: from question to call list
The eCommerce manager of a soft drink brand asks: 'Which quick service restaurants in our biggest city miss combos with our brand?'
The answer: 39 quick service outlets in the city sell combos that include a soft drink, list the brand's products, and name the brand in none of their combos.
She follows up in the same conversation:
| Follow-up question | Answer |
|---|---|
| 'Which of them name our main competitor in a combo?' | 24 outlets |
| 'Split by app' | 15 on one app, 9 on a second app |
| 'Which belong to a chain?' | 10 belong to one burger chain, 14 are independents |
The 10 chain outlets go to the key account manager; the 14 independents go to the field team as a call list, exported with outlet name, address and app.
Why you can trust the answer
An answer is only useful if you can check it. Each one shows the filters and definitions it used, the refresh date, and the outlet list behind any count, so a number can be traced to named outlets on a named app. Brand shares count the menus where your products are recognised, as in the dashboards: an item written too vaguely to tie to a brand is not counted for anyone.
Frequently asked questions
What can I ask the AI analyst?
You can ask about listings, availability, combos, prices, competitors and listing quality on delivery apps, for the cities, apps, outlet types and accounts in your workspace. Questions about outlets, brands and changes over time work best.
How do I check an AI analyst answer?
Every answer shows the filters, definitions and refresh date it used, and links each count to the outlet list behind it, so you can check it. When a question falls outside the data, it says so.
Can I export the answer?
Yes. Any answer that is a list of outlets can be exported, with outlet name, city, app and the fields from the question.
Do I need to know how to write queries?
No. You type the question in plain words, and refine it the same way, for example 'only this app' or 'split by city'.
Is it the same data as the dashboards?
Yes. The analyst answers from the same outlets, brands and definitions as the DashONE dashboards, refreshed monthly.
Can I ask in languages other than English?
Ask about the languages your team needs in the demo; questions are typed in plain words and outlet and brand names are kept as they appear on the menus.
Related reading
The business case for your Head of eCommerce
If you are asked why the team needs this, three points:
- Answers without a queue: managers get dated, outlet-level answers themselves, and analysts spend their time on the questions that need them.
- Same numbers as the reports: the analyst answers from the same data and definitions as the dashboards, so no two versions of the truth.
- From question to action: every answer ends in an exportable list that a field or account team can work from.