A restaurant prospect list should help your team choose the next business to research. For a packaging supplier, that means finding restaurants whose actual offer might suit the product. For an agency, it might mean finding a location whose website deserves a closer look before proposing a project.
This walkthrough follows a fictional supplier of takeaway packaging researching pizza restaurants in three Seattle neighborhoods. The supplier wants a small queue of businesses to investigate, with a reason and a next action attached to every row. These are business prospects, rather than a list of people looking for somewhere to eat.
You can build that queue with G Maps Crawl Online, then work through the evidence in a spreadsheet. All example businesses and decisions below are fictional. No searches or outreach outcomes are represented as live results.
Define a pizza shop prospect before searching
The supplier's brief is specific: research pizza restaurants in Ballard, Fremont, and Wallingford that appear relevant to a conversation about takeaway packaging. Keep each physical location separate, find its official website when available, and check whether its current menu or ordering information supports the proposed fit.
That brief gives the researcher three different questions. Does the listing describe the right kind of business? Does the business serve the intended area? Does its current website provide evidence that packaging is relevant?
Only the first two can be partly answered from listing data. A pizza category does not establish takeaway volume, purchasing authority, or a need to change suppliers. Those become research questions. Write them into the brief instead of turning them into assumed facts.
Choose a stopping point too. For this example, finish the three searches and review the returned locations before expanding to other neighborhoods. The task is complete when the team has an explained queue, even if several records remain on hold.
Run a small neighborhood search grid
Use one clear business type and location per keyword:
pizza restaurants in Ballard Seattle
pizza restaurants in Fremont Seattle
pizza restaurants in Wallingford Seattle
Open your workspace, select the Online scraper, and check the available allowance and keyword limits. Paste the supported number of keywords into a new scrape. The dashboard creates a durable job whose status you can return to later. Follow the Online scraping guide for the current controls.
When a keyword completes, preview its records before downloading. Look for useful category and location matches. A search can also return a grocery store, a nearby restaurant outside the intended area, or a business whose pizza offering is secondary. Keep those observations when deciding whether another search would be worthwhile.
Use an eligible paid Online plan for CSV, Excel, or JSON downloads. Online, Extension, and API allowances are separate. Teams building a programmatic workflow can follow the API quickstart; this example uses an Online job and export.
The search does not establish a complete count of pizza shops in the area. Record the actual queries and completed coverage so someone else can understand the sample.
Keep the fields that explain each restaurant
For a structured business record, listing fields live under record.place. In a spreadsheet export, the column labels are friendlier. This mapping keeps the useful connection between the two.
| Research purpose | Structured field | Spreadsheet column |
|---|---|---|
| Identify the restaurant | place.name | Name |
| Check the listing's business types | place.category, place.categories | Categories contains the category list |
| Check the location | place.address.formatted | Fulladdress |
| Find the public site and phone | place.website, place.phone | Website, Phone |
| Add listing context | place.rating, place.reviewCount | Average Rating, Review Count |
| Recognize the same location again | place.placeId, place.cid | Place Id, Cid |
| Return to the listing | place.mapsUrl | Google Maps URL |
| Preserve the source observation date | place.observedAt | Retain in the full structured JSON |
Add your own columns for source_query, research_area, fit_status, evidence_url, reviewed_at, and next_check. These are research annotations you create, rather than built-in qualification fields.
Keep a full JSON export alongside the spreadsheet when you need observation timestamps and field states. Selecting individual export fields produces a different shape from keeping all structured fields. The export guide explains that distinction.
Separate duplicate listings from separate locations
Neighboring searches can return the same restaurant. Group records by place.placeId when available and preserve the source queries that found each one. A matching place.cid can support a second identity check. If stable identifiers are missing, compare the address and source listing manually rather than merging on the name.
Two branches with the same brand name can have different addresses, websites, and purchasing arrangements. Keep both locations in the location sheet. If your team later confirms shared purchasing, add a separate organization grouping and record the evidence for it.
The same principle applies to independence. A local-sounding name does not prove a restaurant is independently owned. Check the official location pages if ownership or chain affiliation matters to the supplier's offer.
Make a decision from a specific piece of evidence
The following fictional rows show the difference between a candidate and a qualified next step. They are examples of reasoning, not a returned dataset.
| Illustrative business | Available evidence | Research decision |
|---|---|---|
| Fictional Canal Pizza | Pizza category, in-area address, website returned | Open the official menu and ordering page; check whether takeaway packaging is relevant. |
| Fictional Hearth Kitchen | Restaurant category; website field empty | Hold for identity and website research before deciding whether it belongs in the pizza segment. |
| Fictional Harbor Pizza, two branches | Similar names, different addresses and Place Id values | Keep two location records; investigate whether purchasing is shared. |
| Fictional Market Pantry | Grocery category with a pizza counter mentioned on the website | Move to an adjacent-segment tab unless the brief also includes grocery foodservice. |
Give each row a status such as review next, needs evidence, outside brief, or duplicate record. These statuses describe your research decision. They do not imply that a restaurant is interested in buying.
For Canal Pizza, a useful note might read: “Official menu includes takeaway pizza; check current packaging format and the appropriate business contact.” Avoid changing that into “needs new packaging.” The first statement describes evidence and an unanswered question. The second invents a sales need.
Check contact sources after checking fit
A general restaurant email list can be tempting to use immediately. First establish that each business belongs in the intended segment. Then review its available contact paths.
Paid Online plans can add email and social enrichment from business websites. In structured data, place.emails contains available email values, while place.contacts can preserve each contact's sourceUrl, observedAt, and method. Keep the source attached to the address when building your research sheet.
An address published on a restaurant website may belong to bookings, customer support, or a shared office. It does not establish a purchasing manager's identity or mailbox deliverability. If a supplier-inquiry page exists, record that route rather than assigning an unverified job title to a generic inbox.
An empty website or email value remains unknown. Where available, place.fieldStates and place.enrichmentState help distinguish an unsuccessful check from information that was found. See the business-field guide before treating missing contact data as a disqualification.
Hand off a short list with an evidence trail
Before handing the sheet to sales, recheck the chosen businesses' current official websites and listings. Save your review date separately from place.observedAt. A response received today can include older observations: query responses have a 24-hour reuse window, and website contact checks have a seven-day window. Complete Maps details can be reused for up to 730 days.
The handoff should include the searched areas, duplicate decisions, unresolved records, and a concrete next action for each candidate. State which keywords completed if an Online job was partial. A researcher should be able to open the evidence link and understand the decision without rerunning the whole project.
Start with one restaurant category and a few areas you can actually review. Open G Maps Crawl Online to build your first search, then use the business research guide to turn the results into a list your team can explain.
