Use cases

Ten end-to-end Python recipes, from bulk exports and review automation to analytics reports, listings audits, and bulk posting.

Set your API key once before running any example:

bash
export LISTINGSAPI_KEY="your-api-key"

1. List your locations

Connect and print your first page of locations.

01_quickstart.py
import listingsapi client = listingsapi.ListingsAPI() page = client.locations.list(first=5)for loc in page:  print(loc.name, "--", getattr(loc, "city", "N/A"), getattr(loc, "stateIso", "N/A")) print(f"Locations on page: {len(page)}, has_more: {page.has_more}")

2. Bulk export to CSV

Download every location to a CSV using auto_paging_iter().

02_bulk_export_locations.py
import csvimport listingsapi client = listingsapi.ListingsAPI() locations = list(client.locations.list(first=100).auto_paging_iter())print(f"Fetched {len(locations)} locations") fields = ["id", "name", "storeId", "street", "city", "stateIso", "postalCode", "countryIso", "phone"] with open("locations_export.csv", "w", newline="") as f:  writer = csv.DictWriter(f, fieldnames=fields, extrasaction="ignore")  writer.writeheader()  for loc in locations:      writer.writerow(loc.to_dict()) print("Exported to locations_export.csv")

auto_paging_iter() handles all cursor pages automatically. See Locations for the full method reference.


3. Create a location

locations.add() takes every mandatory field as a keyword argument and validates the payload client-side, so a bad request fails fast with a clear message before any network call. Look up a valid sub_category_id with client.subcategories() (use the databaseId).

03_create_location.py
import listingsapifrom listingsapi import ValidationError client = listingsapi.ListingsAPI() subcategories = client.subcategories()dental = [s for s in subcategories if "dentist" in (s.get("name") or "").lower()]sub_category_id = dental[0].databaseId if dental else 1432 try:  result = client.locations.add(      name="Acme Dental",      description=(          "Acme Dental is a family-owned dental practice serving downtown "          "New York for over fifteen years. Our team offers preventive care, "          "cosmetic dentistry, orthodontics, and emergency appointments, with "          "weekend hours and a patient-first approach that keeps every visit "          "comfortable and affordable for the whole family."      ),      sub_category_id=sub_category_id,      country_iso="US",      city="New York",      street="123 Jump Street",      state_iso="NY",      postal_code="10013",      phone="6443859313",      store_id="NYC-001",  )except ValidationError as e:  print(f"Fix the payload: {e}")else:  print(f"Created location: {result.location.id}")

The description must be at least 200 characters; publishers use it as the primary listing copy. client.countries() lists supported country and state ISO codes, and client.plan_sites() shows which directories your plan publishes to.


4. Review monitoring

Scan recent reviews and flag negative ones that need a reply.

04_review_monitoring.py
import listingsapi client = listingsapi.ListingsAPI() page = client.locations.list(first=5) for loc in page:  print(f"\n--- {loc.name} ---")  reviews = client.reviews.list(loc.id, first=10)  if not reviews:      print("  No recent reviews")      continue   for review in reviews:      rating = getattr(review, "rating", "N/A")      author = getattr(review, "authorName", "Anonymous")      site = getattr(review, "siteName", "Unknown")      responded = bool(getattr(review, "responses", None))      flag = " ** NEEDS ATTENTION **" if isinstance(rating, (int, float)) and rating <= 2 else ""      status = "Responded" if responded else "No reply"      print(f"  [{rating}] {author} on {site} ({status}){flag}")

See Reviews for filter options including rating_filters, site_urls, and date ranges.


5. Auto-reply to reviews

workflows.auto_reply_to_reviews() fetches recent reviews, filters by rating and response status, and posts replies from your template in one call. Preview with dry_run=True first. The {rating} placeholder is replaced with each review's star rating.

05_auto_reply_reviews.py
import listingsapi client = listingsapi.ListingsAPI() LOCATION_ID = 16808  # Replace with your location IDTEMPLATE = "Thanks for the {rating}-star review! We appreciate you taking the time." # Preview what would be sentpreview = client.workflows.auto_reply_to_reviews(  LOCATION_ID,  template=TEMPLATE,  min_rating=4,  dry_run=True,)for entry in preview:  print(f"  Would reply to {entry['id']} ({entry['rating']} stars)") # Post the replies for realresults = client.workflows.auto_reply_to_reviews(  LOCATION_ID,  template=TEMPLATE,  min_rating=4,)sent = [r for r in results if r["status"] == "sent"]print(f"Replied to {len(sent)} of {len(results)} matching reviews")

The default min_rating=4 skips negative reviews on purpose: those deserve a personal reply. Handle them individually with reviews.respond():

05b_respond_to_negative.py
import listingsapifrom listingsapi import APIError client = listingsapi.ListingsAPI() negative = client.reviews.list(16808, rating_filters=[1, 2], first=20)for review in negative:  if getattr(review, "responses", None):      continue  try:      client.reviews.respond(          review.interactionId,          "We are sorry to hear about your experience. Please reach out "          "directly so we can make it right.",      )      print(f"Replied to {getattr(review, 'authorName', 'Anonymous')}")  except APIError as e:      print(f"Failed: {e}")

6. Analytics report

Retrieve Google profile analytics and review stats for every location.

06_analytics_report.py
import listingsapi client = listingsapi.ListingsAPI() FROM_DATE = "2026-01-01"TO_DATE = "2026-06-30" all_locations = list(client.locations.list(first=100).auto_paging_iter())print(f"Generating report for {len(all_locations)} locations ({FROM_DATE} to {TO_DATE})\n") for loc in all_locations[:10]:  print(f"--- {loc.name} ---")   google = client.analytics.google(loc.id, from_date=FROM_DATE, to_date=TO_DATE)  if google:      print(f"  Google: {google.to_dict()}")   review_stats = client.reviews.analytics.overview(loc.id, start_date=FROM_DATE, end_date=TO_DATE)  if review_stats:      print(f"  Reviews: {review_stats.to_dict()}")   sites = client.reviews.analytics.sites_stats(loc.id, start_date=FROM_DATE, end_date=TO_DATE)  if sites:      print(f"  Sites: {sites.to_dict()}")   print()

See Analytics for bing() and facebook() profile metrics.


7. Weekly reputation report

workflows.weekly_reputation_report() combines reviews, review analytics, Google and Bing profile analytics, and listings sync status into a single report object.

07_reputation_report.py
import listingsapi client = listingsapi.ListingsAPI() report = client.workflows.weekly_reputation_report(  16808,  start_date="2026-06-29",  end_date="2026-07-05",) summary = report.review_summaryprint(f"Average rating: {summary.get('averageRating')}")print(f"Recent reviews: {len(report.recent_reviews)}") analytics = report.analyticsprint(f"Google: {analytics.get('google')}")print(f"Bing: {analytics.get('bing')}") health = report.listings_healthprint(f"Listings synced: {health.get('synced')}/{health.get('total')} ({health.get('sync_rate')})")

8. Listings health audit

workflows.listings_health_audit() checks premium listings, voice listings, and duplicates for a location and computes a 0-100 health score.

08_listings_audit.py
import listingsapi client = listingsapi.ListingsAPI() page = client.locations.list(first=10)print(f"Auditing listings for {len(page)} locations\n") for loc in page:  audit = client.workflows.listings_health_audit(loc.id)  print(f"--- {loc.name} ---")  print(f"  Health score: {audit.health_score}%")  print(f"  Synced: {audit.synced_count}, issues: {audit.issue_count}")  print(f"  Voice listings: {len(audit.voice)}, duplicates: {len(audit.duplicates)}")  for issue in audit.issues:      print(f"    [{issue.get('syncStatus')}] {issue.get('site')}")  print()

See Listings for the underlying premium, voice, duplicates, and mark-as-duplicate methods.


9. Bulk publish a post

posts.bulk_publish() publishes one post across many locations, defaulting to both Google and Facebook. Location IDs are encoded automatically and the payload is validated client-side before any network call.

09_bulk_publish_post.py
import listingsapi client = listingsapi.ListingsAPI() result = client.posts.bulk_publish(  name="Summer hours 2026",  location_ids=[16808, 16809, 16810],  message="We are open late all summer! Come see us until 9pm, Monday through Saturday.",  media_url="https://cdn.example.com/summer-hours.jpg",  cta_type="LEARN_MORE",  cta_url="https://www.acmedental.com/hours",) post = result.get("socialPost") or {}print(f"Created bulk post: {post.get('id')}")

Pass a dict as message to customize the copy per site:

09b_per_site_message.py
result = client.posts.bulk_publish(  name="July whitening offer",  location_ids=[16808],  message={      "GOOGLE": "20% off teeth whitening this July. Book online today!",      "FACEBOOK": "July special: 20% off teeth whitening. Tap to book your visit!",  },  sites=["GOOGLE", "FACEBOOK"],)

For single-site typed posts, use posts.create_announcement(), posts.create_event(), or posts.create_offer().


10. Google connect flow

Generate an OAuth URL, list connected accounts, and review match suggestions.

10_google_connect_flow.py
import time import listingsapi client = listingsapi.ListingsAPI() # Step 1: Generate an OAuth URL (valid 24 hours)result = client.connected_accounts.connect_google(  success_url="https://yourapp.com/connect/success",  error_url="https://yourapp.com/connect/error",)print(f"Redirect user to: {result.get('url', 'N/A')}") # Step 2: List connected Google accounts. The payload is an offset page:# records[] plus pageInfo, not a bare list.accounts = client.connected_accounts.list(publisher="GoogleAccount", page=1, per_page=20)for acc in accounts.get("records") or []:  account_id = acc["connectedAccountId"]  print(f"{acc['email']} -- status: {acc['status']}")   # Step 3: Kick off the fetch-and-match job for this account.  client.connected_accounts.trigger_matches([account_id])   # Step 4: Matching is asynchronous. Poll the account's details until it  # reports MATCH_COMPLETED, with a bounded number of attempts so a stuck  # job cannot hang the script.  status = None  for _ in range(20):      details = client.connected_accounts.details(account_id)      status = (details.get("details") or {}).get("requestMatchesStatus")      if status == "MATCH_COMPLETED":          break      time.sleep(15)   if status != "MATCH_COMPLETED":      print(f"  matching still {status!r}, skipping for now")      continue   # Step 5: Only now are suggestions populated. Same offset-page shape.  suggestions = client.connected_accounts.suggestions(account_id, page=1, per_page=10)  records = suggestions.get("records") or []  print(f"  Suggestions: {len(records)} matches found")

Reading suggestions before requestMatchesStatus reaches MATCH_COMPLETED returns an empty records array, which is indistinguishable from "no matches exist", so the poll is what makes the recipe correct rather than merely lucky. See Trigger location matches for the status values.

Confirm matches with connected_accounts.confirm_matches(), passing the match-record IDs built from each record's matchedDataDatabaseId and accountType (see Confirm connection matches), and link a specific listing to a location with listings.connect().


Where to go next