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Looker Studio

Build Looker Studio reports on Onym's four flat datasets.

Ask Claude Ask ChatGPT
Replace YOUR_API_KEY with a key from Settings → API and MCP. Keys are shown once when you create them.

Onym publishes four flat, typed datasets made for BI tools. You can bring them into Looker Studio through Google Sheets in a few minutes, or with Onym's community connector.

Dataset One row per
visibility Run date × surface × brand
prompts Prompt
sources Cited site
answers Answer

Option 1: through Google Sheets

The quickest route, and it needs no code.

  1. In a new Google Sheet, enter the formula below in cell A1. Use a dedicated read key: it sits in the sheet.
  2. In Looker Studio, choose Create → Data source → Google Sheets and pick the sheet.
Sheets
=IMPORTDATA("https://getonym.com/api/v1/domains/acme.example/datasets/visibility.csv?range=90d&key=YOUR_API_KEY")

Sheets refreshes IMPORTDATA about once an hour. See Google Sheets and CSV for more.

Option 2: the community connector

The connector fetches the datasets directly and maps their types for Looker Studio. It's an Apps Script project you deploy once in your own Google account.

  1. Go to script.google.com and create a project named "Onym connector".
  2. In Project settings, tick Show "appsscript.json" manifest file in editor.
  3. Replace Code.gs and appsscript.json with the two files below.
  4. Choose Deploy → New deployment, pick Add-on → Looker Studio connector, and copy the deployment ID.
  5. Open https://lookerstudio.google.com/datasources/create?connectorId=DEPLOYMENT_ID, paste a read key, then pick a domain, dataset, range and question type.

Field dictionary

visibility

Field Type Description
run_date date The run's date (UTC)
surface text The surface's name, like ChatGPT
brand text You or a tracked competitor
is_you boolean Whether the row is your brand
answers number Answers on that surface that day
named number Answers that named the brand
rate percent named / answers
low_95, high_95 percent The 95% range

prompts

Field Type Description
prompt text The question
topic text Its topic
status text active, suggested or archived
answers, named number Answers, and answers naming you
visibility percent named / answers
avg_position number Your average rank when named
top_competitor text The competitor named most

sources

Field Type Description
domain text The cited site
owner text Who owns it, or its kind
type text Yours, Competitor, UGC & community, Reviews & directories, Reference, Editorial & other
answers_citing number Answers citing it
share_of_answers percent Those answers as a share of all answers
prompts number Prompts whose answers cite it
named_you number Answers citing it that name you
gap_answers number Answers citing it that name a competitor and not you

answers

Field Type Description
run_date date The run's date
prompt, topic text The question and its topic
surface text Where it was asked
named_you boolean Whether you were named
your_position number Your rank, if named
competitors text Tracked competitors named, separated by ;
citations number How many URLs it cited

Report recipes

  • Visibility trend: time series of rate by run_date, breakdown brand. Add a surface filter control.
  • You vs. the field: bar chart of rate by brand, filtered to the last run date.
  • Where you're missing: table of sources sorted by gap_answers.

Set rate to aggregate as a weighted ratio, not an average: create a calculated field SUM(named) / SUM(answers). Averaging rates gives a day with three answers as much weight as a day with three hundred.