Should You Use an LLM to Analyze Your Attribution Data?

Yes for drafting the query and explaining the output, not for taking the number on trust. The Spider 2.0-Snow leaderboard now lists a top score of 96.70 on 547 text-to-SQL examples that come with prepared metadata and documentation, while a March 2026 benchmark of data agents across four database systems reports 38% for the best frontier model. Your export is neither benchmark, so run the query yourself and reconcile the total to Shopify.

What do SQL and data-agent benchmarks measure?

Spider 2.0 (ICLR 2025 Oral, peer-reviewed) has 632 real-world text-to-SQL workflow problems. The project's leaderboard lists far higher scores for newer systems. Its Snowflake track has Genloop's Sentinel Agent v2 Pro at 96.70, and the top five entries score between 90.49 and 96.70. The same table lists the benchmark's own baseline agent at 12.98 with GPT-4o and 23.58 with o1-preview, so the top scores come from named systems built around models, not from a model alone. The same page's introduction still prints 17.1% for o1-preview and 10.1% for GPT-4o, so if you've seen 10.1% quoted, it comes from that introduction, not from the leaderboard table. The page adds that "the scores may change slightly over time".

No public measurement is cited here of an LLM answering attribution questions on a Shopify or GA4 export.

What do those numbers not tell you about your export?

Each benchmark task has a known right answer; your question doesn't, and a correct count by channel is still credit, not cause. The Snowflake track comes with prepared documentation, while a pasted export has column headers and little else. Long inputs add a third problem: Liu and colleagues (TACL 2023, peer-reviewed) found that performance "significantly degrades when models must access relevant information in the middle of long contexts", and OpenAI's help page says a file "may upload successfully but still be too large, complex, image-heavy, or poorly structured for complete analysis".

Is it safe to paste order data into an LLM?

Not as it comes. The European Commission says "personal data is any information that relates to an identified or identifiable living individual", and lists a name, a home address and an email address as examples. Shopify's order export has columns for email, phone, names and addresses. If a model provider handles them on your behalf, the Commission's description of a data processor applies: it "processes personal data on behalf of the controller, on that controller's documented instructions". OpenAI's help page says available data controls "depend on whether you are signed in, your plan, and your workspace settings", and that if you give feedback on a response, "the entire conversation associated with that feedback may be used to train OpenAI models".

How do you use an LLM on attribution data without trusting it?

Five steps, in this order:

1. Strip personal data. Delete the email, phone, name and address columns. Keep date, channel, region, orders and revenue.
2. Send a daily table by channel, not raw rows. Ask the model to state the row count and date range it used. Pass: they match your file. Fail: they don't.
3. Ask for the query or code and run it yourself. Pass: same numbers. Fail: a difference you can't trace.
4. Reconcile to Shopify. Orders and revenue for the same dates should match Shopify's reports, apart from causes you can name, such as test orders, which Shopify says are in the orders export but not in its Sales reports. Pass: they match. Fail: an unexplained gap.
5. Repeat in a fresh chat and keep a record. OpenAI's API documentation says Chat Completions "are non-deterministic by default", so a ranking that flips is a draft. Save the prompt, the model name, the date, the query and the result together.

Where it helps: drafting the query, explaining a column, summarizing the finished table.

Sources, 30 September 2026: Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows (ICLR 2025); Spider 2.0 leaderboard (Spider 2.0 project); Can AI Agents Answer Your Data Questions? (arXiv, March 2026); Lost in the Middle (TACL 2023); Data analysis with ChatGPT, Data controls in ChatGPT and Advanced usage (OpenAI); Data protection explained (European Commission); Exporting orders and Sales discrepancies (Shopify Help Center).

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