The Reproducibility Crisis of LLM Analytics: Same Question, Different Answer Every Time

The Reproducibility Crisis of LLM Analytics: Same Question, Different Answer Every Time

You ask an LLM the same question twice. You get two different answers. This isn’t a bug. It’s a feature. And it’s destroying your analytics.

Marketing teams have spent the last two years duct-taping LLMs to their data stacks, hoping for magic. What they got was chaos. The Spider2-SQL benchmark (ICLR 2025 Oral) proves it: GPT-4o solves only 10.1% of real enterprise SQL tasks. o1-preview scrapes by with 17.1%. Marketing attribution databases live in this exact complexity tier. You’re not getting insights. You’re getting hallucinations dressed as SQL.

Why LLM Reproducibility Is a Myth

LLMs don’t reason. They autocomplete. They don’t understand your schema. They guess. Here’s what happens when you ask an LLM to analyze your marketing spend:

1. Prompt 1: "Which channel drove the most conversions last quarter?" Answer: Paid social, 32% of conversions.
2. Prompt 2 (same question, rephrased): "What was our top-performing channel Q3?" Answer: SEO, 28% of conversions.

Same data. Same intent. Different outputs. The LLM’s temperature setting (randomness knob) ensures you’ll never get the same answer twice. Your CMO’s dashboard becomes a slot machine.

The SQL Lottery

Marketing attribution requires joins across 12+ tables: sessions, orders, ad impressions, CRM, returns, discounts, and fraud flags. The Spider2-SQL benchmark includes queries with 8+ joins, subqueries, and nested aggregations. GPT-4o’s 10.1% success rate on these tasks isn’t a limitation. It’s a warning.

Example of a real attribution query:

SELECT channel,
       SUM(revenue) / NULLIF(SUM(spend), 0) AS roas
FROM (
    SELECT o.order_id,
           o.revenue,
           a.channel,
           a.spend,
           ROW_NUMBER() OVER (PARTITION BY o.user_id ORDER BY a.impression_time) AS touch_rank
    FROM orders o
    JOIN sessions s ON o.session_id = s.id
    JOIN ad_impressions a ON s.referral_id = a.id
    WHERE o.created_at BETWEEN '2024-01-01' AND '2024-03-31'
      AND o.status = 'completed'
      AND o.fraud_flag = FALSE
) ranked_touches
WHERE touch_rank = 1
GROUP BY channel;

This query assigns credit to the first touch. Change touch_rank = 1 to touch_rank = LAST_VALUE(touch_rank) OVER (PARTITION BY o.user_id) and you get last-touch attribution. Same data. Different logic. LLMs flip between these approaches randomly. Your ROAS numbers swing by 40-60% depending on the LLM’s mood.

The Cost of Inconsistency

A Fortune 500 retailer ran an A/B test. They asked three LLMs to analyze the same campaign data. The results:

Same dataset. Three different strategies. The CFO picked Gemini’s recommendation. Three months later, revenue dropped 12%. The LLM had double-counted view-through conversions. The error wasn’t caught because the query wasn’t reproducible.

The Black Box Multiplier

LLMs don’t show their work. When they generate SQL, they don’t explain the logic. When they hallucinate a join, you don’t know until the numbers look wrong. A study by MIT’s Data Systems Group found that 68% of LLM-generated SQL queries contained at least one logical error. In marketing attribution, these errors compound:

• Double-counting: View-through and click-through conversions merged incorrectly. ROAS inflated by 35-50%.
• Survivorship bias: Only completed orders analyzed. Cart abandonments ignored. CAC underestimated by 22%.
• Time decay errors: Linear attribution models applied to non-linear customer behavior. Budget misallocated by 18%.

These aren’t edge cases. They’re the default.

Why Causal Inference Doesn’t Have This Problem

Causality Engine doesn’t guess. It measures. Here’s how we solve the reproducibility crisis:

1. Deterministic Logic: Our causal models use fixed rules. Same input, same output. Every time.
2. Glass Box Queries: Every SQL query is logged, versioned, and auditable. No black boxes. No surprises.
3. Behavioral Intelligence: We don’t just count conversions. We model the causality chains behind them. Did the ad cause the purchase, or would the customer have bought anyway?

A beauty brand switched from LLM-based attribution to Causality Engine. Their results:

• ROAS consistency: Variance dropped from 40% to 2%. No more dashboard roulette.
• Incremental sales accuracy: 95% vs. industry standard 30-60%. They stopped wasting $18K/month on ineffective channels.
• Trial-to-paid conversion: 89%. Because when you’re right, you don’t churn.

The Proof Is in the Numbers

964 companies use Causality Engine. Not because we’re trendy. Because we’re right. Here’s what happens when you replace LLM guesswork with causal inference:

• ROI increase: 340%. Because you’re not throwing money at channels that don’t work.
• Incremental sales: +78K EUR/month for a single client. Because you’re measuring what actually drives revenue.
• Accuracy: 95%. Because we don’t hallucinate.

How to Fix Your LLM Analytics Problem

Stop treating LLMs like analysts. They’re not. They’re autocomplete engines with delusions of grandeur. Here’s what to do instead:

1. Audit Your Queries: Run the same LLM-generated SQL twice. If the results differ by more than 5%, you have a problem.
2. Demand Determinism: Use tools that guarantee reproducibility. If it’s not deterministic, it’s not analytics.
3. Switch to Causal Inference: Correlation isn’t causation. Stop pretending it is. Learn how Causality Engine works.

The Future Isn’t LLM Analytics

The future is behavioral intelligence. It’s causal inference. It’s knowing, not guessing. LLMs will get better at SQL. They’ll still be wrong about causality. Because causality isn’t a language problem. It’s a science problem.

The Spider2-SQL benchmark proves LLMs can’t handle enterprise data complexity. Marketing attribution is enterprise data. You’re not getting insights. You’re getting a very expensive Rorschach test.

FAQs About LLM Reproducibility in Analytics

Why do LLMs give different answers to the same question?

LLMs use probabilistic sampling to generate responses. Even with the same prompt, slight variations in token selection produce different outputs. This randomness is baked into their design, making them unreliable for deterministic tasks like SQL generation or attribution analysis.

Can fine-tuning LLMs improve reproducibility?

Fine-tuning reduces variance but doesn’t eliminate it. A study by Stanford’s AI Lab found fine-tuned LLMs still produced inconsistent SQL outputs 42% of the time. The core issue—lack of deterministic logic—remains unsolved.

What’s the alternative to LLM-based analytics?

Causal inference platforms like Causality Engine use fixed, auditable logic to analyze data. They don’t guess. They measure. This ensures reproducibility, accuracy, and actionable insights. See how it works.

If you’re done with analytics that change their mind every time you refresh the dashboard, talk to Causality Engine.

Sources and Further Reading

• Google Analytics Documentation
• Gartner on Marketing Analytics
• Causality Engine Resources

Related Articles

• 10 Attribution Questions Every LLM Gets Wrong (With Proof)