LLM Bias in Marketing Data: Does Wording Steer the Answer?
Yes, it can. Studies report that assistants tend to agree with the person asking and that answers shift with option order and prompt format. Flip your framing and reorder your data to test yours.
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By Joris van Huët, Founder & CEOUpdated 4 min read
It can, in ways published studies have reported and you can test yourself. In a 2023 study, five AI assistants consistently showed sycophancy: answers that match the user's views over truthful ones. Answers also shift with option order and prompt format, and the data you paste has its own bias. None of these studies tested attribution, so the check below uses your own export.
Agreement. Sharma and colleagues (arXiv preprint, 2023) tested five AI assistants on four free-form text-generation tasks and found that all five consistently showed sycophancy. In the human preference data they analysed, "when a response matches a user's views, it is more likely to be preferred". Models trained on human feedback can learn that agreeing gets rewarded.
Position. Zheng and colleagues (ICLR 2024, peer-reviewed) analysed 20 LLMs on three benchmarks and found that models "prefer to select specific option IDs as answers", so moving the options moves the answer. A list of channels in a prompt is not a multiple-choice test, but it is worth checking whether the answer follows the order.
Format. Sclar and colleagues (ICLR 2024, peer-reviewed) found that several widely used open-source LLMs are "extremely sensitive to subtle changes in prompt formatting in few-shot settings", with differences of up to 76 accuracy points for LLaMA-2-13B.
These are lab tasks, mostly multiple choice and classification. No source cited here tested whether wording, order or format changes an attribution answer on marketing data.
Is the bias in the model or in your data?
Two things get called LLM bias: how the model reads your question, above, and what is in your table. The second is the one that moves budget. Lewis and Rao (Quarterly Journal of Economics, 2015, peer-reviewed) analysed twenty-five large field experiments with major U.S. retailers and brokerages and concluded that "selection bias, due to the targeted nature of advertising, is a crippling concern for widely employed observational methods".
Retargeting shows ads to people who already visited your store, so some of their orders may have happened without the ad, and the table cannot show how many. A model that summarizes that table reports its credit faithfully, bias included, and no prompt removes the bias. A randomized holdout avoids it: Lewis and Rao describe randomized control trials as "injecting new, unbiased information into the market".
How do you test your own prompts?
Run these on one export, each in a fresh chat, and keep the answers side by side:
- Flip the framing. Ask "Why is Meta driving more revenue than email?" and then "Why is email driving more revenue than Meta?" Then ask neutrally: "Which drove more revenue, Meta or email? Show the totals and the query."
- Reorder. Paste the same table with the channels in reverse order, or shuffle the rows, and ask the neutral question again.
- Change the format. Give the same data as CSV, then as a markdown table.
- Recompute the ranking yourself with a pivot of the export.
Pass: every variant gives the same ranking and the same totals, and they match your pivot. Fail: the answer follows the wording, the order or the format, or the two framed questions each get a confident explanation for opposite conclusions. A fail means that setup should not feed a budget decision until the answer stops moving. A pass means the answer is stable, not that it is causal: the data bias above is untouched.
LLMs are fine for drafting the pivot query and explaining a finished table, where you can see whether the answer moves.
Sources, 30 September 2026: Towards Understanding Sycophancy in Language Models (Sharma et al., arXiv preprint, 2023); Selection bias in LLM multiple-choice answers (Zheng et al., ICLR 2024); Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design (Sclar et al., ICLR 2024); The Unfavorable Economics of Measuring the Returns to Advertising (Lewis and Rao, Quarterly Journal of Economics, 2015).
Frequently asked questions
Are LLMs biased when they analyze marketing data?
They can be in ways studies have reported: agreement with the person asking, the position of answer options and the format of the prompt. No source cited here tested attribution questions, so ask your question framed both ways, reorder your data and recompute the totals.How do I stop an LLM from just agreeing with my assumptions?
Ask neutral questions, ask for the evidence against your hypothesis, and flip the framing to see whether the conclusion flips too. Then recompute the key figure yourself. An answer that follows your wording tells you what you asked, not what the data says.Is my marketing data itself biased?
In a specific way: ads are targeted at people already likely to buy. A peer-reviewed analysis of twenty-five field experiments (Lewis and Rao, 2015) called the resulting selection bias a crippling concern for observational methods. A holdout test addresses it.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- AttributionAttribution identifies user actions that contribute to a desired outcome and assigns value to each. It reveals which marketing touchpoints drive conversions.
- CausationCausation is the relationship where a change in one variable directly causes a change in another.
- Confirmation BiasConfirmation Bias is the tendency to search for, interpret, favor, and recall information that confirms one's prior beliefs. In marketing, it reinforces a customer's decision to buy a product.
- CorrelationCorrelation is a statistical measure showing a relationship between variables; it does not imply causation.
- ExperimentsExperiments are scientific procedures that test hypotheses or demonstrate facts. In marketing, experiments like A/B tests determine the causal effect of campaign changes, enabling data-driven decisions.
- Holdout TestA holdout test is an experiment that keeps a random part of an audience from seeing a campaign and compares its results with those of the part that could see it. The held-out part is the control group, so the gap in outcomes estimates what the campaign added.
- RetargetingRetargeting is online advertising that targets users who have previously interacted with your website or content. Attribution analysis shows the causal role of retargeting in driving conversions and improving ad spend.
- Selection BiasSelection Bias occurs when data points selected for analysis do not represent the target population. This leads to distorted findings about marketing campaign impact.