Distorted A/B testing results showing interference bias in online pricing experiments.

Are False-Positive Pricing Experiments Costing Your Business? How to Avoid Interference Bias

Samir D’Costa in Business & Economy August 2025 • 4 min read.

"Uncover the hidden dangers of interference bias in online retail pricing strategies and discover how to implement more reliable experiments for sustainable growth."

In the fast-paced world of online retail, making informed decisions about pricing is crucial for success. Many businesses rely on A/B testing, a seemingly straightforward method of experimenting with different price points to identify what resonates best with consumers. However, a growing body of research suggests that these tests may not be as reliable as we once thought. Interference bias, a sneaky phenomenon that can significantly skew results, is threatening the validity of online pricing experiments.

Interference bias arises when the treatment applied to one product or customer influences the outcome of another. In the context of pricing, this typically occurs because customers don't operate in a vacuum. They compare prices, seek out deals, and substitute products based on perceived value. When pricing experiments don't account for these interconnected behaviors, the results can be misleading, leading to false positives and potentially costly misinvestments.

This article delves into the insidious nature of interference bias in online retail pricing experiments. We'll explore how it manifests, why it's often overlooked, and, most importantly, what steps you can take to mitigate its impact. By understanding and addressing interference bias, you can make more confident decisions about pricing strategies and pave the way for sustainable, profitable growth.

The Illusion of A/B Testing: How Interference Bias Distorts Pricing Results

Distorted A/B testing results showing interference bias in online pricing experiments.

At its core, A/B testing aims to isolate the impact of a single variable – in this case, price – on a specific outcome, such as sales or revenue. The underlying assumption is that the treatment group (those seeing the new price) and the control group (those seeing the original price) are independent of each other. However, this assumption often breaks down in the real world.

Imagine a scenario where you're testing a price reduction on a popular brand of running shoes. Customers in the treatment group see the lower price, while those in the control group see the original price. While some customers in the treatment group may purchase the shoes directly, others might be influenced to buy a similar, but different, brand because the sale price makes that brand look like a relative deal. In this scenario, some customers in the control group don't respond “naturally” to the unchanged price, but rather, their behavior is changed by the changed price presented to the treatment group. This is interference bias in action.

Cross-Price Substitution: Customers may switch to alternative products if the price of the original is too high.
Budget Constraints: Limited spending power forces choices based on visible deals.
Recommendation Systems: Algorithms highlight comparable prices, influencing choices.

The consequences of ignoring interference bias can be significant. Inflated results might lead you to believe a price change is far more effective than it truly is. This, in turn, can trigger a series of misinformed decisions, such as rolling out the price change across your entire product line, investing in additional inventory, or even altering your marketing strategy.

Beyond A/B Testing: Embracing a Holistic Approach to Pricing

While A/B testing remains a valuable tool, it's essential to acknowledge its limitations and incorporate alternative methods to gain a more complete understanding of pricing dynamics. Observational studies, which analyze historical data to identify patterns and relationships, can provide valuable insights into customer behavior and price sensitivity. By combining A/B testing with observational analysis, businesses can develop more robust and reliable pricing strategies that drive sustainable growth.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2402.14538,

Title: Interference Produces False-Positive Pricing Experiments

Subject: stat.ap econ.em

Authors: Lars Roemheld, Justin Rao

Published: 22-02-2024

Everything You Need To Know

What is interference bias in the context of online retail pricing experiments?

Interference bias occurs when the treatment applied to one product or customer influences the outcome of another in pricing experiments. It happens because customers compare prices, look for deals, and substitute products. This interconnected behavior skews results, leading to inaccurate conclusions about the effectiveness of price changes. For example, if you lower the price of running shoes (treatment group), customers in the control group (seeing the original price) may buy a different, cheaper brand due to the perceived value created by the discount on the running shoes. This impacts the control group's behavior and distorts the A/B test results.

How does cross-price substitution contribute to interference bias?

Cross-price substitution is a key factor in interference bias. When the price of a product changes in a pricing experiment, customers may choose to buy alternative products. If the price of the original product increases, consumers might switch to a similar product offered at a lower price. This substitution impacts the control group's buying behavior, as they might choose different products because of the price change in the treatment group, not because of their inherent preference or the control price. This behavior generates misleading outcomes in A/B testing.

Why are the results of A/B tests often unreliable when interference bias is present?

A/B tests try to isolate the effect of a single variable (price). However, interference bias disrupts this isolation. When customers in the control group change their behavior due to the pricing changes in the treatment group, the comparison becomes flawed. The treatment group's price changes indirectly influence the control group, leading to inaccurate data about the new price's actual impact. It may lead to false positives, where a price change appears more effective than it is, creating bad business decisions.

What are the consequences of ignoring interference bias in online retail pricing?

Ignoring interference bias can lead to significant missteps. Inflated results from A/B tests might make a price change seem more effective than it is, leading to bad decisions. Businesses might then roll out a pricing strategy across the entire product line, invest in excessive inventory, or modify marketing efforts based on inaccurate data. These actions can result in wasted investments, lost profits, and a misunderstanding of customer behavior. Ultimately, it can hinder sustainable and profitable growth.

Besides A/B testing, what other approaches can be used to understand pricing dynamics and mitigate interference bias?

While A/B testing is valuable, businesses can use alternative methods like observational studies to gain a more comprehensive view of pricing dynamics and lessen interference bias. Observational studies analyze historical data to identify patterns, relationships, and customer behavior. Combining these with A/B testing allows for more reliable pricing strategies. Other factors like cross-price substitution, budget constraints, and recommendation systems must be considered, and a holistic strategy that includes both testing and data analysis is essential for sustainable business growth.