How stores are spying on you (encore)⁠87%

By Monica Nickelsburg⁠0% Joshua McNichols⁠0% Lucy Soucek⁠0% Alec Cowan⁠0% Carol Smith⁠0%

12/24/2025, 8:45:01 AM

BS Summary: This article contains 11 faulty reasoning types, including Appeal to Emotion, Optimism Bias, and In-Group Bias, with Framing Effect as the most egregious example at 46.5% saturation with 93 hits. Analysis detected 405 faulty-reasoning hits from 200 analyzed words, generating a BS Score of 79.1% and a BS Rank of ⁠87% (2,977 of 21,886 articles). This article is worse (more manipulative) than 86.40% of the article peer group.

The Booming team is hard at work on some new episodes coming out next year. 
In the meantime, we wanted to revisit some of our favorite stories this holiday season. 
We think this one will be particularly useful for those of you still doing some last-minute holiday shopping. 
It explores the ways AI is being used by some of your favorite retailers to get better at selling you stuff. 
This story first aired in December 2024 -- you can read the original story here: How Stores are Spying on You 
If you want to give Booming a gift this holiday season, you can give us a rating and review on your favorite podcast app. 
Finally, a big thank you from all of us at Booming. 
We appreciate everyone who listens to the show, and we hope you have a great holiday season. 
Thank you to the supporters of KUOW, you help make this show possible! 
If you want to help out, go to kuow.org/donate/boomingnotes. 
Booming is a production of KUOW in Seattle, a proud member of the NPR Network. 
Our editor is Carol Smith. 
Our producers are Lucy Soucek and Alec Cowan. 
Our hosts are Joshua McNichols and Monica Nickelsburg. 
Article reasoning-pattern comparisonThis article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Actor-Observer Bias0.0%This article: 10.5%Monica Nickelsburg: 1.7%KUOW: 1.3%Anchoring Bias10.5%This article: 0.0%Monica Nickelsburg: 4.1%KUOW: 3.4%Availability Heuristic0.0%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.0%Blind-Spot Bias0.0%This article: 0.0%Monica Nickelsburg: 2.6%KUOW: 2.6%Confirmation Bias0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.0%Dunning-Kruger Effect0.0%This article: 46.5%Monica Nickelsburg: 12.0%KUOW: 7.4%Framing Effect46.5%This article: 0.0%Monica Nickelsburg: 1.1%KUOW: 0.9%Fundamental Attribution Error0.0%This article: 7.5%Monica Nickelsburg: 1.7%KUOW: 2.7%Halo Effect7.5%This article: 0.0%Monica Nickelsburg: 0.4%KUOW: 0.7%Hindsight Bias0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Horn Effect0.0%This article: 22.5%Monica Nickelsburg: 2.0%KUOW: 2.0%In-Group Bias22.5%This article: 0.0%Monica Nickelsburg: 0.7%KUOW: 1.0%Loss Aversion0.0%This article: 21.0%Monica Nickelsburg: 9.9%KUOW: 8.0%Negativity Bias21.0%This article: 25.0%Monica Nickelsburg: 4.1%KUOW: 3.8%Optimism Bias25.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.5%Out-Group Homogeneity Bias0.0%This article: 0.0%Monica Nickelsburg: 1.7%KUOW: 1.4%Overconfidence Bias0.0%This article: 0.0%Monica Nickelsburg: 1.8%KUOW: 1.8%Pessimism Bias0.0%This article: 0.0%Monica Nickelsburg: 0.5%KUOW: 0.4%Primacy Effect0.0%This article: 0.0%Monica Nickelsburg: 0.9%KUOW: 1.1%Recency Bias0.0%This article: 0.0%Monica Nickelsburg: 1.0%KUOW: 1.2%Representativeness Heuristic0.0%This article: 14.0%Monica Nickelsburg: 3.3%KUOW: 2.0%Self-Serving Bias14.0%This article: 0.0%Monica Nickelsburg: 0.9%KUOW: 1.0%Status Quo Bias0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.2%Sunk Cost Effect0.0%This article: 0.0%Monica Nickelsburg: 1.2%KUOW: 0.5%Ad Hominem0.0%This article: 0.0%Monica Nickelsburg: 2.4%KUOW: 1.4%Ambiguity (Equivocation)0.0%This article: 0.0%Monica Nickelsburg: 3.6%KUOW: 3.3%Anecdotal0.0%This article: 7.5%Monica Nickelsburg: 5.4%KUOW: 4.3%Appeal to Authority7.5%This article: 31.5%Monica Nickelsburg: 4.9%KUOW: 6.1%Appeal to Emotion31.5%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.2%Appeal to Nature0.0%This article: 4.5%Monica Nickelsburg: 0.9%KUOW: 0.8%Bandwagon4.5%This article: 12.0%Monica Nickelsburg: 0.7%KUOW: 0.8%Begging the Question12.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.3%Burden of Proof0.0%This article: 0.0%Monica Nickelsburg: 0.5%KUOW: 0.1%Circular Reasoning0.0%This article: 0.0%Monica Nickelsburg: 0.4%KUOW: 0.2%Composition/Division0.0%This article: 0.0%Monica Nickelsburg: 2.3%KUOW: 1.4%False Dilemma0.0%This article: 0.0%Monica Nickelsburg: 0.0%KUOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.2%Genetic Fallacy0.0%This article: 0.0%Monica Nickelsburg: 4.5%KUOW: 4.1%Hasty Generalization0.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.1%Middle Ground0.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.1%No True Scotsman0.0%This article: 0.0%Monica Nickelsburg: 0.1%KUOW: 0.1%Personal Incredulity0.0%This article: 0.0%Monica Nickelsburg: 4.4%KUOW: 2.2%Post Hoc (False Cause)0.0%This article: 0.0%Monica Nickelsburg: 0.3%KUOW: 0.3%Red Herring0.0%This article: 0.0%Monica Nickelsburg: 0.7%KUOW: 0.9%Slippery Slope0.0%This article: 0.0%Monica Nickelsburg: 0.3%KUOW: 0.2%Special Pleading0.0%This article: 0.0%Monica Nickelsburg: 0.2%KUOW: 0.3%Straw Man0.0%This article: 0.0%Monica Nickelsburg: 0.3%KUOW: 0.1%Tu Quoque0.0%

200 words analyzed.

Speakers

No attributed speakers were identified in this analysis.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.