Map of mega warehouses in the United States

With the growth in online shopping over the years, companies required more space to store their products, which gave rise to mega warehouses (more than 100k square feet) across the country. Judith Lewis Mernit and Geoff McGhee describe and show the growth with a map.

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New shopping search patterns from the pandemic

Schema Design, Google Trends, and Axios collaborated on The New Normal, looking at how searches for certain products has changed since the pandemic started. Keywords were taken from Google’s product taxonomy, and search volumes are from Google Shopping.

From there, the keywords, compared to search from 2019, were categorized as a new normal, unusual, or about the same as before. They categorized the words manually instead of defining a metric, which surprised me. It seems like it would’ve been useful for sorting beyond alphabetical. Still interesting to poke at though.

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Optimizing retail spaces

Patrick Sisson for The New York Times reports on the growing popularity of tracking customer movement in stores:

Complicating efforts to address privacy concerns is a lack of regulatory clarity. Without an overarching federal privacy law or even a shared definition of personal data, retailers must sort through layers of state and municipal rules, such as California’s Consumer Privacy Act, said Gary Kibel, a partner at the law firm Davis+Gilbert who specializes in retail privacy.

Technology companies counter the pushback by noting that their systems are designed to limit what they collect and anonymize the rest. For instance, Standard AI’s system does not capture faces, so they cannot be analyzed with facial recognition technology.

Uh huh.

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Stores that closed on famous shopping streets

Pre-pandemic, we walked around shopping areas casually browsing, but a lot of retail didn’t make it through. For Quartz, Amanda Shendruk looks at the closures on famous shopping streets, complete with a location-appropriate vehicle to drive in and a police car that appears if you scroll too fast.

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When the interesting pattern ends up just being computer byproduct

Good lesson here. Christian Laesser was playing around with receipt data and initially thought he had a fun pattern at hand. It looked like the shopper always put things in his or her cart in the same order every time. It turns out though that the order just came from the computer ordering items by category. It had nothing to do with shopping order.

Familiarize yourself with your data source before you go deep diving for insights.

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