When routines shift: what Thanksgiving food delivery reveals about occasion-based demand

When special occasions disrupt daily routines, consumer behavior shifts. Thanksgiving food delivery data reveals three key patterns: order volumes drop 33% while basket values rise 10%, timing shifts from evening to morning/midday, and light users emerge for occasion-specific needs. Here's what these holiday delivery trends tell us about how brands can identify and capitalize on shifting consumer patterns.

Traditionally, Thanksgiving is all about home cooking. But that doesn’t mean everyone spends hours over a hot oven during the holiday. OpenTable recently reported that Thanksgiving-day restaurant reservations were up 13% since last year, and plenty of restaurant chains offered carryout and pre-orders for the holiday. With on-demand delivery apps now the norm across categories, do third-party delivery platforms also play a role in Thanksgiving-day eating?

The answer is yes, but not in the way you may expect. Our unique data on in-app food delivery behavior from US consumers shows orders drop on Thanksgiving. But the ordering patterns reveal how the holiday disrupts delivery routines—and what it means for brands.

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Delivery volume drops, values rise

Delivery orders fell 33% on Thanksgiving compared to typical Thursdays in November. But despite lower volume, order values increased 10% from baseline, to $31.78 up from $28.61. 15% of orders were over $50, compared to just 10% on a typical Thursday. This isn't surprising when you consider the context: according to one survey, the average Thanksgiving host expects 9 guests. This isn’t the usual weeknight order for one or two.

Plus, mental accounting is a factor here: Consumers tend to categorize holiday spending as one-time purchases that sit outside the normal budgets, making them less price sensitive. A $50 delivery order that might feel extravagant on a routine evening becomes justified on Thanksgiving.

Delivery as the supporting player

On a typical Thursday, delivery orders ramp up through the afternoon and peak at dinner time (6-8pm). Thanksgiving flips this: orders peak in late morning and early afternoon, then drop off through the evening. A secondary late-night spike around midnight suggests that delivery isn't necessarily replacing the Thanksgiving meal; it's working around it. New moments appear around the main event: breakfast while cooking, post-turkey late-night snacks.

Chains like Denny's, IHOP, and Dunkin' appeared more prominently (and with higher-than-usual order values) in Thanksgiving orders, consistent with a morning family-breakfast order. While it’s a much smaller share of overall orders, consumers are also more likely to order grocery and retail categories on Thanksgiving—suggesting there may be some panic buying of forgotten ingredients.

Light buyers with specific needs

Who’s ordering on Thanksgiving? It’s not the usual crowd. Nearly a quarter of Thanksgiving orders (24%) came from customers who hadn’t ordered at all during the previous month. These aren’t habitual delivery users, but light users solving occasion-specific needs, like those mentioned above. Restaurants and delivery services are catering to a different cohort of customers on Thanksgiving.

The upshot: Using consumer behavior data for holiday planning

Special occasions disrupt normal behavior, creating time windows where different rules apply. Granular behavioral data reveals those opportunities:

  1. Don’t compete, support: Major events don’t necessarily compete with your category, they create new demands and moments. Identifying occasion-specific behavior—and working with it—allows brands to tap into opportunities others may miss.
  1. Spot when price sensitivity shifts: Thanks to mental accounting, consumers are willing to spend more during special occasions. Data shows you when to lean into higher-value offerings and premium positioning.
  1. Know when your audience changes: Light users with specific needs behave differently than your day-to-day core base. Recognizing these shifts lets you tailor messaging and product mix to the consumers actually in market at any given moment, not just your typical customer.

Understanding who's ordering, when, and why helps brands match the moment rather than making assumptions about behavior patterns.

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