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25-34-year-olds are

21%

more likely to drink pumpkin coffees than the average consumer

Quick Insight

It's that time of year again... pumpkin spice is back. 🎃☕

 

According to Circana, 25–34-year-olds are the most likely to enjoy pumpkin-flavored coffee, proving the season's signature sip is going strong.

 

Are you team “too early” or “right on time”?

Company

Solutions

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Our Liquid Data® technology provides cross-industry data and advanced analytics in a single, open platform.

SOLUTIONS

Answer the most pressing business questions.

Designed for small CPG businesses. 

Curated reports and guided analysis.

Data and analytics for a single source of truth. 

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LIQUID DATA™ ESSENTIALS

When Your Data Speaks Retail

See performance the way retailers do without the need for a large team or budget. Built for small and mid-sized suppliers working with major retailers.

Resources

Demand Signals Report

CPG Demand Signals Report

Monitoring the impact of macroeconomic factors, including tariffs, on volume, price, and supply on U.S. consumer behavior within the CPG Industry. 

New strategies and tactics.

Circana's official announcements.

Circana in the press.

Industry rankings vs. previous data period.

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Perspectives from our thought leaders.

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Solving challenges that matter to you.

Thought leaders giving growth insights.

Consumer insights and buying trends.

Suggested solutions

Liquid Data Go® helps small to midsize CPG brands grow faster and dream bigger.

Understand complex consumer behavior with clear, accurate insights into omnichan…

See performance the way retailers do without the need for a large team or budget…

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TERM

Observations

Individual data points in a set.

Definition

An observation is a single record such as a store-week or a transaction.

How to Calculate

Count rows or records used in analysis.

Why it Matters

Sample size affects confidence.

Key Advantages

More observations improve precision.

Example

5,200 store-week observations in a quarter.

Use it to

Count the number of usable records behind a metric to judge statistical stability. Check that observations meet minimum thresholds before drawing conclusions from modeled results.

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