Welcome!

News Feed Item

The US Soup Market: What Consumers Eat and Why?

LONDON, Jan. 29, 2014 /PRNewswire/ -- Reportbuyer.com just published a new market research report:

The US Soup Market: What Consumers Eat and Why?

Product Synopsis

This report provides the results for the Soup market in the US from Canadean's unique, highly detailed study of consumers' Consumer Packaged Goods (CPG) consumption habits, and forms part of an overall series covering all CPG product markets. Its coverage includes, but is not limited to, consumption behaviors, the extent to which consumer trends influence their consumption, the value of the market these trends influence, and brand and private label choices as well as retailer choices. Much of this information can also be analyzed by specific consumer groups, providing hard and fast data on consumers and markets at the product category level.

Introduction and Landscape

Why was the report written?
Marketers in the Soup market face a major challenge. Understanding market size and segmentation is valuable, but the key to effective targeting is knowing just how valuable specific consumer groups are, and to be able to quantify the impact of consumer trends. This data report solves these problems by providing survey-based data on consumer trends and consumer groups and, market data that shows the exact size of consumer groups, how much of the Soup market they account for, and which consumer trends drive their behavior.

What is the current market landscape and what is changing?
As consumer confidence increases proportionally to economic recovery, consumer trends will be directly affected. Since the global financial crisis of 2008-2009, the retail market has been characterized by an increase in the amount of discounted and own-brand products. Higher market volumes in the future will depend on effective marketing campaigns aimed at increasing consumption frequency in Medium and Heavy frequency ranges.

What are the key drivers behind recent market changes?
Consumers' uptake of products and the influence of consumer trends are fundamental causes of change in markets - making knowing what these trends are and the extent of their influence crucial. The survey-based data provided in this report examines over 20 consumer trends that affect the market and examines the share of consumption across 26 consumer groups. This data provides a detailed insight into exactly who the consumer is and just how much impact the latest consumer trends are having.

What makes this report unique and essential to read?
The data provided is unique in the market as it tracks consumer behavior through to its actual value impact on a product market. This provides readers with a unique data analysis of the market, allowing marketing tactics and strategy to be updated in line with the very latest consumer behaviors.

Key Features and Benefits

Consumer data, based upon proprietary surveys and then consumer group tracking and modeling for the following specific categories: Canned/Ambient Soup, Chilled Soup, Dried Soup (mixes), Frozen Soup, and UHT Soup.

Detailed consumer segmentation covering over 26 consumer groups, 20 consumer trends, and consumption frequency for each product category.

Consumer penetration for brands and private labels, based upon the original survey and then subsequent consumer tracking and modeling.

Unique retailer choice data at the product category level, based upon the original survey and then subsequent consumer tracking and modeling.

Key Market Issues

The population of the US is fairly evenly split between Males and Females; however, Males represent a larger value share of the Soup market. The category showing the largest share in favor of Males is Frozen Soup where they consume almost two-thirds. The only category where Females consume a higher share than Males is UHT Soup. Marketers in these categories may find that gender based campaigns would be beneficial.

Two-thirds of the US population describes itself as Urban; however, Urban Dwellers represent over three-quarters of the Chilled Soup market. In general, the Soup market sees higher consumption in Urban areas, which indicates that Rural Dwellers either don't consume much Soup or prefer to make their own. Marketers should target Urban populations with their campaigns.

In proportion to their share of the population, Tweens and Early Teens are the largest group of Soup consumers in the US. This result is driven by their higher average frequency of consumption compared to other age groups. However, in terms of total market value, Older Consumers account for the single largest share of market value and are a prime target for marketers.

Key Highlights

Canned/Ambient Soup dominates the Soup market in the US with a value share of more than three-quarters. Any campaigns to increase consumption rates or appeal to new consumers will have the largest affect in this category. This category will also be most attractive to new entrants.

Not only do a large proportion of US consumers, in certain categories at least, highlight that specific consumer trends have an influence on their consumption, this translates into a significant proportion of actual value being directly influenced as well. Consumers are therefore acting on these trends enough to ensure that targeting them, in the right categories, is essential to success.

Private label penetration in the US Soup market is highest in the Chilled Soup category at just over 25% by volume. The Private Label market in the US is not yet well established; however, national brands in the Chilled Soup category should act now to prevent further loss to Private Label versions before they become a serious threat.

1 Introduction
1.1 What is this Report About?
1.2 Definitions
1.2.1 Consumer Trends
1.2.2 Consumer Groups
1.2.3 End Consumers
1.2.4 Volume Units and Aggregations
1.2.5 Population Profiles (for interpretation of tables and charts)
2 Methodology
2.1 Introduction
2.2 Initial data are based on a large scale, international, program of online consumer surveys
2.3 Demographic groups tracking provides time series data
3 Consumer Segmentation, Group Value and Trend Influence
3.1 Cohort Groups and Soup Market Value
3.1.1 Age Groups
3.1.2 Gender Groups
3.1.3 Location Groups
3.1.4 Education Achieved Groups
3.1.5 Wealth Groups
3.1.6 Busy Lives Groups
3.2 Cohort Groups and Market Value by Category
3.2.1 Canned/Ambient Soup
3.2.2 Chilled Soup
3.2.3 Dried Soup (mixes)
3.2.4 Frozen Soup
3.2.5 Uht Soup
3.3 Behavioral Trends and Market Value
3.3.1 Canned/Ambient Soup
3.3.2 Chilled Soup
3.3.3 Dried Soup (mixes)
3.3.4 Frozen Soup
3.3.5 Uht Soup
4 Consumption Analysis
4.1 Consumption Frequencies by Age and Gender
4.1.1 Canned/Ambient Soup
4.1.2 Chilled Soup
4.1.3 Dried Soup (mixes)
4.1.4 Frozen Soup
4.1.5 Uht Soup
4.2 Consumer Profiles by Product Category
4.2.1 Canned/Ambient Soup
4.2.2 Chilled Soup
4.2.3 Dried Soup (mixes)
4.2.4 Frozen Soup
4.2.5 Uht Soup
5 Brand vs. Private Label Uptake
5.1 Brand vs. Private Label Consumer Penetration
5.1.1 By Category
5.2 Soup Brand Choice and Private Label Consumer Penetration
5.2.1 Canned/Ambient Soup
5.2.2 Chilled Soup
5.2.3 Dried Soup (mixes)
5.2.4 Frozen Soup
5.2.5 Uht Soup
6 The Share of Consumers Influenced by Trends
6.1 Trend Drivers of Consumers' Product Choices
6.1.1 Overall Soup
6.1.2 Canned/Ambient Soup
6.1.3 Chilled Soup
6.1.4 Dried Soup (mixes)
6.1.5 Frozen Soup
6.1.6 Uht Soup
7 Consumption Impact: Market Valuation
7.1 Soup Value Impact of Consumer Consumption Behavior
7.1.1 Market Value by Category
7.1.2 Market Volume by Category
7.2 Soup Value Analysis by Category
7.2.1 Market Value by Category
7.2.2 Expenditure per Capita by Category
7.2.3 Expenditure per Household by Category
7.3 Soup Volume Impact of Consumer Behavior Trends
7.3.1 Market Volume by Category
7.3.2 Consumption per Capita by Category
7.3.3 Consumption Per Household by Category
8 Retailer Choice and Category Share
8.1 Retailer Volume Share
8.1.1 Retailer Volume Share in Soup
8.2 Retailer Volume Share by Category
8.2.1 Retail Share by Volume - Canned/Ambient Soup
8.2.2 Retail Share by Volume - Chilled Soup
8.2.3 Retail Share by Volume - Dried Soup (mixes)
8.2.4 Retail Share by Volume - Frozen Soup
8.2.5 Retail Share by Volume - Uht Soup
8.3 Profiles of End-Consumers of Soup, by Retailer Used
8.3.1 Costco
8.3.2 Kroger
8.3.3 Publix
8.3.4 Safeway
8.3.5 Supervalu
8.3.6 Wal-Mart
8.3.7 Other
9 Appendix
9.1 About Canadean
9.2 Disclaimer

List of Tables

Table 1: Volume Units for the Soup Market
Table 2: United States Survey Respondent profile (weighted), 2012
Table 3: United States Soup Value Share (%), by Age Groups, 2012
Table 4: United States Soup Value Share (%), by Gender, 2012
Table 5: United States Soup Value Share (%), by Urban and Rural Dwellers, 2012
Table 6: United States Soup Value Share (%) by Education Level Achieved Groups, 2012
Table 7: United States Soup Value Share (%) by Wealth Groups, 2012
Table 8: United States Soup Value Share (%) by Busy Lives Groups, 2012
Table 9: United States Canned/Ambient Soup Consumer Group Share (% market value), 2012
Table 10: United States Chilled Soup Consumer Group Share (% market value), 2012
Table 11: United States Dried Soup (mixes) Consumer Group Share (% market value), 2012
Table 12: United States Frozen Soup Consumer Group Share (% market value), 2012
Table 13: United States Uht Soup Consumer Group Share (% market value), 2012
Table 14: United States Total Canned/Ambient Soup Value (US Dollar millions) and Value Share Influenced by Behavioral Trends, 2012
Table 15: United States Total Chilled Soup Value (US Dollar millions) and Value Share Influenced by Behavioral Trends, 2012
Table 16: United States Total Dried Soup (mixes) Value (US Dollar millions) and Value Share Influenced by Behavioral Trends, 2012
Table 17: United States Total Frozen Soup Value (US Dollar millions) and Value Share Influenced by Behavioral Trends, 2012
Table 18: United States Total Uht Soup Value (US Dollar millions) and Value Share Influenced by Behavioral Trends, 2012
Table 19: United States Canned/Ambient Soup Consumption Frequency Analysis (% by Age Group, by Consumption Group), 2012
Table 20: United States Canned/Ambient Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Table 21: United States Chilled Soup Consumption Frequency Analysis (% by Age Group, by Consumption Group), 2012
Table 22: United States Chilled Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Table 23: United States Dried Soup (mixes) Consumption Frequency Analysis (% by Age Group, by Consumption Group), 2012
Table 24: United States Dried Soup (mixes) Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Table 25: United States Frozen Soup Consumption Frequency Analysis (% by Age Group, by Consumption Group), 2012
Table 26: United States Frozen Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Table 27: United States Uht Soup Consumption Frequency Analysis (% by Age Group, by Consumption Group), 2012
Table 28: United States Uht Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Table 29: United States Canned/Ambient Soup Consumer Profiles (% consumers by sub-group), 2012
Table 30: United States Chilled Soup Consumer Profiles (% consumers by sub-group), 2012
Table 31: United States Dried Soup (mixes) Consumer Profiles (% consumers by sub-group), 2012
Table 32: United States Frozen Soup Consumer Profiles (% consumers by sub-group), 2012
Table 33: United States Uht Soup Consumer Profiles (% consumers by sub-group), 2012
Table 34: United States Soup Private Label Consumer Penetration (% Consumers Using), by Category, 2012
Table 35: United States Canned/Ambient Soup Consumer Penetration of Survey-tracked Brands and Private Label (% Consumers Using), 2012
Table 36: United States Chilled Soup Consumer Penetration of Survey-tracked Brands and Private Label (% Consumers Using), 2012
Table 37: United States Dried Soup (mixes) Consumer Penetration of Survey-tracked Brands and Private Label (% Consumers Using), 2012
Table 38: United States Frozen Soup Consumer Penetration of Survey-tracked Brands and Private Label (% Consumers Using), 2012
Table 39: United States Uht Soup Consumer Penetration of Survey-tracked Brands and Private Label (% Consumers Using), 2012
Table 40: United States Soup: Percentage of Consumers Stating that Specific Trends Influence Their Consumption, 2012
Table 41: United States, Canned/Ambient Soup: Percentage of Consumers Stating that Specific Trends Influence Their Consumption, 2012
Table 42: United States, Chilled Soup: Percentage of Consumers Stating that Specific Trends Influence Their Consumption, 2012
Table 43: United States, Dried Soup (mixes): Percentage of Consumers Stating that Specific Trends Influence Their Consumption, 2012
Table 44: United States, Frozen Soup: Percentage of Consumers Stating that Specific Trends Influence Their Consumption, 2012
Table 45: United States, Uht Soup: Percentage of Consumers Stating that Specific Trends Influence Their Consumption, 2012
Table 46: United States Soup Market Value (US Dollar million), by Category, 2012
Table 47: United States Soup Market Volume (Kg m), by Category, 2012
Table 48: United States Soup Market Value (US$ million), by Category, 2012
Table 49: United States Soup Expenditure Per Capita (US Dollar), by Category, 2012
Table 50: United States Soup Expenditure Per Household (US Dollar), by Category
Table 51: United States Soup Market Volume (Kg m), by Category, 2012
Table 52: United States Soup Consumption Per Capita (Kg / Population), by Category, 2012
Table 53: United States Soup Consumption Per Household (Kg / Household), by Category, 2012
Table 54: United States Soup Survey-tracked Retailer Shares by Volume (% of Kg m), 2012
Table 55: United States Canned/Ambient Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Table 56: United States Chilled Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Table 57: United States Dried Soup (mixes) Survey-tracked Retailer Shares by Volume (Kg m), 2012
Table 58: United States Frozen Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Table 59: United States Uht Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Table 60: United States: Profile of Soup Consumers Whose Goods Mainly Come From Costco (% by Subgroup, as tracked by the Survey), 2012
Table 61: United States: Profile of Soup Consumers Whose Goods Mainly Come From Kroger (% by Subgroup, as tracked by the Survey), 2012
Table 62: United States: Profile of Soup Consumers Whose Goods Mainly Come From Publix (% by Subgroup, as tracked by the Survey), 2012
Table 63: United States: Profile of Soup Consumers Whose Goods Mainly Come From Safeway (% by Subgroup, as tracked by the Survey), 2012
Table 64: United States: Profile of Soup Consumers Whose Goods Mainly Come From Supervalu (% by Subgroup, as tracked by the Survey), 2012
Table 65: United States: Profile of Soup Consumers Whose Goods Mainly Come From Wal-Mart (% by Subgroup, as tracked by the Survey), 2012
Table 66: United States: Profile of Soup Consumers Whose Goods Mainly Come From Other (% by Subgroup, as tracked by the Survey), 2012

List of Figures

Figure 1: Consumer Trends Report Methodology
Figure 2: United States Soup Value Share (%), by Age Groups, 2012
Figure 3: United States Soup Value Share (%), by Gender, 2012
Figure 4: United States Soup Value Share (%), by Urban and Rural Dwellers, 2012
Figure 5: United States Soup Value Share (%) by Education Level Achieved Groups, 2012
Figure 6: United States Soup Value Share (%) by Wealth Groups, 2012
Figure 7: United States Soup Value Share (%) by Busy Lives Groups, 2012
Figure 8: United States Canned/Ambient Soup Consumption Frequency Analysis (% by Age Group by Consumption Group), 2012
Figure 9: United States Canned/Ambient Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Figure 10: United States Chilled Soup Consumption Frequency Analysis (% by Age Group by Consumption Group), 2012
Figure 11: United States Chilled Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Figure 12: United States Dried Soup (mixes) Consumption Frequency Analysis (% by Age Group by Consumption Group), 2012
Figure 13: United States Dried Soup (mixes) Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Figure 14: United States Frozen Soup Consumption Frequency Analysis (% by Age Group by Consumption Group), 2012
Figure 15: United States Frozen Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Figure 16: United States Uht Soup Consumption Frequency Analysis (% by Age Group by Consumption Group), 2012
Figure 17: United States Uht Soup Consumption Frequency Analysis (% by Gender by Consumption Group), 2012
Figure 18: United States Soup Market Value (US$ million), by Category, 2012
Figure 19: United States Soup Expenditure Per Capita (US$), by Category, 2012
Figure 20: United States Soup Expenditure Per Household (US$), by Category
Figure 21: United States Soup Survey-tracked Retailer Shares by Volume (% of Kg m), 2012
Figure 22: United States Canned/Ambient Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Figure 23: United States Chilled Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Figure 24: United States Dried Soup (mixes) Survey-tracked Retailer Shares by Volume (Kg m), 2012
Figure 25: United States Frozen Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012
Figure 26: United States Uht Soup Survey-tracked Retailer Shares by Volume (Kg m), 2012

Companies Mentioned

Costco, Kroger, Publix, Safeway, Supervalu ,Walmart

Read the full report:
The US Soup Market: What Consumers Eat and Why?
http://www.reportbuyer.com/food_drink/fruit_vegetable/us_soup_market_consumers_eat_why_1.html#utm_source=prnewswire&utm_medium=pr&utm_campaign=Fruit_and_Vegetable

For more information:
Sarah Smith
Research Advisor at Reportbuyer.com
Email: [email protected]  
Tel: +44 208 816 85 48
Website: www.reportbuyer.com

SOURCE ReportBuyer

More Stories By PR Newswire

Copyright © 2007 PR Newswire. All rights reserved. Republication or redistribution of PRNewswire content is expressly prohibited without the prior written consent of PRNewswire. PRNewswire shall not be liable for any errors or delays in the content, or for any actions taken in reliance thereon.

Latest Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will d...
While DevOps most critically and famously fosters collaboration, communication, and integration through cultural change, culture is more of an output than an input. In order to actively drive cultural evolution, organizations must make substantial organizational and process changes, and adopt new technologies, to encourage a DevOps culture. Moderated by Andi Mann, panelists discussed how to balance these three pillars of DevOps, where to focus attention (and resources), where organizations might...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Le...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
CI/CD is conceptually straightforward, yet often technically intricate to implement since it requires time and opportunities to develop intimate understanding on not only DevOps processes and operations, but likely product integrations with multiple platforms. This session intends to bridge the gap by offering an intense learning experience while witnessing the processes and operations to build from zero to a simple, yet functional CI/CD pipeline integrated with Jenkins, Github, Docker and Azure...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Dhiraj Sehgal works in Delphix's product and solution organization. His focus has been DevOps, DataOps, private cloud and datacenters customers, technologies and products. He has wealth of experience in cloud focused and virtualized technologies ranging from compute, networking to storage. He has spoken at Cloud Expo for last 3 years now in New York and Santa Clara.
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure ...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.