According to a survey carried out in 2024 in the United States, some ** percent of baby boomers were shopping for groceries once a week. Among millennials, the share of those shopping weekly for groceries was lower, at ** percent. On the other hand, ** percent of millennials were shopping for groceries daily, while baby boomers were only ******percent. Find this and more survey data in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

In a 2023 survey of U.S. shoppers, 68 percent of respondents who identified themselves as primary shoppers in their household were women, while 56 percent were men. For those who identified themselves as self shoppers, that is, people who live alone and are responsible for their own spending, 15 percent were men and 13 percent were women.

Grocery Sales Database - Data Card

Overview

The Grocery Sales Database is a structured relational dataset designed for analyzing sales transactions, customer demographics, product details, employee records, and geographical information across multiple cities and countries. This dataset is ideal for data analysts, data scientists, and machine learning practitioners looking to explore sales trends, customer behaviors, and business insights.

Database Schema

The dataset consists of seven interconnected tables:

Table Descriptions

1. categories

2. cities

3. countries

4. customers

5. employees

6. products

In 2025, consumers in the United States were surveyed about their regular food and everyday products shopping destinations. Among those who shopped at discount stores, ** percent of Millennials reported doing so, whereas the corresponding share for baby boomers was ** percent. Find this and more survey data in our Consumer Insights tool. Filter by countless demographics, drill down to your own, hand-tailored target audience, and compare results across countries worldwide.

Click-and-Collect Grocery Market Outlook

According to our latest research, the global click-and-collect grocery market size has reached USD 70.4 billion in 2024, reflecting the rapid digital transformation in the retail sector. The market is experiencing robust momentum, growing at a compound annual growth rate (CAGR) of 13.2% from 2025 to 2033. By the end of 2033, the market is forecasted to reach a substantial USD 209.8 billion. This impressive growth is primarily driven by evolving consumer preferences for convenience, time-saving shopping experiences, and the increasing penetration of digital technologies in the grocery retail segment.

One of the primary growth factors fueling the click-and-collect grocery market is the shifting consumer behavior towards online shopping, particularly in the aftermath of the COVID-19 pandemic. Consumers are increasingly valuing the ability to order groceries online and pick them up at their convenience, avoiding long in-store queues and minimizing physical contact. This demand for convenience and safety has encouraged retailers to expand their click-and-collect services, integrating user-friendly digital platforms and mobile applications. The proliferation of smartphones and enhanced internet connectivity worldwide have further accelerated the adoption of these services, making click-and-collect grocery shopping accessible to a broader demographic.

Another significant driver is the technological advancements in logistics and supply chain management. Retailers are investing heavily in automation, real-time inventory tracking, and data analytics to streamline order processing and ensure accurate, timely fulfillment of online grocery orders. Innovations such as dedicated pickup lanes, temperature-controlled lockers, and advanced notification systems have enhanced the efficiency and reliability of click-and-collect services. These improvements not only boost customer satisfaction but also enable retailers to handle higher order volumes, supporting the scalability of click-and-collect operations. As a result, both large supermarket chains and independent grocery stores are leveraging these technological upgrades to stay competitive in a rapidly evolving market landscape.

The market is also benefiting from strategic partnerships and collaborations between retailers, technology providers, and logistics companies. These alliances are enabling the development of integrated platforms that offer seamless end-to-end experiences for consumers. Retailers are increasingly adopting omnichannel strategies, blending online and offline touchpoints to create a cohesive shopping journey. This trend is particularly pronounced in urban areas, where busy lifestyles and high population density drive demand for flexible and efficient grocery pickup solutions. Additionally, the rise of eco-friendly and contactless pickup options is attracting environmentally conscious consumers, further expanding the marketÂ’s appeal across diverse customer segments.

Retailers are increasingly turning to Click and Collect Solutions to enhance customer convenience and streamline operations. These solutions allow customers to place orders online and pick them up at a designated location, often without leaving their vehicles. By integrating Click and Collect Solutions, retailers can offer a seamless shopping experience that combines the ease of online shopping with the immediacy of in-store pickup. This approach not only reduces wait times but also minimizes the need for additional staffing, making it a cost-effective strategy for businesses. As consumer demand for flexible shopping options grows, the implementation of Click and Collect Solutions is becoming a critical component of modern retail strategies.

Regionally, North America leads the click-and-collect grocery market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The North American market is characterized by widespread adoption of digital retail solutions, a high concentration of major supermarket chains, and strong consumer inclination towards convenience-oriented shopping. In Europe, regulatory support for digital commerce and increasing investments in retail infrastructure are driving market growth. Meanwhile, the Asia Pacific region is witnessing rapid expansion, fueled by growing urbanization, rising disposable incomes,

The dataset consists of a list of customer names that represent individuals who have shopped at a supermarket grocery store. These names might have been collected from sales records, customer databases, or loyalty programs. The dataset includes a wide range of names, reflecting diverse customer demographics. Names are often repeated, suggesting multiple purchases or records for the same individuals, indicating returning customers.

It can be used to analyze purchasing patterns, identify frequent shoppers, and develop customer relationship strategies. This dataset might also be helpful in personalizing marketing campaigns or segmenting customers based on their shopping behavior. The presence of repeated names suggests that this could be a longitudinal dataset, tracking purchases over time.

Overall, this dataset provides valuable information for understanding customer behavior, loyalty, and the potential for targeted marketing in a supermarket grocery context.

Sure! Here's a description for your dataset:

Dataset Description

This dataset contains transaction records related to orders placed in various cities. Each entry provides detailed information about the order, including:

• Order ID: A unique identifier for each order.
• Category: The main classification of the product (e.g., electronics, clothing).
• Sub Category: A more specific classification within the main category (e.g., smartphones, t-shirts).
• City: The location where the order was placed.
• Order Date: The date on which the order was made, formatted as YYYY-MM-DD.
• MRP (Maximum Retail Price): The original price of the product before any discounts.
• Discount: The amount or percentage deducted from the MRP, indicating the savings on the order.

This dataset can be utilized for various analyses, such as sales performance by category, discount impact on order volume, and geographic trends in purchasing behavior.

A 2023 survey conducted in Indonesia revealed that Shopee was the leading online grocery shopping platforms, being used by ** percent of Gen Z and ** percent of Gen Y respondents. On the other hand, Gen Y showed a higher preference for shopping through Tokopedia now, with *** percent favoring it, surpassing the *** percent preference observed among Gen Z respondents.

This dataset contains detailed information about customer transactions, behavior, and demographic data for a grocery store. The dataset includes customer information, transactional data, and customer behavior metrics, making it ideal for building predictive models for customer churn and Customer Lifetime Value (CLV) analysis.

The target column for churn prediction is churn, which indicates whether a customer has churned (1) or remained active (0). The dataset is designed to help you build models to predict customer retention, analyze customer behavior, and forecast customer lifetime value.

Key Features: Customer Information: Includes demographic details such as age, gender, income bracket, marital status, education level, and occupation.

Transactional Data: Contains information about each transaction, such as transaction date, quantity, price, product category, and payment method.

Customer Behavior Metrics: Includes features like average purchase value, purchase frequency, and last purchase date.

Promotional Data: Includes details on promotions, including type, effectiveness, and target audience.

Churn: The target column indicates whether the customer has churned (1) or remained active (0).

Size of the Dataset: This dataset contains 35,843 rows and 26 attributes (including the churn column), making it suitable for predictive modeling, classification, and regression analysis tasks.

GapMaps GIS data for USA and Canada sourced from Applied Geographic Solutions (AGS) includes an extensive range of the highest quality demographic and lifestyle segmentation products. All databases are derived from superior source data and the most sophisticated, refined, and proven methodologies.

GIS Data attributes include:

1. Latest Estimates and Projections The estimates and projections database includes a wide range of core demographic data variables for the current year and 5- year projections, covering five broad topic areas: population, households, income, labor force, and dwellings.

2. Crime Risk Crime Risk is the result of an extensive analysis of a rolling seven years of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, Crime Risk provides an accurate view of the relative risk of specific crime types (personal, property and total) at the block and block group level.

3. Panorama Segmentation AGS has created a segmentation system for the United States called Panorama. Panorama has been coded with the MRI Survey data to bring you Consumer Behavior profiles associated with this segmentation system.

4. Business Counts Business Counts is a geographic summary database of business establishments, employment, occupation and retail sales.

5. Non-Resident Population The AGS non-resident population estimates utilize a wide range of data sources to model the factors which drive tourists to particular locations, and to match that demand with the supply of available accommodations.

6. Consumer Expenditures AGS provides current year and 5-year projected expenditures for over 390 individual categories that collectively cover almost 95% of household spending.

7. Retail Potential This tabulation utilizes the Census of Retail Trade tables which cross-tabulate store type by merchandise line.

8. Environmental Risk The environmental suite of data consists of several separate database components including: -Weather Risks -Seismological Risks -Wildfire Risk -Climate -Air Quality -Elevation and terrain

Primary Use Cases for GapMaps GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic & segmentation profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular census block level using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate AGS demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Finance / Insurance (eg. Hedge Funds, Investment Advisors, Investment Research, REITs, Private Equity, VC)

8. Network Planning

9. Customer (Risk) Profiling for insurance/loan approvals

10. Target Marketing

11. Competitive Analysis

12. Market Optimization

13. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

14. Tenant Recruitment

15. Target Marketing

16. Market Potential / Gap Analysis

17. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

18. Customer Profiling

19. Target Marketing

20. Market Share Analysis

Factors that explain adoption of online grocery shopping: Logit estimation results.

Abstract

Rates of overweight, obesity, and chronic diseases such as cardiovascular diseases, hypertension, type 2 diabetes and certain cancers (bowel, lung, prostate and uterine) are on the rise in most sub-saharan Africa (SSA) countries like kenya. These increases can be largely attributed to the shift toward unhealthy diet patterns and increased access to processed foods that are high in fat, sugar, and sodium. The influx of supermarkets in east africa and the replacement of traditional foods for processed foods places this region in a vulnerable position for greater increases in chronic disease rates. Consumer purchasing history from supermarkets can provide valuable insight to food intake over time and the present and future effects on chronic diseases. Purchasing data from supermarkets is available yet underutilized in SSA.

The study aimed to harmonize and increase accessibility to grocery data, use statistical methods to explore purcharing patterns and predict the effects of nutrition on chronic diseases, and inform policy on the various influences on consumer purchases.

Geographic coverage

National coverage: Kiambu, Nyeri, Embu, Murang'a and Nairobi Counties

Analysis unit

Individuals and supermarket transaction records.

Universe

The survey covers transaction records of individuals who made purchases in supermarkets.

Sampling procedure

The study is a cross-sectional exploratory study with a phased approach employing quantitative secondary data collection from a third-party information management solution provider. The third party provider employs an open integrated point of sale and store information retail system that connects retail touch points and sales channels in several counties in Kenya.

Sampling was conducted after a census of all supermarkets subscribed to the third party system was done. Only those counties with supermarkets subscribed to the platform were sampled. A sample of large, medium sized and small supermarkets were selected to participate in the study. The supermarket sizes were determined as follows; large supermarkets ( supermarkets with a cumulative total of more than 8 branch networks). Medium size supermarkets will be those with 3-8 branch networks in the counties and smaller supermarkets are those with 1-2 branch networks.

Grocery data was received a supermarket chain with 11 branches.

Sampling deviation

Not Applicable

Mode of data collection

Other [oth]

Research instrument

A standardized form was developed to guide in extration of information from 3rd party information provider for supermarket purchase data. Variables of interest includes supermarket name, supermarket branch, location of supermarket, invoice id, customer id, customer demographics (gender, age), date and time of purchase, product name purchased, unit price per item, number of items purchased, payment method used by customer for purchase etc.

Secondary data collected will not be identifiable as it will be anonymized at the supermarket and client level.

The standardized form is provided as external resources data. V1-V18 the questions are found in the “Study abstraction tool”

Cleaning operations

Not Applicable

Response rate

Not Applicable

Sampling error estimates

Not Applicable

This dataset contains the annual behavioral records of members of a large membership-based grocery store in China over the course of 2024. It captures key aspects of consumer activity such as shopping frequency, average basket size, promotions engagement, mobile app engagement, points usage, membership renewal, and tier classification. Each row represents a summary of a member's behavior throughout the year.

• membership_auto_renew: Whether the membership is set to auto-renew (1 = Yes, 0 = No)
• push_notification_enabled: Whether push notifications are enabled (1 = Enabled, 0 = Not enabled)
• shared_account: Whether the membership is shared with family/friends (1 = Yes, 0 = No)
• have_app: Whether the member uses the grocery store’s mobile app (1 = Yes, 0 = No)
• bought_store_brand: Whether the member bought store-brand products (1 = Yes, 0 = No)
• average_basket_size: The average spending per shopping trip, in CNY (¥)
• use_count: Number of times the member used their account to make a purchase
• reward_points_used: Total reward points redeemed during the year

The COVID-19 pandemic has had a lasting impact on many economies around the globe. One area where significant changes have been documented is consumer behavior. A questionnaire survey was carried out to understand the impact of COVID-19 on grocery purchase behavior of Canadian consumers and evaluate the permanence of these effects. With a focus on online grocery shopping, this work integrates multiple existing theories of consumer behavior to explore the influence of different factors on consumers’ adoption of online mode of grocery shopping during the pandemic and their intentions to continue the use of this mode in the post-pandemic world. A total of more than 600 usable survey responses were analyzed using statistical analysis and a Logit econometrics technique. The results reveal that 72% of the survey participants had to alter their grocery shopping habits as a result of the COVID-19 pandemic; 63% of these consumers claim that the changes that occurred would prevail in the future, with no return to the “pre-COVID normal”. The results also show that the pandemic resulted in significant proliferation of online grocery shopping among Canadian consumers. Further, the findings show that the important factors that explain adoption of online grocery shopping and the shift towards higher reliance on online grocery purchases in the future include the perceived threat of COVID, pre-COVID shopping habits, socio-demographic characteristics, and the variables that capture technological opportunities and abilities.

The global e-grocery market is experiencing robust growth, driven by increasing internet penetration, changing consumer lifestyles, and the convenience offered by online shopping. The market, estimated at $500 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $1.5 trillion by 2033. This expansion is fueled by several key factors, including the rising adoption of smartphones and readily available high-speed internet access, particularly amongst younger demographics. Consumers are increasingly seeking the ease and time-saving benefits of online grocery shopping, particularly for busy professionals and families. Furthermore, innovative technologies like AI-powered personalized recommendations and improved delivery logistics are further enhancing the shopping experience and driving market expansion. The emergence of quick commerce, offering ultra-fast delivery, is a significant trend shaping the market landscape. Major players like Walmart, Amazon, Kroger, and Tesco are aggressively investing in enhancing their e-grocery platforms and expanding their delivery networks to maintain a competitive edge. However, challenges remain, including maintaining consistent product quality and freshness, managing last-mile delivery costs effectively, and addressing concerns about food safety and security. Competition is intensifying, with both established retailers and smaller, specialized online grocers vying for market share. The market is segmented geographically, with North America and Europe currently dominating, but emerging markets in Asia and Africa are showing significant growth potential. Successfully navigating these challenges and capitalizing on emerging trends will be crucial for sustained success in this dynamic market.

Customer Retention with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used for customer retention purposes, such as performing a shopper retention analysis over time for a specific company.

Inquire about a CE subscription to perform more complex, near real-time competitive analysis functions on public tickers and private brands like: • Choose a pair of merchants to determine spend overlap % between them by period (yearly, quarterly, monthly) • Explore cross-shop history within subindustry and market share (updated weekly)

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Competitive Analysis

Problem A grocery delivery brand needs to assess overall company performance, including customer acquisition and retention levels relative to key competitors.

Solution Consumer Edge transaction data can uncover performance over time and help companies understand key drivers of retention: • By geography and demographics • By channel • By shop date

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on customer retention for company-wide reporting • Reduce investment in underperforming channels, both online and offline • Determine demo and geo drivers of retention for refined targeting • Analyze customer acquisition campaigns driving retention and plan accordingly

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets