This statistic shows the different types of stationary stores where Americans did their shopping in the past 12 months in 2024. The results were sorted by income tier. As of March 2024, 52 percent of respondents who stated their income was high said they had shopped at a pharmacy store in the last 12 months. The survey was conducted among 8,217 respondents. Access millions of exclusive survey results with Statista Consumer Insights.

During a May 2022 survey, ** percent of responding customers stated that a positive customer service experience made them more likely to purchase again. Moreover, ** percent of customers would recommend a company based solely on excellent customer service.

In today’s rapidly evolving digital landscape, understanding consumer behavior has never been more crucial for businesses seeking to thrive. Our Consumer Behavior Data database serves as an essential tool, offering a wealth of comprehensive insights into the current trends and preferences of online consumers across the United States. This robust database is meticulously designed to provide a detailed and nuanced view of consumer activities, preferences, and attitudes, making it an invaluable asset for marketers, researchers, and business strategists.

Extensive Coverage of Consumer Data Our database is packed with thousands of indexes that cover a broad spectrum of consumer-related information. This extensive coverage ensures that users can delve deeply into various facets of consumer behavior, gaining a holistic understanding of what drives online purchasing decisions and how consumers interact with products and brands. The database includes:

Product Consumption: Detailed records of what products consumers are buying, how frequently they purchase these items, and the spending patterns associated with these products. This data allows businesses to identify popular products, emerging trends, and seasonal variations in consumer purchasing behavior. Lifestyle Preferences: Insights into the lifestyles of consumers, including their hobbies, interests, and activities. Understanding lifestyle preferences helps businesses tailor their marketing strategies to resonate with the values and passions of their target audiences. For example, a company selling fitness equipment can use this data to identify consumers who prioritize health and wellness.

Product Ownership: Information on the types of products that consumers already own. This data is crucial for businesses looking to introduce complementary products or upgrades. For instance, a tech company could use product ownership data to target consumers who already own older versions of their gadgets, offering them incentives to upgrade to the latest models.

Attitudes and Beliefs: Insights into consumer attitudes, opinions, and beliefs about various products, brands, and market trends. This qualitative data is vital for understanding the emotional and psychological drivers behind consumer behavior. It helps businesses craft compelling narratives and brand messages that align with the values and beliefs of their target audience.

Success.ai’s Consumer Behavior Data for Consumer Goods & Electronics Industry Leaders in Asia, the US, and Europe offers a robust dataset designed to empower businesses with actionable insights into global consumer trends and professional profiles. Covering executives, product managers, marketers, and other professionals in the consumer goods and electronics sectors, this dataset includes verified contact information, professional histories, and geographic business data.

With access to over 700 million verified global profiles and firmographic data from leading companies, Success.ai ensures your outreach, market analysis, and strategic planning efforts are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is ideal for businesses aiming to navigate and lead in these fast-paced industries.

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3. Continuously Updated Datasets

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4. Ethical and Compliant

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• 700M+ Verified Global Profiles: Connect with industry leaders, marketers, and decision-makers in consumer goods and electronics industries worldwide.
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• Professional Histories: Understand career trajectories, skills, and expertise of professionals driving innovation and strategy.

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1. Decision-Maker Profiles in Consumer Goods and Electronics

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2. Advanced Filters for Precision Campaigns

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   • Build relationships with key decision-makers at companies specializing in consumer goods or electronics manufacturing and distribution.
   • Present innovative solutions, supply chain partnerships, or co-marketing opportunities to grow your market share.

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Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality consumer behavior data at competitive prices, ensuring maximum ROI for your outreach, research, and ma...

During a May 2023 survey among the general population of selected countries worldwide, ** percent of respondents said they were more price-conscious compared to the previous year. Additionally, ** percent stated that they were doing more research before each purchase and making fewer impulse purchases altogether.

The GapMaps Consumer Behavior database sourced from Applied Geographic Solutions (AGS) is derived from an analysis of the MRI surveys using Panorama. Each of the approximately 40,000 records in the MRI survey is geocoded then assigned the Panorama code of the block group. The results are then summarized for each variable over the sixty-eight segments, in effect providing the average value for each Panorama segment. For example, a variable such as “Shopped at Macy’s” is computed by summarizing the records for each segment as a yes/no response, then finding the average percentage of households in each segment who shopped at Macy’s. This is often referred to as a profile.

The profile is then applied to geographic areas by making the assumption that households in demographically similar neighborhoods will tend to have similar consumption patterns as a result of their similar economic means, life stage, and other characteristics. The result is a series of estimates for geographic areas which measure the relative propensity of consumers in each geographic area to shop at particular stores, own various household items, and engage in activities.

In most cases, these should be considered as relative indicators, since local differences may result in different behavior. In addition, in some cases, variables must be considered as potential only, since the activity or store may not be locally available.

1) Data Introduction • The Consumer Behavior and Shopping Habits Dataset is a tabular collection of customer demographics, purchase history, product preferences, shopping frequency, and online and offline purchasing behavior.

2) Data Utilization (1) Consumer Behavior and Shopping Habits Dataset has characteristics that: • Each row contains detailed consumer and transaction information such as customer ID, age, gender, purchased goods and categories, purchase amount, region, product attributes (size, color, season), review rating, subscription status, delivery method, discount/promotion usage, payment method, purchase frequency, etc. • Data is organized to cover a variety of variables and purchasing patterns to help segment customers, establish marketing strategies, analyze product preferences, and more. (2) Consumer Behavior and Shopping Habits Dataset can be used to: • Customer Segmentation and Target Marketing: You can analyze demographics and purchasing patterns to define different customer groups and use them to develop customized marketing strategies. • Product and service improvement: Based on purchase history, review ratings, discount/promotional responses, etc., it can be applied to product and service improvements such as identifying popular products, managing inventory, and analyzing promotion effects.

Observed linkages between consumer and B2B emails and website domains, categorized into IAB classification codes.

This data provides an unprecedented view into individuals' in-market intent, interests, lifestyle indicators, online behavior, and propensity to purchase. It is highly predictive when measuring buyer intent leading up to a purchase being made.

Hashed emails can be linked to plain-text emails to append all consumer and B2B data fields for a full view of the individual and their online intent and behavior.

Files are updated daily. These are highly comprehensive datasets from multiple live sources. The linkages include first and last-seen dates and an "intent intensity" score derived from the frequency of similar intent categories over a period of time.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Around ** percent of consumers in the United States stated that they were making an effort to buy fewer things in a bid to be more environmentally friendly in 2024. Roughly a fifth of people were intentionally buying from green brands.

According to a study conducted globally in 2023, nearly ********** of people surveyed stated that they are open to the idea of buying new products and/or services recommended to them by generative AI. There was no significant distinction between the age groups, as each generation was more or less equally receptive to the idea of using generative AI for purchasing decisions. For more information on Capgemini's report on why consumers love generative AI, click here.

• Audience Data 1P Data Audience ResolveID™ Platform - Audience Identity Cookieless Technology • Audience Data Identity Global US Graph – 670m + Identity Records • Access to 14 Billion Identity Consumer Profile Data Identifiers • Over 500+ Consumer Attributes, Online & Offline Data Behavior & Signals • IAB™ Seller-Defined Cookieless-Contextual Category – Intent & Behavior Signal Audience Cohorts
• Access to Customer Data Enrichment & Customer Data Ingestion • First-Party Data Ingestion & Data Appending

The Global Audience Segments dataset categorizes people in Canada based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse Canadian and global audiences with various interests and demographic profiles.

The Consumer Behavior database is derived from an analysis of ‘doublebase’ survey data using geodemographic market segmentation. Each of the approximately 40,000 records in the survey is geocoded then assigned the geodemographic market segment code of the block group. The results are then summarized for each variable over the sixty-eight segments, in effect providing the average value for each market segment. For example, a variable such as “Shopped at Macy’s” is computed by summarizing the records for each segment as a yes/no response, then finding the average percentage of households in each segment who shopped at Macy’s. This is often referred to as a profile.

The profile is then applied to geographic areas by making the assumption that households in demographically similar neighborhoods will tend to have similar consumption patterns as a result of their similar economic means, life stage, and other characteristics. The result is a series of estimates for geographic areas which measure the relative propensity of consumers in each geographic area to shop at particular stores, own various household items, and engage in activities.

Consumer Behavior Categories include; • Apparel • Appliances • Attitudes and Organizations • Advertising • Media Advertising • Media Attitudes • Automobiles • Buying Habits • Consumer Confidence • Financial • Food • Health • Intended Purchases • Political Outlook • Public Activities • Sports • Technology • Vacations • Automotive • Baby • Beverages • Computer • Electronics • Family Restaurants • Fast Food and Drive-In Restaurants • Financial • Groceries • Health & Beauty • Health & Medical • Home Furnishings and Equipment • Insurance • Internet • Leisure • Media Radio • Media Read • Media Television • Pets • Shopping • Sports • Telephone • Travel • Video

During a survey carried out in summer 2023 in Germany, ** percent of responding marketers stated that they believed consumers bought more sustainably now than they used to earlier. On the other hand, ** percent said that consumer behavior had not changed at all in this respect.

Market Survey Data for article FACTORS AFFECTING THE SATISFACTION OF CONSUMERS INVESTMENT PATTERN IN GOLD

Access consumer behavior data for 700M+ consumer goods and electronics professionals globally with Success.ai. Includes detailed contact information, professional histories, and business locations. GDPR-compliant. Best price guaranteed.

Our Market Analysis dataset reveals where your visitors also shop, helping you define trade areas, uncover cross-shopping behavior, and enhance location strategy.

Focused on major European markets, this GDPR-compliant, non-PII dataset shows which brands and categories are most visited by people frequenting your POI — supporting smarter site selection, lease renegotiation, and market expansion.

Key data points include: - Cross-visitation by brand/category - Trade area reach and behavior mapping - Aggregated weekly, monthly, quarterly - Cleaned, normalized, GDPR-compliant data - Coverage across key European countries

Built for retailers, landlords, and consultants seeking actionable insights into regional consumer behavior and competitive dynamics.

The Global Audience Segments dataset categorizes people in Germany based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse German and global audiences with various interests and demographic profiles.

The Global Audience Segments dataset categorizes people in Switzerland based on their travel to relevant stores, businesses, or other points of interest - therefore exposing audience media habits, hobbies, and consumer behaviors.

This dataset is a valuable tool for marketers and researchers aiming to understand and reach diverse Swiss and global audiences with various interests and demographic profiles.

Supermarket Ordering, Invoicing, and Sales Analysis

Measuring Consumer Behavior and Engagement

By [source]

About this dataset

This data set provides an in-depth look into the ordering, invoicing and sales processes at a supermarket. With information ranging from customers' meal choices to the value of their orders and whether they were converted into sales, this dataset opens up endless possibilities to uncover consumer behavior trends and engagement within the business. From understanding who is exchanging with the company and when, to seeing what types of meals are most popular with consumers, this rich collection of data will allow us to gain priceless insights into consumer actions and habits that can inform strategic decisions. Dive deep into big data now by exploring Invoices.csv, OrderLeads.csv and SalesTeam.csv for invaluable knowledge about your customers!

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How to use the dataset

This dataset provides an in-depth look into the ordering and invoicing processes of a supermarket, as well as how consumers are engaging with it. This dataset can be used to analyze and gain insights into consumer purchasing behaviors and preferences at the store.

The first step in analyzing this data set is to familiarize yourself with its content. The dataset contains three CSV files: Invoices.csv, OrderLeads.csv, and SalesTeam.csv have different features like date of meal, participants, Meal Price, Type of meal ,company Name ,Order Value etc .Each file contains a list of columns containing data related to each particular feature like Date ,Date Of Meal Participants etc .

Once you understand what types of information is included in each table it’ll be easier for you to start drawing conclusions about customer preferences and trends from within the store's data set. You can use mathematical functions or statistical models such as regression analysis or cluster analysis in order to gain even further insight into customers’ behaviors within the store setting. Additionally you could use machine learning algorithms such as K-Nearest Neighbors (KNN) or Support Vector Machines (SVM) if your goal was improving targeting strategy or recognizing patterns between customer purchases over time.

All these techniques will help you determine what promotional tactics work best when trying to attract customers and promote sales through various marketing campaigns at this supermarket chain They will also help shed light on how customers engage with products within categories across different days/weeks/months according to their own individual purchasing habits which would ultimately contribute towards improved marketing strategies from management side .

Overall this data set provides immense potential for advancing understanding retail behaviour by allowing us access specific transactions that occurred at a given time frame; ultimately providing us detailed insight into customer behavior trends along with tools such software packages that allow us manipulate these metrics however necessary for entertainment purposes that help us identify strategies designed for greater efficiency when increasing revenue

Research Ideas

• Identifying the most profitable customer segment based on order value and converted sales.
• Leveraging trends in participant size to suggest meal packages for different types of meals.
• Analyzing the conversion rate of orders over time to optimize promotional strategies and product offerings accordingly

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Invoices.csv | Column name | Description | |:-----------------|:-------------------------------------------------------------| | Date | The date the order was placed. (Date) | | Date of Meal | The date the meal was served. (Date) | | Participants | The number of people who participated in the meal. (Integer) | | Meal Price | The cost of the meal. (Float) | | Type of Meal | The...