Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

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2. MONTH -> from April (4) to August (8)

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3. DAY -> day number of the month

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4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

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8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

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9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

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12. PRICE -> price in US dollars

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13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

In 2024, convenience was the leading reason to spend more money online during Cyber Week than in the previous year. Prices being lower online was the second most common reason for U.S. Cyber Week shoppers.

For 2024's Black Friday and Cyber Monday sales event, also known as the 'Cyber Week', approximately 77 percent of shoppers in the United States that planned to visit online retailers during Cyber Week specifically intended to buy clothing and accessories, making it the most popular product category. Just over 70 percent of respondents also planned to buy electronics.

Online Shopping Consumer Behavior Dataset

Consumer Buying Patterns in E-Commerce

By Weitong Li [source]

About this dataset

This dataset is a rich compilation of data that thoroughly guides us through consumers' behavior and their buying intentions while engaged in online shopping. It has been constructed with immense care to ensure it effectively examines an array of factors that influence customers' purchasing intentions in the increasingly significant realm of digital commerce.

The dataset is exhaustively composed with careful attention to collecting a diverse set of information, thus allowing a broad view into what affects online shopping behavior. Specific columns included cover customer's existing awareness about the website or source from where they are shopping, their information regarding the products they wish to purchase, and more importantly, their satisfaction level related to previous purchases.

Additionally, the dataset delves deep into investigating both objective and subjective aspects impacting customer behavior online. As such, it includes data on various webpage factors like loading speed, user-friendly interface design, webpage aesthetics, etc., which could significantly persuade the consumer's decision-making process during online shopping. The completion and submission convenience provided by those websites also form part of this database.

In order to fully understand consumer behavior within an online environment from multiple facets', individual consumers' subjective views are also captured in this dataset; it explores how consumers perceive their trust towards an e-commerce site or if they believe it’s convenient for them to shop via these platforms versus traditional methods? Do they feel relaxed when doing so?

In recognizing how crucial products competitiveness within such landscapes influences buyer intention - columns that provide details on critical characteristics like price comparisons against offline stores or similar product competitors across different websites have been included too.

Overall this comprehensive aggregated data collection aims not only at understanding fundamental consumer preferences but also towards predicting future buying behaviors hence forth enabling businesses capitalize on emerging trends within online retail spaces more efficiently & profitably

How to use the dataset

In an online-focused world, understanding consumer behavioral data is crucial. The 'Online Shopping Purchasing Intention Dataset' provides a comprehensive collection of consumer-based insights based on their behavior in virtual shopping environments. This dataset explores various factors that might affect a customer's decision to purchase. Here's how you can harness this dataset:

Defining the Problem

Identify a problem or question this data may answer. This might be: understanding what factors influence buying decisions, predicting whether a visit will result in a purchase based on user behavior, analyzing the impact of the month, operating system or traffic type on online purchasing intention etc.

Data Exploration

Understand the structure of the dataset by getting to know each variable and its meaning: - Administrative: Counting different types of pages visited by the user in that session. - Informational & Product Related: Measures how many informational/product related pages are viewed. - Bounce Rates, Exit Rate, Page Values: Assess these metrics as they provide significant insight about visitor activity. - Special Day: Explore correlation between proximity to special days (like Mother’s day and Valentine’s Day) with transactions. - Operating Systems / Browser / Region / Traffic Type: Uncover behavioral patterns associated with technical specs/geo location/ source of traffic.

Analysis and Visualization

Use appropriate statistical analysis techniques to scrutinize relationships between variables such as correlation analysis or chi-square tests for independence etc.

Visualize your findings using plots like bar graphs for categorical features comparison or scatter plots for multivariate relationships etc.

Model Building

Use machine learning algorithms (like logistic regression or decision tree models) potentially useful if your goal is predicting purchase intention based on given features.

This could also involve feature selection - choosing most relevant predictors; training & testing model and finally assessing model performance through metrics like accuracy score, precision-recall scores etc.

Remember to appropriately handle missing values if any before diving into predictive modeling

The comprehens...

Success.ai’s Retail Data for the Retail Sector in North America offers a comprehensive dataset designed to connect businesses with key players across the diverse retail industry. Covering everything from department stores and supermarkets to specialty shops and e-commerce platforms, this dataset provides verified contact details, business locations, and leadership profiles for retail companies in the United States, Canada, and Mexico.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, marketing, and business development efforts are powered by accurate, continuously updated, and AI-validated data.

Backed by our Best Price Guarantee, this solution empowers businesses to thrive in North America’s competitive retail landscape.

Why Choose Success.ai’s Retail Data for North America?

1. Verified Contact Data for Precision Outreach

   • Access verified phone numbers, work emails, and LinkedIn profiles of retail executives, store managers, and decision-makers.
   • AI-driven validation ensures 99% accuracy, enabling confident communication and efficient campaign execution.

2. Comprehensive Coverage Across Retail Segments

   • Includes profiles of retail businesses across major markets, from large department stores and grocery chains to boutique retailers and online platforms.
   • Gain insights into the operational dynamics of retail hubs in cities such as New York, Los Angeles, Toronto, and Mexico City.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, new store openings, market expansions, and shifts in consumer preferences.
   • Stay aligned with evolving industry trends and emerging opportunities in the North American retail sector.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible and lawful use of data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with executives, marketing directors, and operations managers across the North American retail sector.
• 30M Company Profiles: Access firmographic data, including revenue ranges, store counts, and geographic footprints.
• Store Location Data: Pinpoint retail outlets, regional offices, and distribution centers to refine supply chain and marketing strategies.
• Leadership Contact Details: Connect with CEOs, CMOs, and procurement officers influencing retail operations and vendor selections.

Key Features of the Dataset:

1. Retail Decision-Maker Profiles

   • Identify and engage with store owners, category managers, and marketing directors shaping customer experiences and product strategies.
   • Target professionals responsible for inventory planning, vendor contracts, and store performance.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (luxury, grocery, e-commerce), geographic location, company size, or revenue range.
   • Tailor outreach to align with regional market trends, customer demographics, and operational priorities.

3. Market Trends and Operational Insights

   • Analyze trends such as online shopping growth, sustainability practices, and supply chain optimization.
   • Leverage insights to refine product offerings, identify partnership opportunities, and design effective campaigns.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and enhance engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technology solutions to retail procurement teams, marketing departments, and operations managers.
   • Build relationships with retailers seeking innovative tools, efficient supply chain solutions, or unique product offerings.

2. Market Research and Consumer Insights

   • Analyze retail trends, customer behaviors, and seasonal demands to inform marketing strategies and product launches.
   • Benchmark against competitors to identify gaps, emerging niches, and growth opportunities.

3. E-Commerce and Digital Strategy Development

   • Target e-commerce managers and digital transformation teams driving online retail initiatives and omnichannel integration.
   • Offer solutions to enhance online shopping experiences, logistics, and customer loyalty programs.

4. Recruitment and Workforce Solutions

   • Engage HR professionals and hiring managers in recruiting talent for store operations, customer service, or marketing roles.
   • Provide workforce optimization tools, training platforms, or staffing services tailored to retail environments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing and outreach efforts in North America.

2. Seamless Integration
   ...

This dataset is from a study that aimed to test the impact of taxes and warning labels on red meat purchases in a naturalistic online grocery store. The dataset contains observations from n=3,518 US red meat consumers, sampled from a national panel. Eligibility criteria included: aged 18 years or older and self-reported consumption of red meat at least 1 or more times per week in the past 30 days. Participants were randomized to one of four conditions: control, warning labels, tax, or combined (warning labels and tax). Participants then completed a shopping task in a naturalistic online grocery store. Participants in the experimental conditions (warning labels, tax, or combined) saw red meat products with health and environmental warning labels about red meat, a 30% tax, or both, according to the assigned condition. Participants in the control condition saw no additional taxes or labels on red meat products. The co-primary outcomes were the count of products in the shopping basket that contained red meat and the percent of products in the shopping basket that contained red meat. Secondary outcomes included total saturated fat, sodium, and calories purchased, as well as cognitive elaboration, perceived healthfulness, perceived risk of cancer, and perceived environmental harms. The University of North Carolina at Chapel Hill Institutional Review Board approved this study (protocol #: 19-3349). This work was funded through a grant from the Wellcome Trust, grant id #216042/Z/19/Z.

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

pritamdeb68/Online-Shopping-Product-Catalog dataset hosted on Hugging Face and contributed by the HF Datasets community

Total-Current-Assets Time Series for Maplebear Inc.. Maplebear Inc., doing business as Instacart, engages in the provision of online grocery shopping services to households in North America. Its service can be provided through company's mobile application or website. The company also provides advertising services; and software-as-a-service solutions. Maplebear Inc., was incorporated in 2012 and is headquartered in San Francisco, California.

🏢 USA Online Shopping Companies Dataset (2024) This dataset showcases a snapshot of the top-performing online shopping companies in the United States as of 2024. It includes crucial business insights such as company names, their respective industries, annual revenues, growth rates, employee counts, and headquarters locations.

📦 Dataset Overview: Year Covered: 2024

Total Companies: N (replace with actual count)

Columns Included:

Rank – Position based on revenue or market performance

Name – Name of the company

Industry – Type of e-commerce business (e.g., Electronics, Fashion, Retail)

Revenue (USD millions) – Annual revenue in millions of USD

Revenue Growth – Percentage growth compared to the previous year

Employees – Number of employees in the company

Headquarters – City and state where the company is based

💡 Use Cases: Market research and industry analysis

Business intelligence dashboards

Growth trend modeling in e-commerce

Employment and revenue correlation studies

Geographic distribution of successful shopping companies

This dataset is ideal for analysts, students, and professionals aiming to explore the structure and performance of the leading online shopping companies in the USA.

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

Concerns about the numerous health problems associated with unhealthy snacks prompted recommendations to steer individuals toward healthier eating habits. One such recommendation advises limiting unhealthy snacks and replacing them with more fruits and vegetables with significant health benefits. This study investigates US consumers’ perceptions and preferences for healthy (vegetable-based) snacks/beverages. An online survey was designed to estimate consumer perception and willingness-to-pay (WTP) for vegetable-based crackers, spreads, and beverages. A sampling company sent the survey to its national consumer panels in 2020, resulting in a sample of 402 US consumers. Eligible participants were adults, primary grocery shoppers who consumed crackers, spreads, and beverages. Consumer WTP for healthy snacks/beverages, the dependent variable, was elicited using a payment card method. Independent variables include personality traits (Innovativeness and Extraversion) and the important factors affecting healthy snack purchases, health consciousness, and demographic variables. Results show that consumers’ preferences for healthy snacking vary by product, even when the products have similar health benefits. Significant positive associations exist between WTP for healthy snacks/beverages and personality traits, health consciousness, and some demographics. This study provides critical insights to policymakers and informs marketing campaigns to promote healthy snacking in the US more effectively.

Comprehensive dataset containing 1 verified Online shopping locations in California, United States with complete contact information, ratings, reviews, and location data.

About

This data is from E-Commerce. I used postgreSQL for data cleaning. I transformed NULL values to 'Not defined' and orginal data have only category name column(which was 'category_code') and that was 'DOT' seperated value which show us the products class from wide to specific. So I split them with delimeter('.').

The orignal data have record with 5 months but I only used December of 2019. If you want more data you can visit the link above and use.

File structure

Acknowledgements

Many thanks Thanks to REES46 Marketing Platform for this dataset and Michael Kechinov

Using datasets in your works, books, education materials

You can use this dataset for free. Just mention the source of it: link to this page and link to REES46 Marketing Platform and Origin data provider

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

The landscape of department store sales in the United States has undergone a significant transformation in recent years, and the sales data paints a clear picture of this shift. Once dominant players in the retail industry, large brick-and-mortar-based retailers like Macy's, Sears, and Kmart have struggled to adapt to the rapid changes in consumer behavior and the rise of e-commerce. Headlines such as "It’s Not Just Macy’s: Department Stores Are in a Death Spiral" and "Is your local Sears or Kmart among 150 stores to be axed? See the list" reflect the dire situation that many department stores find themselves in today. This trend has been evident for several years and continues to worsen, as highlighted by news reports in 2017 that cover store closings and layoffs across the country.

Department stores, which once thrived as the cornerstone of American shopping, particularly during holiday seasons, have seen their sales figures steadily decline. The 2016 holiday season, which was traditionally a major revenue generator, failed to deliver the kind of growth that these retailers desperately needed. The 2017 outlook wasn't much better, as large department stores remained far from finding the right formula to succeed in an increasingly digital shopping environment.

The challenges facing department stores are deeply rooted in broader changes within the retail trade sector. According to the Retail Trade and Food Services Report from the U.S. Census, which focuses specifically on department stores, the sales data clearly illustrates the difficult position these stores are in. Although the full Retail Trade data includes a variety of retail types, the department store category stands out for its troubling decline. This dataset reveals that department stores are struggling to keep up with evolving shopping habits, as consumers gravitate more toward online shopping platforms and smaller, more specialized retailers.

In years past, department stores were able to attract a wide range of customers by offering a variety of products under one roof. Shoppers could find everything from clothing and home goods to appliances and cosmetics in a single location, making department stores a convenient one-stop shop. However, with the rise of e-commerce giants like Amazon, the convenience that department stores once provided has been eclipsed by the ability to shop online from the comfort of one’s home, often with faster shipping times and lower prices. This shift in consumer preferences has put immense pressure on traditional department stores to innovate, but many have struggled to make the necessary changes.

The financial struggles of department stores are evident in the wave of store closures and layoffs that have swept across the country. Sears, once a retail titan, has been steadily closing stores for years, and its future remains uncertain. Similarly, Macy's, another iconic department store chain, has been forced to shutter locations and reduce its workforce in an effort to cut costs and stay afloat. These closures not only reflect the changing retail landscape but also have far-reaching consequences for employees, communities, and the commercial real estate market. The loss of a department store can leave a significant void in shopping malls and city centers, leading to reduced foot traffic and a decline in surrounding businesses.

While department stores have attempted various strategies to adapt, such as expanding their online presence, offering in-store experiences, and experimenting with smaller store formats, these efforts have not been enough to reverse their downward trajectory. The challenge for department stores moving forward will be to find a way to bridge the gap between their traditional business model and the modern retail environment. Without significant innovation and a deeper understanding of consumer preferences, department stores may continue to lose ground to more agile competitors in the retail space.

In conclusion, department stores in the United States are facing a period of significant upheaval, as reflected in the sales data from the U.S. Census and the widespread closures reported in the media. The once-dominant retail format has struggled to keep pace with the rapid changes in consumer behavior and the rise of e-commerce, resulting in a steady decline in sales and store closures. While some department stores are attempting to adapt to this new reality, the future remains uncertain, and it will take bold innovation for these retailers to regain their footing in an increasingly competitive retail landscape.

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Walmart is one of the world's largest and most well-known retail chains, often referred to as a "Global Superstore." Founded in 1962 by Sam Walton in Rogers, Arkansas, Walmart has grown to become a multinational retail giant with a presence in various countries. It's known for its vast and diverse product offerings, including groceries, electronics, clothing, household goods, and more.

Here are some key points that highlight Walmart's status as a Global Superstore:

• International Presence: Walmart operates in multiple countries across the globe, making it a truly global retailer. Its international expansion began in the 1990s, and it now has a presence in numerous countries, including the United States, Mexico, Canada, the United Kingdom, China, India, Brazil, and many others.

• Extensive Product Range: Walmart offers an extensive and diverse range of products, from everyday groceries to electronics, apparel, furniture, automotive supplies, and more. This vast selection of goods under one roof makes it a one-stop shop for a wide variety of consumer needs.

• Competitive Pricing: Walmart is known for its commitment to offering low prices, often leveraging its buying power to negotiate favorable deals with suppliers and pass those savings on to customers. This has helped it attract price-conscious shoppers and maintain a competitive edge.

• E-commerce and Online Presence: In addition to its physical stores, Walmart has a significant online presence through its e-commerce platform. It has invested heavily in expanding its digital retail capabilities, including online ordering, home delivery, and curbside pickup, to meet the changing shopping preferences of consumers.

• Private Label Brands: Walmart offers a range of private-label or store-brand products, providing more affordable alternatives to well-known brands. These private label products cover a wide range of categories and help draw budget-conscious customers.

• Sustainability Efforts: Walmart has made significant efforts to enhance its sustainability practices. It aims to reduce its environmental footprint by using renewable energy, improving energy efficiency, and implementing sustainable sourcing practices.

• Community Engagement: Walmart is actively involved in philanthropic activities and community engagement. The company supports various social and charitable initiatives, including education, hunger relief, and disaster relief efforts.

• Employment and Economic Impact: Walmart is one of the world's largest employers, providing jobs to millions of people across its global operations. Its presence in many communities contributes to local economies and often influences pricing and employment trends in those areas.

• Supply Chain and Logistics: Walmart is renowned for its efficient supply chain and distribution systems, which allow it to stock products, restock shelves, and move goods efficiently. This logistical expertise is a key element of its success as a Global Superstore.

Overall, Walmart's status as a Global Superstore is characterized by its massive reach, diverse product offerings, competitive pricing, and continuous efforts to adapt to changing consumer preferences and demands. Its global footprint and influence in the retail industry make it a prominent player in the world of commerce.

Analyzing Customer Spending Habits to Improve Sales Performance

A Cross-Country Study

By Vineet Bahl [source]

About this dataset

This Sales Data dataset offers a unique insight into the spending habits of customers from various countries across the globe. With detailed information on customer age, gender, product category, quantity, unit cost and price, as well as revenue generated through sales of products listed in this dataset, you can explore and discover patterns in consumer behavior. Analyze shifts in consumer trends with qualitative data like customer age and gender to know what drives customers’ decisions when shopping online or offline. Compare different markets to analyze pricing strategies for new product launches or promotional campaigns. Also with this dataset you can gain valuable insights about the changes in consumer demand for specific products over time – find out which Products had better margin or however see how different promotions impacted overall sales performance from different categories and sub-categories! Analyzing consumer behavior is key to success when it comes to commerce business models so this Sales Data offers powerful ways into understanding your customer base better!

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For more datasets, click here.

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How to use the dataset

This dataset presents a great opportunity to actively analyze customer spending habits on products and services to improve sales performance. The data contains information about the date of purchase, year, month, customer age, gender, country, state and product category. Further analysis can reveal insights into different customer segments based on their demographic characteristics such as age and gender as well as location (country & state).

The dataset also includes 3 additional columns at the end: quantity purchased in each transaction, unit cost and unit price for each product or service purchased which can be used to determine if customers are purchasing items in bulk or buying more expensive items than usual. Likewise any discrepancies between the unit cost & price can help establish whether discounts were applied during those transactions which could potentially point towards loyalty or reward programs being put in place for returning customers. Lastly the final column shows total revenue generated from those purchases which we can use to identify any patterns whereby certain groups of customers show higher purchasing power than others based on their spends (unit cost & quantity combination) over various periods/months/years of sales interactions with them.

In summary this dataset allows us to explore numerous dimensions related to ascertaining superior sales performance by studying how its various attributes play out together when it comes down to driving profitability through improved customer acquisition strategies as well increasing purchase rates from existing ones minus any discounts available in-between!

Research Ideas

• Analyzing customer demographics by countries and states to better target future marketing campaigns.

• Tracking changes in customers’ spending habits over time for different product categories.

• Identifying which product categories have the highest average revenue per sale to help prioritize resources for those products or services

Acknowledgements

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

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: SalesForCourse_quizz_table.csv | Column name | Description | |:---------------------|:--------------------------------------------------| | Date | Date of the sale. (Date) | | Year | Year of the sale. (Integer) | | Month | Month of the sale. (Integer) | | Customer Age | Age of the c...