This is a dataset obtained from an online survey conducted in August 2020. In the survey, participants were introduced to the concept of a smartphone-based shopping assistant application with the help of pictures and videos when shopping with and without the application. Participants were presented with three different shopping scenarios. In each scenario, we showed products on a shelf (groceries, luxury chocolate, shoes, books). The first shopping scenario was a regular shopping scenario (RSS), the second was an augmented reality shopping scenario (ARSS), and the third was an augmented reality shopping scenario with explainable AI features (XARSS). For each scenario participants had to answer questions about how they perceived the scenario and how it influenced their overall purchase intention. The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.

Graph and download economic data for E-Commerce Retail Sales as a Percent of Total Sales (ECOMPCTSA) from Q4 1999 to Q2 2025 about e-commerce, retail trade, percent, sales, retail, and USA.

Revolutionize Customer Engagement with Our Comprehensive Ecommerce Data

Our Ecommerce Data is designed to elevate your customer engagement strategies, providing you with unparalleled insights and precision targeting capabilities. With over 61 million global contacts, this dataset goes beyond conventional data, offering a unique blend of shopping cart links, business emails, phone numbers, and LinkedIn profiles. This comprehensive approach ensures that your marketing strategies are not just effective but also highly personalized, enabling you to connect with your audience on a deeper level.

What Makes Our Ecommerce Data Stand Out?

• Unique Features for Enhanced Targeting
  Our Ecommerce Data is distinguished by its depth and precision. Unlike many other datasets, it includes shopping cart links—a rare and valuable feature that provides you with direct insights into consumer behavior and purchasing intent. This information allows you to tailor your marketing efforts with unprecedented accuracy. Additionally, the integration of business emails, phone numbers, and LinkedIn profiles adds multiple layers to traditional contact data, enriching your understanding of clients and enabling more personalized engagement.

• Robust and Reliable Data Sourcing
  We pride ourselves on our dual-sourcing strategy that ensures the highest levels of data accuracy and relevance:

  • Real-Time Information from 10 Active Publication Sites: Our databases are continuously updated with the latest information, sourced from ten active publication sites that provide real-time data.
  • Dedicated Contact Discovery Team: Complementing our automated sources, our dedicated Contact Discovery Team conducts thorough research and investigations, ensuring that every piece of data is accurate and reliable. This two-pronged approach guarantees that our Ecommerce Data is both up-to-date and relevant, providing you with a solid foundation for your business strategies.

  Primary Use Cases Across Industries

Our Ecommerce Data is versatile and can be leveraged across various industries for multiple applications: - Precision Targeting in Marketing: Create personalized marketing campaigns based on detailed shopping cart activities, ensuring that your outreach resonates with individual customer preferences. - Sales Enrichment: Sales teams can benefit from enriched client profiles that include comprehensive contact information, enabling them to connect with key decision-makers more effectively. - Market Research and Analytics: Research and analytics departments can use this data for in-depth market studies and trend analyses, gaining valuable insights into consumer behavior and market dynamics.

Global Coverage for Comprehensive Engagement

Our Ecommerce Data spans across the globe, providing you with extensive reach and the ability to engage with customers in diverse regions: - North America: United States, Canada, Mexico - Europe: United Kingdom, Germany, France, Italy, Spain, Netherlands, Sweden, and more - Asia: China, Japan, India, South Korea, Singapore, Malaysia, and more - South America: Brazil, Argentina, Chile, Colombia, and more - Africa: South Africa, Nigeria, Kenya, Egypt, and more - Australia and Oceania: Australia, New Zealand - Middle East: United Arab Emirates, Saudi Arabia, Israel, Qatar, and more

Comprehensive Employee and Revenue Size Information

Our dataset also includes detailed information on: - Employee Size: Whether you’re targeting small businesses or large corporations, our data covers all employee sizes, from startups to global enterprises. - Revenue Size: Gain insights into companies across various revenue brackets, enabling you to segment the market more effectively and target your efforts where they will have the most impact.

Seamless Integration into Broader Data Offerings

Our Ecommerce Data is not just a standalone product; it is a critical piece of our broader data ecosystem. It seamlessly integrates with our comprehensive suite of business and consumer datasets, offering you a holistic approach to data-driven decision-making: - Tailored Packages: Choose customized data packages that meet your specific business needs, combining Ecommerce Data with other relevant datasets for a complete view of your market. - Holistic Insights: Whether you are looking for industry-specific details or a broader market overview, our integrated data solutions provide you with the insights necessary to stay ahead of the competition and make informed business decisions.

Elevate Your Business Decisions with Our Ecommerce Data

In essence, our Ecommerce Data is more than just a collection of contacts—it’s a strategic tool designed to give you a competitive edge in understanding and engaging your target audience. By leveraging the power of this comprehensive dataset, you can elevate your business decisions, enhance customer interactions, and navigate the digital landscape with confidence and insight.

Context:

This dataset provides detailed insights into consumer behaviour and shopping patterns across various demographics, locations, and product categories. It contains 3,900 customer records with 18 attributes that describe purchase details, shopping habits, and preferences.

The dataset includes information such as:

• Customer demographics (age, gender, location)
• Product details (item purchased, category, size, color, season)
• Purchase information (amount spent in USD, payment method, shipping type)
• Shopping behaviour (frequency of purchases, previous purchases, subscription status, discount usage, promo codes)
• Customer feedback (review ratings)

This dataset can be used to explore consumer decision-making and market trends, including:

• How age, gender, or location influence shopping preferences.
• The relationship between discounts, promo codes, and purchase amounts.
• Which product categories and colors are most popular in different seasons.
• Patterns in payment method usage (e.g., PayPal vs. Credit Card).
• How subscription and loyalty behaviours affect shopping frequency.

Researchers, data analysts, and students can use this dataset to practice customer segmentation, predictive modelling, recommendation systems, and market basket analysis. It also serves as a valuable resource for learning techniques in exploratory data analysis (EDA), machine learning, and business analytics.

Dataset Glossory(Column wise)

Customer ID: A unique identifier assigned to each customer. It helps distinguish one shopper’s data from another without revealing their personal identity.

Age: The age of the customer in years, which can provide insights into generational shopping habits and how preferences differ across age groups.

Gender: Indicates whether the customer is male or female, allowing analysis of gender-based buying trends and preferences in product categories.

Item Purchased: The specific product that the customer bought, giving a direct view of consumer demand and popular items in the dataset.

Category: The broader classification of the purchased item, such as clothing or footwear, which helps in grouping products and understanding category-level trends.

Purchase Amount (USD): The total money spent on the purchase in U.S. dollars, which reflects customer spending power and the value of each transaction.

Location: The state or region where the customer resides, useful for identifying geographical shopping patterns and regional differences in consumer behaviour.

Size: The size of the purchased item (e.g., S, M, L), which helps reveal customer preferences in apparel and how sizing impacts sales.

Color: The chosen color of the purchased item, offering insights into which colors are more appealing to consumers during different seasons or product categories.

Season: The season (Winter, Spring, etc.) in which the purchase was made, showing how customer demand changes across seasonal trends.

Review Rating: A numerical score reflecting the customer’s satisfaction with the product, valuable for measuring quality perception and post-purchase behaviour.

Subscription Status: Indicates whether the customer has an active subscription with the store, which may influence loyalty, discounts, and purchase frequency.

Shipping Type: The delivery option chosen by the customer, such as free shipping or express, which highlights convenience preferences and urgency of purchase.

Discount Applied: Shows whether a discount was used during the purchase, allowing analysis of how discounts affect buying decisions and sales growth.

Promo Code Used: Specifies if the customer used a promotional code, useful for understanding the impact of marketing strategies on purchase behaviour.

Previous Purchases: The number of items the customer has bought before, reflecting their shopping history and overall loyalty to the store.

Payment Method: The mode of payment used (Credit Card, PayPal, etc.), which sheds light on financial behaviour and preferred transaction methods.

Frequency of Purchases: Indicates how often the customer engages in purchasing activities, a critical metric for assessing customer loyalty and lifetime value.

Acknowledgment

Special thanks to Sir Sourav Banerjee Associate Data Scientist at CogniTensor

Kolkata, West Bengal, India

Retail Sales in the United States increased 0.60 percent in August of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

Sales records for the year 2011-2014 with 3 Product, 17 sub-categories over different segments is recorded. Objective is to expand the business in profitable regions based on the growth percentage and profits.

Data Dictionary

Order ID: A unique ID given to each order placed. Order Date: The date at which the order was placed. Customer Name: Name of the customer placing the order. Country: The country to which the customer belongs to. State: The state to which the customer belongs from the country. City:Detail about the city to which the customer resides in. Region: Contains the region details. Segment:The ordered product belongs to what segment. Ship Mode: The mode of shipping of the order to the customer location. Category: Contains the details about what category the product belongs to. Sub : Category: Contains the details about what sub - category the product belongs to. Product Name:The name of the product ordered by the customer. Discount: The discount applicable on a product. Sales: The actual sales happened for a particular order. Profit: Profit earned on an order. Quantity:The total quantity of the product ordered in a single order. Feedback: The feedback given by the customer on the complete shopping experience. If feedback provided, then TRUE. If no feedback provided, then FALSE.

Inspiration

This data-set can be helpful to analyze data to develop marketing strategies and to measure parameters like customer retention rate,churn rate etc.

Up-Vote⬆️ for more such dataset

This survey shows the rating of how willing consumers are to share various types of information with retailers in order to have a more personalised shopping experience in the UK, by age of consumer. According to the survey conducted in 2013, respondents aged 18 to 33 were least willing to share their phone number collected at point of sale, with a rating of *** out of 5.

The Retail Sales Index (RSI) is like a health check-up for the shopping world, done every three (3) months. Imagine visiting many different stores, from big to small, and noting how much they are selling. That is what the RSI does. It adds up the sales from these stores to get a feel for how well retail businesses are doing. This index helps us understand if people spend more or less at shops, which is a big deal for the economy. Think of it as a way to gauge our shopping habits. Plus, by comparing it with the Retail Price Index (RPI), which tracks price changes, we can see how much we are spending but how much stuff we are actually buying, considering price changes.

The mobile phone data were collected in February 2013 together with the National Consumer Net Shopping Study conducted by market research company Tietoykkönen Oy. The target group was 15--79 years old mobile phone owners in Finland. The data collection method was telephone interviews by using a computer-assisted telephone interviewing (CATI) system. The sample source was targeting service Fonecta Finder B2C, which contains all publicly available phone numbers in Finland. Random sampling was made by setting quotas in respondents’ gender, age and region in the major region level excluding Åland autonomic region. The sample size was 536 completed interviews. All 536 survey respondents had a mobile phone. The respondents answered the following questions (originally in Finnish): What is the brand of your mobile phone? When did you purchase your mobile phone? (year and month; if the month was not recalled the season was asked) What was the brand of your previous mobile phone? When did you purchase your previous mobile phone? (year and month; if the month was not recalled the season was asked) Which brand would be the most interesting for you if you were to buy a mobile phone now? Is your mobile phone a smart phone, a feature phone with an internet connection or a phone without an internet connection? In addition, the respondents where asked for their gender, age group (six categories: 15--24, 25--34, 35--44, 45--55, 55--64 and 65--79 years), geographical region (Helsinki-Uusimaa, Southern Finland, Western Finland, Northern & Eastern Finland) and income of the household (five categories: 30,000 euros or less, 30,001--50,000 euros, 50,001--70,000 euros, over 70,000 euros and no answer). Citation: Cannot be used without citation to J. Karvanen, A. Rantanen, L. Luoma, Survey data and Bayesian analysis: a cost-efficient way to estimate customer equity. Quantitative Marketing and Economics, DOI:10.1007/s11129-014-9148-4, 2014. (preprint available at http://arxiv.org/pdf/1304.5380)

Retail Fashion Boutique Data Sales Analytics 2025

Overview

This comprehensive fashion retail synthetic dataset contains 2,176 real-world style records spanning seasonal collections, customer purchasing behavior, pricing strategies, and return analytics. Perfect for data science projects, machine learning models, and business intelligence dashboards focused on retail analytics and e-commerce insights.

Dataset Highlights

• 📊 Complete Sales Cycle: Purchase patterns, pricing strategies, and customer feedback
• 🔄 Return Analytics: Detailed return tracking with specific reasons and patterns
• 🛍️ Multi-Brand Coverage: 8 major fashion brands across diverse product categories
• 📈 Seasonal Intelligence: Four-season data with realistic markdown strategies
• ⭐ Customer Insights: Rating systems and purchasing behavior analysis
• 💰 Pricing Analytics: Original pricing, markdown percentages, and final pricing data

Key Applications

• Retail Analytics: Sales performance analysis and trend identification
• Customer Segmentation: Behavior analysis and purchasing pattern recognition
• Inventory Management: Stock optimization and seasonal demand forecasting
• Return Prediction: Machine learning models for return likelihood prediction
• Pricing Strategy: Dynamic pricing and markdown optimization analysis
• Business Intelligence: Comprehensive retail KPI dashboards and reporting

Column Details

Data Quality Features

• ✅ Realistic Business Logic: 15% return rate matching industry standards
• ✅ Seasonal Pricing: Authentic markdown patterns aligned with retail cycles
• ✅ Missing Data Handling: Strategic nulls for data cleaning practice (15% in ratings, size nulls for accessories)
• ✅ Balanced Distribution: Even representation across brands, categories, and seasons
• ✅ Price Consistency: Mathematically accurate pricing with discount calculations

Perfect For

• Data Analytics Projects: Retail KPI analysis, sales forecasting, customer behavior studies
• Machine Learning Models: Return prediction, demand forecasting, recommendation systems
• Business Intelligence: Executive dashboards, performance tracking, trend analysis
• Academic Research: Retail analytics case studies, pricing strategy research
• Portfolio Development: Comprehensive data science project demonstrations

File Formats Available

• CSV: Universal compatibility for data analysis tools
• Excel: Business reporting and stakeholder presentations
• JSON: API integration and web applications
• SQL: Database integration and advanced querying

Sample Use Cases

1. Return Prediction Model: Build ML models to predict return likelihood based on product attributes
2. Seasonal Demand Forecasting: Analyze purchasing patterns across different seasons and categories
3. Pricing Optimization: Study markdown effectiveness and optimal pricing strategies
4. Customer Satisfaction Analysis: Correlate ratings with return patterns and product characteristi...

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

https://i.imgur.com/6UEqejq.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

Dataset resulting from tracking 200 respondents in six town centres to examine consumer behaviour in relation to the changing landscape of town centres. Research was carried out in Swindon, Huddersfield, Watford, Loughborough, Bury St Edmunds and Sandbach; typical for the type of towns that could be under threat from rival forms of retailing. To start with, focus groups explored initial customer experience questions/issues. Respondents kept online diaries in which they logged every single element of their shopping activity over the course of a four-week period. This included where they shopped, how much time and money they spent in doing so and how they used internet or mobile technology to support or supplant the physical shopping experience. Respondents also completed a weekly questionnaire in which they recounted the critical incidents occurring during their “customer experience journey”. The study identified 11 key interactions or “touch points”, some of them physical (or “functional”) and some intangible (or “experiential”). The plight of Britain’s town centres has attracted attention at the highest levels in recent years. The government has introduced a number of actions, from the Portas Review to the Future High Streets Forum, in response to undeniable evidence of economic decline. It is clear that town centres are changing, as is the way in which shoppers use them. With the internet offering unprecedented choice, comparison and convenience, consumers have come to expect more from town and city centres. Yet, although we are all deeply aware of this fundamental shift, surprisingly little is known about what actually constitutes the customer experience in a town centre. This research aims to fill that knowledge gap. By examining consumer behaviour in relation to the changing landscape of town centres, it provides evidence of how the customer experience is formed from a consumer perspective. Data were collected in various ways. Focus groups discussions explore initial customer experience questions/issues. Online diaries were kept to log every single element of consumer shopping activity over a course of a 4 week period. Weekly questionnaires with recount the critical incidence occurring from consumers’ journey. Telephone exit interviews to assess the importance/ perceived quality of touch points.

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

Comprehensive dataset containing 20,187 verified Cell phone store businesses in Turkey with complete contact information, ratings, reviews, and location data.

Analysis of ‘Store Transaction data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/iamprateek/store-transaction-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Nielsen receives transaction level scanning data (POS Data) from its partner stores on a regular basis. Stores sharing POS data include bigger format store types such as supermarkets, hypermarkets as well as smaller traditional trade grocery stores (Kirana stores), medical stores etc. using a POS machine.

While in a bigger format store, all items for all transactions are scanned using a POS machine, smaller and more localized shops do not have a 100% compliance rate in terms of scanning and inputting information into the POS machine for all transactions.

A transaction involving a single packet of chips or a single piece of candy may not be scanned and recorded to spare customer the inconvenience or during rush hours when the store is crowded with customers.

Thus, the data received from such stores is often incomplete and lacks complete information of all transactions completed within a day.

Additionally, apart from incomplete transaction data in a day, it is observed that certain stores do not share data for all active days. Stores share data ranging from 2 to 28 days in a month. While it is possible to impute/extrapolate data for 2 days of a month using 28 days of actual historical data, the vice versa is not recommended.

Nielsen encourages you to create a model which can help impute/extrapolate data to fill in the missing data gaps in the store level POS data currently received.

Content

You are provided with the dataset that contains store level data by brands and categories for select stores-

Hackathon_ Ideal_Data - The file contains brand level data for 10 stores for the last 3 months. This can be referred to as the ideal data.

Hackathon_Working_Data - This contains data for selected stores which are missing and/or incomplete.

Hackathon_Mapping_File - This file is provided to help understand the column names in the data set.

Hackathon_Validation_Data - This file contains the data stores and product groups for which you have to predict the Total_VALUE.

Sample Submission - This file represents what needs to be uploaded as output by candidate in the same format. The sample data is provided in the file to help understand the columns and values required.

Acknowledgements

Nielsen Holdings plc (NYSE: NLSN) is a global measurement and data analytics company that provides the most complete and trusted view available of consumers and markets worldwide. Nielsen is divided into two business units. Nielsen Global Media, the arbiter of truth for media markets, provides media and advertising industries with unbiased and reliable metrics that create a shared understanding of the industry required for markets to function. Nielsen Global Connect provides consumer packaged goods manufacturers and retailers with accurate, actionable information and insights and a complete picture of the complex and changing marketplace that companies need to innovate and grow. Our approach marries proprietary Nielsen data with other data sources to help clients around the world understand what’s happening now, what’s happening next, and how to best act on this knowledge. An S&P 500 company, Nielsen has operations in over 100 countries, covering more than 90% of the world’s population.

Know more: https://www.nielsen.com/us/en/

Inspiration

Build an imputation and/or extrapolation model to fill the missing data gaps for select stores by analyzing the data and determine which factors/variables/features can help best predict the store sales.

--- Original source retains full ownership of the source dataset ---

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Abstract copyright UK Data Service and data collection copyright owner. To ascertain the shopping habits of housewives and their attitudes towards shopping facilities in the area. Main Topics: Attitudinal/Behavioural Questions Data on shopping habits: day of the week on which most shopping is done (also when particular foodstuffs are normally bought), where various foodstuffs and durable goods are usually bought (11 categories), mode of transport used, use of Coalville Co-op, frequency of shopping at precinct (and whether used mostly during the week or at weekends), when last bought foodstuffs and durable goods at precinct and shops then used, amount spent on all shopping in last week (i.e. durable goods included), proportion of this spent at: shops in Coalville, precinct shops, the Co-op. Attitudes towards: Coalville Co-op (best and worst aspects, changes respondent would like to see), the precinct (main advantage, major difference it has made to Coalville, worst thing about it). A collection of statements about the Coalville Co-op and precinct are included, about which respondents are asked whether they agree or disagree - e.g. ''the Co-op does not have as big a variety of groceries as the precinct''. Attitudes on the shopping facilities in the precinct are gauged more precisely. This includes a section comprising 18 statements (favourable) with which respondents are asked if they agree or disagree. The effectiveness of precinct publicity and advertising is assessed. Finally, respondents are asked, more generally, whether they think that shopping facilities are better in Loughborough, Winkley or Ashby than in Coalville. Background Variables Age cohort, marital status, working status (e.g. full-time etc.), number and age of children, occupation and place of work of head of household, social status, car ownership (if owned, whether respondent ever drives it or uses it for shopping), whether member of Coalville Co-op, if so, amount spent there in last six months (i.e. last dividend - 8 categories), number of shopping visits generally made per week. Finally, whether respondent goes to Coalville for a special purpose, i.e. to collect a government grant of any sort, because of a bank account there, to go to cinema or bingo. Random routes within defined areas

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

Analysis of ‘Retail Case Study Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/darpan25bajaj/retail-case-study-data on 13 November 2021.

--- Dataset description provided by original source is as follows ---

Analytics in Retail:

With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival.

Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers.

Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior.To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. In a nutshell, for big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others.

About the Data

A Retail store is required to analyze the day-to-day transactions and keep a track of its customers spread across various locations along with their purchases/returns across various categories.

What can be done with the data?

Create a report and display the calculated metrics, reports and inferences.

Data Schema

This book has three sheets (Customer, Transaction, Product Hierarchy):

• Customer: Customer information including demographics
• Transaction: Transaction of customers
• Product Hierarchy: Product information

--- Original source retains full ownership of the source dataset ---