Dataset

This dataset was created by Ravi Kolluru

Contents

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

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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

Abstract

The L2 Consumer Dataset contains information about consumers from all 50 states and the District of Columbia. The data, which is sourced from credit bureaus and other consumer information sources, is generally bought and used by companies for marketing purposes. Updates are expected quarterly.

All tables (except for New Jersey) were last updated on 03-25-2024. New Jersey was updated on 05-11-2024, when about 25,000 records were removed to comply with Daniel's Law.

Methodology

To create this file, L2 processes nationwide consumer data on an ongoing basis for all 50 states and the District of Columbia with refreshes typically at least every ninety days. The data are sourced from credit bureaus and other consumer information sources. Those data are standardized and consist of approximately 240,000,000 records nationwide.

Usage

Each table contains 667 variables. For more information about these variables, see ***2024-04-20-Commercial-Data-Dictionary.xlsx ***(under Supporting files).

The L2 Consumer and L2 Voter and Demographic data can be joined on the Lalvoterid variable.

One can also use the Lalvoterid variable to validate the state. For example, let's look at the Lalvoterid for one row in the CA-Commercial-2024-03-25 table. The characters in the fourth and fifth positions of this identifier, LALCA25840445, are 'CA' (California).

The date appended to each table name represents when the data was last updated. All tables (except for New Jersey) were last updated on 03-25-2024. New Jersey was updated on 05-11-2024 when about 25,000 records were removed to comply with Daniel's Law. For more information about this release, see 2024-03-27-Commercial-Data-Release-Notes.docx* *(under Supporting files).

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

1) Data Introduction • The Sample Sales Data is a retail sales dataset of 2,823 orders and 25 columns that includes a variety of sales-related data, including order numbers, product information, quantity, unit price, sales, order date, order status, customer and delivery information.

2) Data Utilization (1) Sample Sales Data has characteristics that: • This dataset consists of numerical (sales, quantity, unit price, etc.), categorical (product, country, city, customer name, transaction size, etc.), and date (order date) variables, with missing values in some columns (STATE, ADDRESSLINE2, POSTALCODE, etc.). (2) Sample Sales Data can be used to: • Analysis of sales trends and performance by product: Key variables such as order date, product line, and country can be used to visualize and analyze monthly and yearly sales trends, the proportion of sales by product line, and top sales by country and region. • Segmentation and marketing strategies: Segmentation of customer groups based on customer information, transaction size, and regional data, and use them to design targeted marketing and customized promotion strategies.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

ExactOne delivers unparalleled consumer transaction insights to help investors and corporate clients uncover market opportunities, analyze trends, and drive better decisions.

Dataset Highlights - Source: Debit and credit card transactions from 600K+ active users and 2M accounts connected via Open Banking. Scale: Covers 250M+ annual transactions, mapped to 1,800+ merchants and 400+ tickers. Historical Depth: Over 6 years of transaction data. Flexibility: Analyse transactions by merchant/ticker, category/industry, or timeframe (daily, weekly, monthly, or quarterly).

ExactOne data offers visibility into key consumer industries, including: Airlines - Regional / Budget Airlines - Cargo Airlines - Full Service Autos - OEMs Communication Services - Cable & Satellite Communication Services - Integrated Telecommunications Communication Services - Wireless Telecom Consumer - Services Consumer - Health & Fitness Consumer Staples - Household Supplies Energy - Utilities Energy - Integrated Oil & Gas Financial Services - Insurance Grocers - Traditional Hotels - C-corp Industrial - Misc Industrial - Tools And Hardware Internet - E-commerce Internet - B2B Services Internet - Ride Hailing & Delivery Leisure - Online Gambling Media - Digital Subscription Real Estate - Brokerage Restaurants - Quick Service Restaurants - Fast Casual Restaurants - Pubs Restaurants - Specialty Retail - Softlines Retail - Mass Merchants Retail - European Luxury Retail - Specialty Retail - Sports & Athletics Retail - Footwear Retail - Dept Stores Retail - Luxury Retail - Convenience Stores Retail - Hardlines Technology - Enterprise Software Technology - Electronics & Appliances Technology - Computer Hardware Utilities - Water Utilities

Use Cases

For Private Equity & Venture Capital Firms: - Deal Sourcing: Identify high-growth opportunities. - Due Diligence: Leverage transaction data to evaluate investment potential. - Portfolio Monitoring: Track performance post-investment with real-time data.

For Consumer Insights & Strategy Teams: - Market Dynamics: Compare sales trends, average transaction size, and customer loyalty. - Competitive Analysis: Benchmark market share and identify emerging competitors. - E-commerce vs. Brick & Mortar Trends: Assess channel performance and strategic opportunities. - Demographic & Geographic Insights: Uncover growth drivers by demo and geo segments.

For Investor Relations Teams: - Shareholder Insights: Monitor brand performance relative to competitors. - Real-Time Intelligence: Analyse sales and market dynamics for public and private companies. - M&A Opportunities: Evaluate market share and growth potential for strategic investments.

Key Benefits of ExactOne - Understand Market Share: Benchmark against competitors and uncover emerging players. - Analyse Customer Loyalty: Evaluate repeat purchase behavior and retention rates. - Track Growth Trends: Identify key drivers of sales by geography, demographic, and channel. - Granular Insights: Drill into transaction-level data or aggregated summaries for in-depth analysis.

With ExactOne, investors and corporate leaders gain actionable, real-time insights into consumer behaviour and market dynamics, enabling smarter decisions and sustained growth.

This is a sample dataset of Telco Customer Churn. It's inspired by the original dataset of "Telco customer churn (11.1.3+)" from IBM Business Analytics Community. This sample dataset is being cleaned and aggregated from the original dataset. It would be good for telco customer churn analysis or prediction by the classification or regression model for experiment and learning purposes.

Column Description: * customerID: A unique ID that identifies each customer. * gender: The customer’s gender: Male (1), Female (0). * SeniorCitizen: Indicates if the customer is 65 or older: No (0), Yes (1). * Partner: Service contract is resold by the partner: No (0), Yes (1). * Dependents: Indicates if the customer lives with any dependents: No (0), Yes (1). * Tenure: Indicates the total amount of months that the customer has been with the company. * PhoneService: Indicates if the customer subscribes to home phone service with the company: No (0), Yes (1). * MultipleLines: Indicates if the customer subscribes to multiple telephone lines with the company: No (0), Yes (1). * InternetService: Indicates if the customer subscribes to Internet service with the company: No (0), DSL (1), Fiber optic (2). * OnlineSecurity: Indicates if the customer subscribes to an additional online security service provided by the company: No (0), Yes (1), NA (2). * OnlineBackup: Indicates if the customer subscribes to an additional online backup service provided by the company: No (0), Yes (1), NA (2). * DeviceProtection: Indicates if the customer subscribes to an additional device protection plan for their Internet equipment provided by the company: No (0), Yes (1), NA (2). * TechSupport: Indicates if the customer subscribes to an additional technical support plan from the company with reduced wait times: No (0), Yes (1), NA (2). * StreamingTV: Indicates if the customer uses their Internet service to stream television programing from a third party provider: No (0), Yes (1), NA (2). The company does not charge an additional fee for this service. * StreamingMovies: Indicates if the customer uses their Internet service to stream movies from a third party provider: No (0), Yes (1), NA (2). The company does not charge an additional fee for this service. * Contract: Indicates the customer’s current contract type: Month-to-Month (0), One Year (1), Two Year (2). * PaperlessBilling: Indicates if the customer has chosen paperless billing: No (0), Yes (1). * PaymentMethod: Indicates how the customer pays their bill: Bank transfer - automatic (0), Credit card - automatic (1), Electronic cheque (2), Mailed cheque (3). * MonthlyCharges: Indicates the customer’s current total monthly charge for all their services from the company. * TotalCharges: Indicates the customer’s total charges. * Churn: Indicates if the customer churn or not: No (0), Yes (1).

Research Hypothesis: Using the concept of Neutrosophy to deal Indeterminacy in Feedback

Data: Feedback given by customers of a restaurant. Questionnaire based on six factors, i.e., Quality of Food, Service, Hygiene, Value for money, Ambiance, Overall Experience. Each question (based on the factor) has five membership values as follows: , Positive, Positive Indeterminate, Indeterminate, Negative Indeterminate and Negative.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

Subtitle:

Simulated Dataset of Customer Purchase Behavior

Description:

This dataset contains simulated data representing customer purchase behavior. It includes various features such as age, gender, income, education, region, loyalty status, purchase frequency, purchase amount, product category, promotion usage, and satisfaction score.

File Information:

• File Format: CSV
• Number of Rows: 100000
• Number of Columns: 12

Column Descriptors:

• age: Age of the customer.
• gender: Gender of the customer (0 for Male, 1 for Female).
• income: Annual income of the customer.
• education: Education level of the customer.
• region: Region where the customer resides.
• loyalty_status: Loyalty status of the customer.
• purchase_frequency: Frequency of purchases made by the customer.
• purchase_amount: Amount spent by the customer in each purchase.
• product_category: Category of the purchased product.
• promotion_usage: Indicates whether the customer used promotional offers (0 for No, 1 for Yes).
• satisfaction_score: Satisfaction score of the customer.

Provenance:

The dataset was simulated using the simstudy package in R. Various distributions and formulas were used to generate synthetic data representing customer purchase behavior. The data is organized to mimic real-world scenarios, but it does not represent actual customer data.

Success.ai’s Consumer Marketing Data for Food, Beverage & Consumer Goods Professionals Globally provides a comprehensive dataset tailored for businesses seeking to connect with decision-makers and marketing professionals in these dynamic industries. Covering roles such as brand managers, marketing strategists, and product developers, this dataset offers verified contact details, decision-maker insights, and actionable business data.

With access to over 700 million verified global profiles, Success.ai ensures your marketing, sales, and research efforts are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is essential for businesses aiming to lead in the food, beverage, and consumer goods sectors.

Why Choose Success.ai’s Consumer Marketing Data?

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   • Includes profiles of professionals from food and beverage companies, consumer goods manufacturers, and marketing agencies in key markets worldwide.
   • Gain insights into regional trends in product marketing, consumer engagement, and purchasing behaviors.

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   • Real-time updates capture changes in professional roles, company strategies, and market trends.
   • Stay aligned with the fast-evolving consumer goods industry to identify emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with decision-makers, marketers, and product managers in the food, beverage, and consumer goods sectors worldwide.
• Leadership Insights: Gain detailed profiles of brand managers, marketing executives, and product developers shaping consumer trends.
• Contact Details: Access verified phone numbers and work emails for precision outreach.
• Industry Trends: Understand global marketing trends, regional consumer preferences, and market dynamics.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key professionals managing brand strategies, product launches, and marketing campaigns in the food, beverage, and consumer goods industries.
   • Access data on career histories, certifications, and market expertise for targeted outreach.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (snack foods, beverages, household goods), geographic location, or job function.
   • Tailor campaigns to align with specific needs such as product placement, consumer engagement, or regional expansion.

3. Regional Trends and Consumer Insights

   • Leverage data on consumer preferences, product demand, and spending patterns in key markets.
   • Use these insights to refine product offerings, marketing strategies, and audience targeting.

4. AI-Driven Enrichment

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

Strategic Use Cases:

1. Marketing Campaigns and Brand Outreach

   • Design targeted campaigns for food, beverage, and consumer goods products based on verified data and consumer insights.
   • Leverage multi-channel outreach, including email, phone, and digital advertising, to maximize engagement.

2. Product Development and Launch Strategies

   • Utilize consumer trend data to guide product development and market entry strategies.
   • Collaborate with brand managers and marketing professionals to align offerings with consumer preferences.

3. Sales and Partnership Development

   • Build relationships with distributors, retailers, and marketers in the consumer goods supply chain.
   • Present co-branding opportunities, joint marketing campaigns, or distribution strategies to decision-makers.

4. Market Research and Competitive Analysis

   • Analyze global trends in consumer goods marketing, product innovations, and purchasing behaviors to refine strategies.
   • Benchmark against competitors to identify growth opportunities, underserved markets, and high-demand products.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality consumer marketing data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, marketing platforms, or analytics tools via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Acc...

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.

WiserBrand's Comprehensive Customer Call Dataset: A Decade of Insights

WiserBrand offers an unparalleled dataset comprising over 16 million customer call records, meticulously gathered over the past 10 years and updated daily. This extensive dataset includes:

• User ID and Firm Name: Identify and categorize calls by unique user IDs and company names.
• Call Duration: Analyze engagement levels through call lengths.
• Geographical Information: Detailed data on city, state, and country for regional analysis.
• Call Timing: Track peak interaction times with precise timestamps.
• Call Reason and Group: Categorized reasons for calls, helping to identify common customer issues.
• Device and OS Types: Information on the devices and operating systems used for technical support analysis.

We can build a dataset based on your request, by category, industry, company, date, etc.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data and Consumer Behavior applications.

The more you purchase, the lower the price will be.

Success.ai’s B2C Contact Data Real-Time API provides businesses with on-demand access to continuously updated consumer information, ensuring your marketing and engagement strategies always remain current and impactful. By leveraging AI-validated data from over 700 million global profiles, this API empowers you to adapt swiftly to changes in consumer demographics, behaviors, and purchasing patterns.

From personalizing offers to targeting the right audiences at the right time, Success.ai’s real-time consumer data ensures every interaction is more relevant, timely, and effective. Backed by our Best Price Guarantee, this solution helps you stay ahead in a rapidly evolving consumer market.

Why Choose Success.ai’s B2C Contact Data Real-Time API?

1. Continuously Updated Consumer Data

   • Access the most recent consumer profiles, ensuring you’re always engaging with active, relevant audiences.
   • Real-time data refreshes keep pace with shifting consumer trends, enabling agile decision-making.

2. Comprehensive Global Coverage

   • Includes consumer information from key markets worldwide, allowing you to scale campaigns and tap into emerging demographics.
   • Gain insights into purchasing behaviors, brand preferences, and lifestyle indicators across regions and sectors.

3. AI-Validated Accuracy and Reliability

   • AI-driven validation ensures 99% accuracy, reducing wasted outreach and maximizing campaign success rates.
   • Trust that your data is always high-quality, actionable, and ready to inform your marketing strategies.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring your data usage remains responsible and lawful.

Data Highlights:

• 700M+ Global Profiles: Access a vast and diverse pool of consumer data for more informed targeting.
• Real-Time Updates: Continuously updated data ensures timely relevance, supporting dynamic marketing strategies.
• Behavioral and Lifestyle Insights: Understand consumer behaviors, interests, and preferences to tailor campaigns and messaging.
• Demographic and Purchasing Patterns: Leverage key indicators like age, location, and past purchase behaviors to refine targeting.

Key Features of the Real-Time API:

1. Instant Data Enrichment

   • Seamlessly enhance your CRM or marketing platforms with fresh consumer data, eliminating manual updates and data decay.
   • Maintain top-notch data hygiene to support long-term ROI on marketing efforts.

2. Powerful Filtering and Segmentation

   • Query the API using advanced parameters like demographics, interests, or purchase history.
   • Zero in on precise audience segments for higher conversion rates and personalized consumer experiences.

3. Adaptive Marketing Campaigns

   • Respond quickly to evolving market conditions, seasonal trends, or shifting consumer preferences.
   • Dynamically adjust campaigns and content strategies as new data emerges, ensuring ongoing relevance.

4. AI-Driven Validation

   • Rely on an AI-powered validation framework that continuously verifies data accuracy.
   • Improve reliability and reduce the risk of inaccurate targeting or messaging.

Strategic Use Cases:

1. Personalized Marketing Campaigns

   • Tailor your messaging, offers, and content based on real-time consumer insights.
   • Increase engagement, loyalty, and sales by delivering relevant experiences that resonate with target audiences.

2. Audience Expansion and Market Entry

   • Identify new consumer segments and emerging markets supported by the latest consumer profiles.
   • Confidently enter new territories or product categories, backed by high-quality, up-to-date data.

3. Competitive Analysis and Market Insights

   • Monitor changing consumer preferences, compare segments, and spot trends before competitors do.
   • Refine product development, pricing strategies, and promotions to stay ahead of industry shifts.

4. Enhanced Customer Support and Retention

   • Equip support teams with updated consumer data to address inquiries more effectively.
   • Strengthen customer relationships through personalized interactions and timely problem resolution.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality B2C contact data at highly competitive prices, ensuring strong ROI for your marketing, sales, and operational initiatives.

2. Seamless Integration

   • Integrate the Real-Time API into CRM systems, marketing automation tools, or analytics platforms with ease, streamlining workflows and minimizing complexity.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and enhance overall engagement outcomes.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific demographics, interests, or regions, adapting as your business needs evolve a...

Example of modeled customer behavioral data showing user sessions, engagement metrics, and conversion data across multiple platforms and devices

This study aimed to understand consumers’ experiences with purchasing items and services, the type and impact of problems they faced in doing so, and how they managed to resolve these problems. Detailed analysis tables The first set of tables contains data on respondents: these can be used to explore what socio-economic demographics (for example age, gender, ethnicity) are associated with given detriment outcomes and patterns. The second set of tables cover the detriment instances experienced and show in more detail how they vary by type of product, channel of purchase, type of problem, and more.

Dataset Description: E-commerce Customer Behavior

Overview: This dataset provides a comprehensive view of customer behavior within an e-commerce platform. Each entry in the dataset corresponds to a unique customer, offering a detailed breakdown of their interactions and transactions. The information is crafted to facilitate a nuanced analysis of customer preferences, engagement patterns, and satisfaction levels, aiding businesses in making data-driven decisions to enhance the customer experience.

Columns:

1. Customer ID:

   • Type: Numeric
   • Description: A unique identifier assigned to each customer, ensuring distinction across the dataset.

2. Gender:

   • Type: Categorical (Male, Female)
   • Description: Specifies the gender of the customer, allowing for gender-based analytics.

3. Age:

   • Type: Numeric
   • Description: Represents the age of the customer, enabling age-group-specific insights.

4. City:

   • Type: Categorical (City names)
   • Description: Indicates the city of residence for each customer, providing geographic insights.

5. Membership Type:

   • Type: Categorical (Gold, Silver, Bronze)
   • Description: Identifies the type of membership held by the customer, influencing perks and benefits.

6. Total Spend:

   • Type: Numeric
   • Description: Records the total monetary expenditure by the customer on the e-commerce platform.

7. Items Purchased:

   • Type: Numeric
   • Description: Quantifies the total number of items purchased by the customer.

8. Average Rating:

   • Type: Numeric (0 to 5, with decimals)
   • Description: Represents the average rating given by the customer for purchased items, gauging satisfaction.

9. Discount Applied:

   • Type: Boolean (True, False)
   • Description: Indicates whether a discount was applied to the customer's purchase, influencing buying behavior.

10. Days Since Last Purchase:

    • Type: Numeric
    • Description: Reflects the number of days elapsed since the customer's most recent purchase, aiding in retention analysis.

11. Satisfaction Level:

    • Type: Categorical (Satisfied, Neutral, Unsatisfied)
    • Description: Captures the overall satisfaction level of the customer, providing a subjective measure of their experience.

Use Cases:

1. Customer Segmentation:

   • Analyze and categorize customers based on demographics, spending habits, and satisfaction levels.

2. Satisfaction Analysis:

   • Investigate factors influencing customer satisfaction and identify areas for improvement.

3. Promotion Strategy:

   • Assess the impact of discounts on customer spending and tailor promotional strategies accordingly.

4. Retention Strategies:

   • Develop targeted retention strategies by understanding the time gap since the last purchase.

5. City-based Insights:

   • Explore regional variations in customer behavior to optimize marketing efforts based on location-specific trends.

Note: This dataset is synthetically generated for illustrative purposes, and any resemblance to real individuals or scenarios is coincidental.

This dataset contains raw, unprocessed data files pertaining to the management tool group focused on 'Customer Experience Management' (CEM) and 'Customer Relationship Management' (CRM), including related concepts like Customer Satisfaction Surveys and Measurement. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "customer relationship management" + "customer experience management" + "customer satisfaction" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Customer Relationship Management+Customer Experience Management+Customer Satisfaction Measurement+Customer Satisfaction Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("customer relationship management" OR "customer experience management" OR "customer satisfaction" OR "customer satisfaction measurement" OR CRM) AND ("management" OR "strategy" OR "approach" OR "system" OR "implementation" OR "evaluation") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Customer Satisfaction Surveys (1993); Customer Satisfaction (1996); Customer Satisfaction Measurement (1999, 2000); Customer Relationship Management (2002, 2006, 2008, 2010, 2012, 2017); CRM (2004, 2014); Customer Experience Management (2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 1994, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1996/784; 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Customer Satisfaction Surveys (1993); Customer Satisfaction (1996); Customer Satisfaction Measurement (1999, 2000); Customer Relationship Management (2002, 2006, 2008, 2010, 2012, 2017); CRM (2004, 2014); Customer Experience Management (2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 1994, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1996/784; 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

This linked dataset contains sample versions of a selection of consumer price inflation tables prepared following the GSS guidance on releasing statistics in spreadsheets.

Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.