Demographical Shopping Purchases Data

Analyzing customer purchasing patterns and preferences

By Joseph Nowicki [source]

About this dataset

This dataset contains demographic information about customers who have made purchases in a store, including their name, IP address, region, age, items purchased, and total amount spent. Furthermore, this data can provide insights into customer shopping behaviour for the store in question - from their geographical information to the types of products they purchase. With detailed demographic data like this at hand it is possible to make strategic decisions regarding target customers as well as developing specific marketing campaigns or promotions tailored to meet their needs and interests. By gaining deeper understanding of customer habits through this dataset we unlock more possibilities for businesses seeking higher engagement levels with shoppers

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset includes information such as customer's names, IP address, age, items purchased and amount spent. This data can be used to uncover patterns in spending behavior of shoppers from different areas or regions across demographics like age group or gender.

Research Ideas

• Analyze customer shopping trends based on age and region to maximize targetted advertising.
• Analyze the correlation between customer spending habits based on store versus online behavior.
• Use IP addresses to track geographical trends in items purchased from a particular online store to identify new markets for targeted expansion

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: Demographic_Data_Orig.csv | Column name | Description | |:---------------|:------------------------------------------------------------------------------------------------| | full.name | The full name of the customer. (String) | | ip.address | The IP address of the customer. (String) | | region | The region of residence of the customer. (String) | | in.store | A boolean value indicating whether the customer made the purchase in-store or online. (Boolean) | | age | The age of the customer. (Integer) | | items | The number of items purchased by the customer. (Integer) | | amount | The total amount spent by the customer. (Float) |

Acknowledgements

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

Demografy is a privacy by design customer demographics prediction AI platform.

Core features: - Demographic segmentation - Demographic analytics - API integration - Data export

Key advantages: - 100% coverage of lists - Accuracy estimate before purchase - GDPR-compliance as no sensitive data is required. Demografy can work with only first names or masked last names

Use cases: - Actionable analytics about your customers to get demographic insights - Appending missing demographic data to your records for customer segmentation and targeted marketing campaigns - Enhanced personalization knowing you customer better

Unlike traditional solutions, you don’t need to know and disclose your customer or prospect addresses, emails or other sensitive information. You can provide even masked last names keeping personal data in-house. This makes Demografy privacy by design and enables you to get 100% coverage of your audience since all you need to know is names.

This dataset was sourced from KPMG AU's Data Analytics virtual internship course on Forage

Sprocket Pvt Ltd is a client of KPMG AU. Sprocket is a bike and bike accessories retail business. They need to find the right customer segment to target for marketing to boost revenue. The following dataset is of their customer demographics for the past 3 years.

The original dataset of 3 separate sheets of Customer demographic, Transactions, and Customer Addresses was fully cleaned and merged using a power query. Data types of columns were changed, and values of certain columns which had illegal values were corrected using a standard approach. This final master dataset can be used for customer segmentation projects using clustering methods.

During a survey carried out in November 2021 among marketers from ** countries worldwide, ** percent stated their organizations used past purchases to define target consumer segments. Consumer demographics, such as age, gender, income, or location, were used most often, named by ** percent of respondents.

Project Overview: Customer Segmentation Using K-Means Clustering

Introduction In this project, I analysed customer data from a retail store to identify distinct customer segments. The dataset includes key attributes such as age, city, and total sales of the customers. By leveraging K-Means clustering, an unsupervised machine learning technique, I aim to group customers based on their age and sales metrics. These insights will enable the creation of targeted marketing campaigns tailored to the specific needs and behaviours of each customer segment.

Objectives - Cluster Customers: Use K-Means clustering to group customers based on age and total sales. - Analyse Segments: Examine the characteristics of each customer segment. - Targeted Marketing: Develop strategies for personalized marketing campaigns targeting each identified customer group.

Data Description The dataset comprises:

• Age: The age of the customers.
• City: The city where the customers reside.
• Total Sales: The total sales generated by each customer.

Methodology - Data Preprocessing: Clean and preprocess the data to handle any missing or inconsistent entries. - Feature Selection: Focus on age and total sales as primary features for clustering. - K-Means Clustering: Apply the K-Means algorithm to identify distinct customer segments. - Cluster Analysis: Analyse the resulting clusters to understand the demographic and sales characteristics of each group. - Marketing Strategy Development: Create targeted marketing strategies for each customer segment to enhance engagement and sales.

Expected Outcomes - Customer Segments: Clear identification of customer groups based on age and purchasing behaviour. - Insights for Marketing: Detailed understanding of each segment to inform targeted marketing efforts. - Business Impact: Enhanced ability to tailor marketing campaigns, potentially leading to increased customer satisfaction and sales.

By clustering customers based on age and total sales, this project aims to provide actionable insights for personalized marketing, ultimately driving better customer engagement and higher sales for the retail store.

Success.ai’s Consumer Marketing Data API empowers your marketing, analytics, and product teams with on-demand access to a vast and continuously updated dataset of consumer insights. Covering detailed demographics, behavioral patterns, and purchasing histories, this API enables you to go beyond generic outreach and craft tailored campaigns that truly resonate with your target audiences.

With AI-validated accuracy and support for precise filtering, the Consumer Marketing Data API ensures you’re always equipped with the most relevant data. Backed by our Best Price Guarantee, this solution is essential for refining your strategies, improving conversion rates, and driving sustainable growth in today’s competitive consumer landscape.

Why Choose Success.ai’s Consumer Marketing Data API?

1. Tailored Consumer Insights for Precision Targeting

   • Access verified demographic, behavioral, and purchasing data to understand what consumers truly value.
   • AI-driven validation ensures 99% accuracy, minimizing wasted spend and improving engagement outcomes.

2. Comprehensive Global Reach

   • Includes consumer profiles from diverse regions and markets, enabling you to scale campaigns and discover emerging opportunities.
   • Adapt swiftly to new markets, product launches, and shifting consumer preferences with real-time data at your fingertips.

3. Continuously Updated and Real-Time Data

   • Receive ongoing updates that reflect evolving consumer behaviors, interests, and market trends.
   • Respond quickly to seasonal changes, competitor moves, and industry disruptions, ensuring your campaigns remain timely and relevant.

4. Ethical and Compliant

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

Data Highlights:

• Detailed Demographics: Age, gender, location, and income levels to refine targeting and messaging.
• Behavioral Insights: Interests, browsing patterns, and content consumption habits to anticipate consumer needs.
• Purchasing History: Understand consumer spending, brand loyalty, and product preferences to tailor promotions effectively.
• Real-Time Updates: Keep pace with evolving consumer tastes, ensuring your strategies remain forward-focused and competitive.

Key Features of the Consumer Marketing Data API:

1. Granular Targeting and Segmentation

   • Query the API to segment consumers by demographics, interests, past purchases, or engagement patterns.
   • Focus campaigns on the most receptive audiences, enhancing conversion rates and ROI.

2. Flexible and Seamless Integration

   • Easily integrate the API into CRM systems, marketing automation tools, or analytics platforms.
   • Streamline workflows and eliminate manual data imports, freeing resources for strategic initiatives.

3. Continuous Data Enrichment

   • Refresh consumer profiles with the latest data, ensuring every decision is backed by current insights.
   • Reduce data decay and maintain top-notch data hygiene to maximize long-term marketing effectiveness.

4. AI-Driven Validation

   • Rely on advanced AI validation techniques to guarantee high-quality data accuracy and reliability.
   • Increase confidence in your campaigns and decrease budget wasted on irrelevant targets.

Strategic Use Cases:

1. Highly Personalized Marketing Campaigns

   • Deliver tailored offers, recommendations, and content that align with individual consumer preferences.
   • Boost engagement and loyalty by making every touchpoint relevant and meaningful.

2. Market Expansion and Product Launches

   • Identify segments most receptive to new products or services, ensuring successful market entry.
   • Stay ahead of consumer demands, evolving your product line and marketing mix to meet changing preferences.

3. Competitive Analysis and Trend Forecasting

   • Leverage consumer insights to anticipate emerging trends and outpace competitors in capturing new markets.
   • Adjust marketing strategies proactively to capitalize on seasonal, cultural, or economic shifts.

4. Customer Retention and Loyalty Programs

   • Use historical purchase and engagement data to identify at-risk customers and implement retention strategies.
   • Cultivate brand advocates by delivering personalized offers and exclusive perks to loyal consumers.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality consumer marketing data at unmatched prices, ensuring maximum ROI for your outreach efforts.

2. Seamless Integration

   • Easily incorporate the API into existing workflows, eliminating data silos and manual data management.

3. Data Accuracy with AI Validation

   • Depend on 99% accuracy to guide data-driven decisions, refine targeting, and elevate your marketing initiatives.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific demog...

Column Descriptors:

customer_id: Unique identifier for each customer. customer_name: Name of the customer with potential metacharacters in 10% of names. product_category: Category of the purchased product (electronics, clothing, books, appliances). purchase_amount: Amount spent on the purchase. delivery_status: Status of the delivery (delivered, pending, shipped, cancelled). payment_status: Status of the payment (completed, pending, cancelled). customer_age: Age of the customer. customer_gender: Gender of the customer (Male, Female). product_rating: Rating of the purchased product (1 to 5). shipping_region: Region for shipping (North, South, East, West). loyalty_status: Loyalty status of the customer (Silver, Gold, Platinum). country: Country of the customer.

This statistic shows the retail sales market share of Target Corporation in the United States in 2012 and 2013. In 2013, Target held a market share of over *** percent in the United States.

According to a March 2022 survey of companies from selected countries that have already invested in the metaverse, most of the responding businesses saw big companies, men, and Gen Z as the target audience for their metaverse activities. In total, **** percent of respondents stated that men were a metaverse target audience, compared to only *** percent who stated the same about women. Additionally, big businesses were approximately * times more attractive than SMBs.

Context and Objective:

This dataset is developed as part of a business analysis project aimed at exploring sales performance and customer demographics. It is inspired by real-world scenarios where companies strive to enhance their marketing strategies by understanding consumer behavior. The project focuses on the year 2023 and provides insights into how targeted marketing impacts sales while emphasizing demographic characteristics such as age and gender.

Source:

The dataset is synthetically generated, designed to simulate real-world sales scenarios for 20 products. It includes data points that mirror industry practices, ensuring a realistic and comprehensive foundation for analysis. The structure and data content are informed by common business intelligence practices and hypothetical yet plausible marketing scenarios.

Inspiration:

This dataset is inspired by the challenges businesses face in balancing targeted and broad marketing strategies. Companies frequently debate whether niche marketing for specific demographics or campaigns targeting a wider audience yields better outcomes. The dataset serves as a sandbox for exploring these questions, combining data analytics, visualization, and storytelling to drive actionable business insights.

Key Features:

Sales Data: Includes monthly sales records for 20 products, categorized by revenue, units sold, and discounts applied.

Demographic Information: Covers customer age, gender, and location to enable segmentation and trend analysis.

Applications:

Business Insights: Explore product popularity trends across different demographic groups. Revenue Analysis: Understand revenue patterns throughout 2023 and their correlation with customer age and gender.

Marketing Strategy Optimization: Evaluate the effectiveness of targeted vs. broad campaigns, particularly those targeting specific gender or age groups.

Visualization and Storytelling: Build dashboards and presentations to communicate insights effectively. This dataset is ideal for analysts and students seeking hands-on experience in SQL, exploratory data analysis, and visualization tools like Power BI. It bridges the gap between data science and practical business decision-making.

The Silver Target market, which primarily focuses on the demographic of older adults aged 50 and above, represents a significant and growing segment of the consumer landscape. As the global population ages, this market has gained traction, driven by an increasing life expectancy and a rising number of baby boomers e

In marketing and selling products or services, it is essential to put in mind that different customers have different preferences, needs, and behaviors, and it's crucial to understand these differences to effectively reach and engage with them. One powerful way to do this is by segmenting customers by age. By doing so, you can tailor your marketing strategies to better resonate with each group and ultimately drive more sales and customer loyalty. This dataset is intended for analysis to identify the effects of different Age Group on revenue and profit

Acknowledgements

https://skillsforall.com/

As of March 2023, shoppers aged between 25 and 44 accounted for the majority of pet store revenue with a 37.2 percent share, thus making them the largest target market in the United States (U.S.). Those aged between 45 and 64 made up the second largest market by a very tight margin, providing 37.1 percent of pet store revenue in the same year. Pet stores in the U.S. There are 18,323 pet store establishments in the U.S. and California is the state with the largest number of pet stores, with 2,120 establishments. Florida closely follows, with 1,606 pet stores. The leading pet store company in the U.S. is the retail chain PetSmart Inc., with a market share of almost one-quarter. PetSmart Inc. and its main competitor, PETCO Animal Supplies, have a total market share of close to 40 percent. Pet stores in the U.S. generate revenue of almost 22 billion U.S. dollars annually. Online purchase of pet food and supplies in the U.S. The sales value of pet food in the U.S. amounts to almost 52 billion U.S. dollars. The store-based retailing channel generates close to 34 billion U.S. dollars of the total sales value, as compared to the e-commerce sale, with approximately 18 billion U.S. dollars. The website chewy.com is the leading online store in the pet supplies segment in the U.S. by a large margin. Chewy's generates over 11.1 billion U.S. dollars in net sales, offering various foods and supplies. However, for the online purchase of pet products in the U.S., the websites of Amazon and Walmart are the main destinations.

Mall Shoppers Customer Segmentation Dataset

Overview:

The Mall Shoppers Customer Segmentation Dataset is a rich collection of data designed to provide insights into the shopping behaviors and demographic profiles of customers visiting a mall. This dataset is pivotal for businesses aiming to tailor their marketing strategies, improve customer engagement, and enhance the shopping experience through targeted offers and services.

Content:

The dataset includes information on several hundred mall visitors, encompassing a variety of features such as:

• Customer ID: A unique identifier for each customer.
• Age: The age of the customer.
• Gender: The gender of the customer.
• Annual Income (k$): The annual income of the customer, expressed in thousands of dollars.
• Spending Score (1-100): A score assigned to the customer based on their spending behavior and purchasing data. A higher score indicates higher spending.

Purpose:

The primary purpose of this dataset is to enable the identification of distinct customer segments within the mall's clientele. By analyzing patterns in age, income, spending score, and gender, businesses can uncover valuable insights into customer preferences and behaviors. This, in turn, allows for the development of targeted marketing strategies, personalized shopping experiences, and improved product offerings to meet the diverse needs of each customer segment.

Applications:

This dataset is an excellent resource for: - Customer Segmentation: Utilizing clustering techniques to categorize customers into meaningful groups based on their features. - Targeted Marketing: Crafting personalized marketing campaigns aimed at specific customer segments to increase engagement and sales. - Market Analysis: Understanding the demographic makeup and spending habits of mall visitors to inform business decisions and strategies. - Personalization: Enhancing the customer experience through personalized services, recommendations, and offers.

Conclusion:

The Mall Shoppers Customer Segmentation Dataset offers a foundational step towards a deeper understanding of customer dynamics in a retail environment. It serves as a valuable asset for retailers, marketers, and business analysts seeking to leverage data-driven insights for strategic advantage.

Sourcing accurate and up-to-date demographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

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

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

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

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

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

This simulated customer dataset provides a practical foundation for performing segmentation analysis and identifying distinct customer groups. The dataset encompasses a blend of demographic and behavioral information, equipping users with the necessary data to develop targeted marketing strategies, personalize customer experiences, and ultimately drive sales growth.

Dataset Schema: Customer Demographics and Behavior

This dataset is structured to provide a comprehensive view of each customer, combining demographic information with detailed purchasing behavior. The columns included are:

• id: A unique identifier assigned to each customer.
• age: The customer's age in years.
• gender: The gender of the customer (e.g., Male, Female).
• income: The customer's annual income, denominated in USD.
• spending_score: A score ranging from 1 to 100 that reflects a customer's spending habits and loyalty.
• membership_years: The total number of years the customer has held a membership.
• purchase_frequency: The total number of purchases the customer has made in the last 12 months.
• preferred_category: The shopping category most frequently chosen by the customer (e.g., Electronics, Clothing, Groceries, Home & Garden, Sports).
• last_purchase_amount: The monetary value (in USD) of the customer's most recent transaction.

Potential Applications and Use Cases

The insights derived from this dataset can be applied to several key business areas:

• Customer Segmentation: Group customers into distinct segments by analyzing their demographic and behavioral data to better understand the composition of your customer base.
• Targeted Marketing: Craft and execute bespoke marketing campaigns tailored to the specific characteristics and preferences of each customer segment.
• Customer Loyalty Programs: Develop and implement loyalty initiatives that are designed to reward desirable spending behaviors and align with customer preferences.
• Sales Analysis: Examine sales data to identify purchasing patterns, understand trends, and forecast future sales performance.

The Resolution Test Target market is a specialized segment within the imaging and diagnostic industry, focusing on tools and methodologies that assess and optimize image resolution in various applications, including medical imaging, aerospace, and security systems. These test targets play a crucial role in ensuring

Snapshot img

The miso market size will grow up to USD 64.59 mn at a CAGR of 4% during 2021-2025.

This miso market analysis report entails exhaustive statistical qualitative and quantitative data on Product (white miso, yellow miso, and red miso) and Geography (APAC, North America, Europe, South America, and MEA) and their contribution to the target market. View our sample report to gather market insights on the segmentations. Furthermore, with the latest key findings on the post COVID-19 impact on the market, available in this report, you can create successful business strategies to generate new sales opportunities.

What will the Miso Market Size be in 2021?

Browse TOC and LoE with selected illustrations and example pages of Miso Market

Get Your FREE Sample Now!

Miso Market: Key Drivers and Trends

According to our research output, there has been a positive impact on the market growth post COVID-19 era. Key drivers such as the increasing soy production are notably supporting the miso market growth. On the other hand, factors such as product contamination have been identified as market challenges that limit the growth of market vendors. This report offers detailed insights on the challenges to stay prepared for the obstacles in the future, which will help companies analyze and develop growth strategies.

This post-pandemic miso market report has assessed the shift in consumer behavior and identified trends and drivers that will help market players outmaneuver challenges. Technology innovations, implementation, and improvisation scope identified in the miso market trends is essential for building new business opportunities across segmentations and geographies.

Who are the Major Miso Market Vendors?

The miso market forecast report provides insights on complete key vendor profiles and their business strategies to reimage themselves. The leading companies included in the report are as follows:

Eden Foods Inc. Great Eastern Sun HIKARI MISO CO. LTD. Ichibiki Co. Ltd. MARUSAN-AI CO. LTD. Miyako Oriental Foods Inc. Miyasaka USA Saikyo-Miso Co. Ltd. Urban Platter Yamato Soysauce & Miso Co. Ltd.

From our Porter’s five forces analysis study, get detailed insights on the functional involvement of the buyers and suppliers to form well-rounded knowledge about the supply chain and create cost reduction plans. The miso market analysis report also contains exhaustive observation on the organic and inorganic growth strategies deployed by the vendors. Click here to uncover details of successful business strategies adopted by the vendors.

Furthermore, our research experts have outlined the magnitude of the economic impact on each segment and recovery expectations post pandemic. To recover from post COVID-19 impact, market vendors should create strategies to grab business opportunities from the fast-growing segments, while refining their scope of growth in the slow-growing ones.

For insights on complete key vendor profiles, download a free sample of the miso market forecast report. The profiles include information on the production, sustainability, and prospects of the leading companies. The report's vendor landscape section also provides industry risk assessment in terms of labor cost, raw material price fluctuation, and other parameters, which is crucial for effective business planning.

Which are the Key Regions for Miso Market?

For more insights on the market share of various regions Request for a FREE sample now!

Japan, US, China, South Korea (Republic of Korea), and UK are the key markets for miso market in APAC. Learn about the key, emerging, and untapped markets from our miso market size, share, & trends analysis report for targeting your business efforts toward promising growth regions. 62% of the market’s growth will originate from APAC during the forecast period.

APAC has been recording significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. drivers.2 has been identified as one of the chief factors that will drive the miso market growth in APAC over the forecast period. To garner further competitive intelligence and regional opportunities in store for vendors, view our sample report.

What are the Revenue-generating Product Segments in the Miso Market?

To gain further insights on the market contribution of various segments Request for a FREE sample!

The miso market share growth by the _ segment has been significant. The miso market report provides comprehensive understanding of the subsegments of the target market to identify niche customer groups and demographic requirements. Furthermore, the report provides insights on the impact of COVID-19 on market segments, which can be used to deduce transformation patterns in consumer behavior in the coming years and improvise business plans.

Request for a free sample of the report to get an exclusive glimpse of actionable market insights on post