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.

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...

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.

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.

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

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

The dataset was collected from Kaggle. It includes various features related to customer demographics, purchasing behavior, and other relevant metrics.

Tapestry segment descriptions can be found here..http://www.esri.com/library/brochures/pdfs/tapestry-segmentation.pdf For more than 30 years, companies, agencies, and organizations have used segmentation to divide and group their consumer markets to more precisely target their best customers and prospects. This targeting method is superior to using “scattershot” methods that might attract these preferred groups. Segmentation explains customer diversity, simplifies marketing campaigns, describes lifestyle and lifestage, and incorporates a wide range of data. Segmentation systems operate on the theory that people with similar tastes, lifestyles, and behaviors seek others with the same tastes—“like seeks like.” These behaviors can be measured, predicted, and targeted. Esri’s Tapestry Segmentation system combines the “who” of lifestyle demography with the “where” of local neighborhood geography to create a model of various lifestyle classifications or segments of actual neighborhoods with addresses—distinct behavioral market segments. The tapestry segmentation is almost comical in the sense that it trys to describe such small details of individuals daily lives just by analyzing the data provided on your CENSUS form. These segements are not only ideal for marketing and targeting lifestyles within a geographic location, but they are fun to read. Take the time to find out which segment you live in!

Dataset Description: This dataset contains 1,000 anonymized records of individuals, capturing a mix of demographic, financial, health, and behavioral attributes. The data is structured to support analysis in areas such as market research, risk assessment, public health studies, and customer segmentation.

Key Attributes: Personal Information

Name: Full name of the individual (synthetic).

Age: Age in years (range: 18–80).

Gender: Binary classification (Male/Female).

Financial Metrics

Annual Income: Yearly earnings in USD (range: 20K–250K).

Credit Score: FICO-like score (range: 300–850).

Transaction Frequency: Monthly transactions (count).

Health Indicators

BMI: Body Mass Index (range: 15–45).

Blood Pressure (Systolic): mmHg (range: 90–180).

Geospatial Data

Latitude: Approximate location (32.0°–42.0° N).

Longitude: Approximate location (-120.0°–-75.0° W).

Behavioral Data

Monthly Data Usage: Internet consumption in GB.

Potential Use Cases: Market Research: Segment customers by income, location, or spending habits.

Health Analytics: Study correlations between age, BMI, and blood pressure.

Financial Modeling: Assess credit risk based on income and transaction behavior.

Geospatial Analysis: Map demographic trends across regions.

Data Quality Notes: Contains 3% missing values (randomly distributed).

Numeric values are rounded for readability (e.g., BMI to 1 decimal place).

Success.ai’s Audience Targeting Data API empowers your marketing, sales, and product teams with on-demand access to a vast dataset of over 700 million verified global profiles. By delivering rich demographic, firmographic, and behavioral insights, this API enables you to hone in on precisely the right audiences for your campaigns.

Whether you’re exploring new markets, optimizing ABM strategies, or refining personalization techniques, Success.ai’s data ensures your message reaches the most relevant prospects. Backed by our Best Price Guarantee, this solution is indispensable for maximizing engagement, conversion, and ROI in a competitive global environment.

Why Choose Success.ai’s Audience Targeting Data API?

1. Vast, Verified Global Coverage

   • Access a broad range of professional and consumer profiles spanning industries, regions, and roles.
   • Expand confidently into new markets and segments, supported by accurate, continuously updated data.

2. AI-Validated Accuracy

   • Depend on 99% accuracy through AI-driven validation processes, reducing wasted spend and improving campaign performance.
   • Trust that your targeting efforts are always based on the most current and reliable information available.

3. Continuous Data Refreshes

   • Receive real-time updates to ensure your contact lists remain relevant and reflective of evolving market conditions.
   • Swiftly adapt strategies to seasonal shifts, product launches, or changing buyer behaviors, maintaining long-term effectiveness.

4. Ethical and Compliant

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

Data Highlights:

• 700M+ Verified Global Profiles: Engage with diverse, high-quality audiences from any industry or market segment.
• Demographic, Firmographic, and Behavioral Insights: Tailor campaigns with nuanced targeting strategies.
• Real-Time Data: Dynamically adjust outreach as market conditions evolve, ensuring relevance and timeliness.
• Best Price Guarantee: Achieve premium results at highly competitive prices, optimizing your ROI.

Key Features of the Audience Targeting Data API:

1. Granular Segmentation and Query

   • Filter audiences by demographics, industry, location, job role, purchasing patterns, and more.
   • Zero in on precisely the profiles that match your ideal customer profile (ICP) criteria, driving conversion and efficiency.

2. Instant Data Enrichment

   • Enhance existing CRM or marketing automation systems with continuously updated audience data, removing manual data imports and guesswork.
   • Maintain pristine data hygiene, ensuring your team always works with actionable intelligence.

3. Seamless Integration and Flexibility

   • Effortlessly incorporate the API into your existing workflows, marketing tools, or analytics platforms.
   • Adjust parameters, queries, and segmentation strategies as your business objectives evolve or market conditions shift.

4. AI-Driven Validation and Reliability

   • Leverage AI-powered verification to confirm data accuracy, reducing bounce rates and improving engagement outcomes.
   • Confidently invest resources in campaigns backed by verified, real-time data.

Strategic Use Cases:

1. Highly Personalized Campaigns

   • Use demographic and behavioral insights to craft tailored messages and offers.
   • Improve engagement, open rates, and conversions by delivering content that resonates with targeted segments.

2. ABM Strategies and Market Expansion

   • Identify and target key accounts or emerging market opportunities with precision.
   • Develop ABM campaigns that focus on decision-makers and influencers, accelerating deal velocity.

3. Product Launches and Seasonal Promotions

   • Quickly adapt targeting parameters to match seasonal trends, promotional periods, or new product introductions.
   • Engage ideal audiences at the most opportune moments, ensuring campaign relevance and impact.

4. Enhanced Competitive Advantage

   • Monitor audience shifts and competitor moves, refining segmentation and messaging proactively.
   • Stay a step ahead by anticipating audience needs and adjusting campaigns for maximum market resonance.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-tier audience targeting data at competitive prices, ensuring unparalleled value and ROI.

2. Seamless Integration

   • Incorporate the API into your current marketing stacks, simplifying workflows and improving team productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, enhance targeting, and achieve exceptional campaign performance.

4. Customizable and Scalable Solutions

   • Tailor datasets and segmentation parameters to align perfectly with your evolving business needs, product lines, and strategic imperatives.

Additional...

Success.ai’s Consumer Sentiment Data offers businesses unparalleled insights into global audience attitudes, preferences, and emotional triggers. Sourced from continuous analysis of consumer behaviors, conversations, and feedback, this dataset includes psychographic profiles, interest data, and sentiment trends that help marketers, product teams, and strategists better understand their target customers. Whether you’re exploring a new market, refining your brand message, or enhancing product offerings, Success.ai ensures your consumer intelligence efforts are guided by timely, accurate, and context-rich data.

Why Choose Success.ai’s Consumer Sentiment Data?

1. Comprehensive Audience Insights

   • Access psychographic and interest-based profiles that reveal what motivates and influences your audience’s decisions.
   • Continuous updates ensure you stay aligned with shifting consumer sentiments, seasonal preferences, and emerging trends.

2. Global Reach Across Industries and Demographics

   • Includes insights from various markets, age groups, cultural backgrounds, and income levels.
   • Identify consumer attitudes in different regions, helping you tailor campaigns, products, and messaging to diverse audiences.

3. Continuously Updated Datasets

   • Real-time data analysis ensures that your consumer sentiment insights remain fresh, relevant, and actionable.
   • Adapt quickly to consumer feedback, market changes, and competitive pressures.

4. Ethical and Compliant

   • Adheres to global data privacy regulations, ensuring your usage of consumer sentiment data is both legal and respectful of personal boundaries.

Data Highlights:

• Psychographic Profiles: Understand lifestyle preferences, values, and interests that shape consumer choices.
• Sentiment Trends: Track evolving emotional responses to brands, products, and categories.
• Global Audience Insights: Evaluate consumer sentiments across multiple regions, languages, and cultural contexts.
• Continuous Updates: Receive current data that reflects the latest shifts in mood, opinion, and interest.

Key Features of the Dataset:

1. Granular Segmentation

   • Segment audiences by demographic, interest, buying behavior, and sentiment scores for targeted marketing efforts.
   • Focus on the attributes that matter most, from eco-conscious consumers to luxury shoppers or value seekers.

2. Contextual Sentiment Analysis

   • Go beyond basic positive/negative sentiment to understand nuanced emotional responses.
   • Identify triggers that inspire loyalty, dissatisfaction, trust, or skepticism.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data provide deeper insights into consumer lifestyles, brand perceptions, and product affinities.
   • Leverage advanced analytics to develop personalized campaigns and product strategies.

Strategic Use Cases:

1. Marketing and Campaign Optimization

   • Craft campaigns that resonate emotionally by understanding what drives consumer engagement.
   • Adjust messaging, timing, and channels to align with evolving sentiment trends and seasonal shifts in consumer mood.

2. Product Development and Innovation

   • Identify unmet consumer needs and preferences before launching new products.
   • Refine features, packaging, and pricing strategies based on real-time consumer responses.

3. Brand Management and Positioning

   • Monitor brand perceptions to detect early signs of brand fatigue, trust erosion, or negative publicity.
   • Strengthen brand loyalty by addressing concerns, highlighting strengths, and adapting to changing market contexts.

4. Competitive Analysis and Market Entry

   • Benchmark consumer sentiment towards competitors, industry leaders, and emerging disruptors.
   • Assess market readiness and optimize entry strategies for new regions or segments.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access high-quality, verified data at competitive prices, ensuring efficient allocation of your marketing and research budgets.

2. Seamless Integration

   • Integrate enriched sentiment data into your analytics, CRM, or marketing platforms via APIs or downloadable formats.
   • Simplify data management and accelerate decision-making processes.

3. Data Accuracy with AI Validation

   • Benefit from AI-driven validation for reliable insights into consumer attitudes, leading to more confident data-driven strategies.

4. Customizable and Scalable Solutions

   • Tailor datasets to focus on specific segments, regions, or interests, and scale as your business grows and evolves.

APIs for Enhanced Functionality:

1. Data Enrichment API

   • Enhance your existing consumer records with psychographic and sentiment insights, deepening your understanding of audience motivations.

2. Lead Generation API

   • Identify audience segments receptive to your messaging, streamlini...

This dataset provides a comprehensive collection of consumer behavior data that can be used for various market research and statistical analyses. It includes information on purchasing patterns, demographics, product preferences, customer satisfaction, and more, making it ideal for market segmentation, predictive modeling, and understanding customer decision-making processes.

The dataset is designed to help researchers, data scientists, and marketers gain insights into consumer purchasing behavior across a wide range of categories. By analyzing this dataset, users can identify key trends, segment customers, and make data-driven decisions to improve product offerings, marketing strategies, and customer engagement.

Key Features: Customer Demographics: Understand age, income, gender, and education level for better segmentation and targeted marketing. Purchase Behavior: Includes purchase amount, frequency, category, and channel preferences to assess spending patterns. Customer Loyalty: Features like brand loyalty, engagement with ads, and loyalty program membership provide insights into long-term customer retention. Product Feedback: Customer ratings and satisfaction levels allow for analysis of product quality and customer sentiment. Decision-Making: Time spent on product research, time to decision, and purchase intent reflect how customers make purchasing decisions. Influences on Purchase: Factors such as social media influence, discount sensitivity, and return rates are included to analyze how external factors affect purchasing behavior.

Columns Overview: Customer_ID: Unique identifier for each customer. Age: Customer's age (integer). Gender: Customer's gender (categorical: Male, Female, Non-binary, Other). Income_Level: Customer's income level (categorical: Low, Middle, High). Marital_Status: Customer's marital status (categorical: Single, Married, Divorced, Widowed). Education_Level: Highest level of education completed (categorical: High School, Bachelor's, Master's, Doctorate). Occupation: Customer's occupation (categorical: Various job titles). Location: Customer's location (city, region, or country). Purchase_Category: Category of purchased products (e.g., Electronics, Clothing, Groceries). Purchase_Amount: Amount spent during the purchase (decimal). Frequency_of_Purchase: Number of purchases made per month (integer). Purchase_Channel: The purchase method (categorical: Online, In-Store, Mixed). Brand_Loyalty: Loyalty to brands (1-5 scale). Product_Rating: Rating given by the customer to a purchased product (1-5 scale). Time_Spent_on_Product_Research: Time spent researching a product (integer, hours or minutes). Social_Media_Influence: Influence of social media on purchasing decision (categorical: High, Medium, Low, None). Discount_Sensitivity: Sensitivity to discounts (categorical: Very Sensitive, Somewhat Sensitive, Not Sensitive). Return_Rate: Percentage of products returned (decimal). Customer_Satisfaction: Overall satisfaction with the purchase (1-10 scale). Engagement_with_Ads: Engagement level with advertisements (categorical: High, Medium, Low, None). Device_Used_for_Shopping: Device used for shopping (categorical: Smartphone, Desktop, Tablet). Payment_Method: Method of payment used for the purchase (categorical: Credit Card, Debit Card, PayPal, Cash, Other). Time_of_Purchase: Timestamp of when the purchase was made (date/time). Discount_Used: Whether the customer used a discount (Boolean: True/False). Customer_Loyalty_Program_Member: Whether the customer is part of a loyalty program (Boolean: True/False). Purchase_Intent: The intent behind the purchase (categorical: Impulsive, Planned, Need-based, Wants-based). Shipping_Preference: Shipping preference (categorical: Standard, Express, No Preference). Payment_Frequency: Frequency of payment (categorical: One-time, Subscription, Installments). Time_to_Decision: Time taken from consideration to actual purchase (in days).

Use Cases: Market Segmentation: Segment customers based on demographics, preferences, and behavior. Predictive Analytics: Use data to predict customer spending habits, loyalty, and product preferences. Customer Profiling: Build detailed profiles of different consumer segments based on purchase behavior, social media influence, and decision-making patterns. Retail and E-commerce Insights: Analyze purchase channels, payment methods, and shipping preferences to optimize marketing and sales strategies.

Target Audience: Data scientists and analysts looking for consumer behavior data. Marketers interested in improving customer segmentation and targeting. Researchers are exploring factors influencing consumer decisions and preferences. Companies aiming to improve customer experience and increase sales through data-driven decisions.

This dataset is available in CSV format for easy integration into data analysis tools and platforms such as Python, R, and Excel.

E-Commerce Customer Segmentation Dataset This synthetic dataset contains information about 20 customers of an e-commerce platform, designed for customer segmentation and classification tasks.

Dataset Overview Each record represents a unique customer with demographic and behavioral features that help classify them into different customer segments.

Features: customer_id: Unique identifier for each customer

age: Age of the customer (years)

annual_income_k$: Annual income in thousands of dollars

spending_score: A score between 0 and 100 indicating customer spending habits (higher means more spending)

membership_years: Length of membership in years

segment: Customer segment label; possible values are:

Low (low-value customers)

Medium (medium-value customers)

High (high-value customers)

Potential Use Cases Customer segmentation

Targeted marketing campaigns

Customer lifetime value prediction

Behavioral analytics and profiling

Clustering and classification algorithm testing

Dataset Size 20 samples

6 columns

License This dataset is provided under the Apache 2.0 License.

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

This project performs customer segmentation using K-Means clustering on a marketing campaign dataset. The objective is to identify distinct groups of customers based on demographic and behavioral variables such as age, income, number of children, frequency of online and in-store purchases, and total spending.

Key steps included: 1. Data cleaning & preprocessing 2. Feature selection and normalization 3. Dimensionality reduction using PCA 4. Cluster identification with Elbow Method & Silhouette Score 5. Final clustering using KMeans 6. Interpretation of each cluster's characteristics 7. The result reveals 4 unique customer segments:

🟢 Keluarga Muda Hemat (Young Budget-Conscious Families): Mid-aged customers with lower income and spending, often browsing online but making fewer purchases. 🟣 Elite Loyal Shopper: High-income, high-spending loyal customers who actively purchase across all channels. 🔵 Orang Tua Aktif Digital (Digitally Active Older Parents): Older customers with teens at home, tech-savvy and active in online shopping. ⚪ Konsumen Tradisional Pasif (Passive Traditional Consumers): Older segment with low spending and less engagement in marketing channels.

🎯 This segmentation can help businesses design more targeted and personalized marketing strategies based on customer profiles.

🎓 Credit: Mata Kuliah: Big Data Kelompok Senjavana, Anggota Tim: - Hafiz Fadli Faylasuf - J0403221103 - Hasna Nabiilah Widiani - J0403221043 - Anggito Rangkuti Bagas Muzaqi - J0403221096 - Asa Yuaziva - J0403221003 Dosen Pengampu: Gema Parasti Midara S.Si., M.Kom.

The objectives of the Smallholder Household Survey in Tanzania were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Tanzania according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Unlock powerful B2B marketing with the AmeriList U.S. Business Database, your gateway to connecting with over 20 million public and private companies across the U.S. and Canada.

Whether your goal is lead generation, account-based marketing, email campaigns, sales outreach, or market analysis, this database gives you the depth, accuracy, and segmentation you need to reach key decision makers efficiently.

AmeriList is a proven leader in direct marketing and data services since 2002. We combine multiple data sources, rigorous verification processes, and ongoing hygiene services to deliver one of the most dependable B2B data assets in the market.

Key Features & Data Coverage:

1. Extensive Coverage & Business Universe
2. Access to over 20 million U.S. and Canadian business profiles (public and private)
3. Annual telephone verification ensures current, accurate contact information

4. Aggregated from multiple trusted sources: Yellow Pages, white pages, SEC filings, government records, trade publications, etc.

5. Rich Firmographic & Demographic Selects For precise targeting, you can filter and segment by:

6. SIC & NAICS codes (industry classification)

7. Business size: employee count, sales volume, year established

8. Executive names, titles, decision makers

9. Public vs private status, location, executive roles, and more

10. Data Quality & Hygiene Services Your success hinges on clean data. AmeriList offers:

11. List hygiene services including merge/purge, data suppression, deceased handling, DMA suppression, etc.

12. Address correction & postal accuracy via NCOA, LACS, DSF2, CASS, ZIP+4 processing

13. Data enhancement services to append missing emails, phone numbers, firmographics, and demographic data

14. Specialty & Vertical Lists: In addition to the main business database, you can access more than 65,000 specialty mailing lists (e.g. auto owners, executives on the go, brides-to-be, healthcare professionals, etc.).

15. Some niche examples: dentists, lawyers, real estate professionals, contractors, home-based businesses (SOHO), credit-seeking businesses, start-ups, and more.

16. SOHO (Home-based Businesses) database: reach entrepreneurs running their business from home with selective targeting on industry, revenue, email, etc.

17. Booming Start-Ups database: newly formed, rapidly growing businesses that may be highly responsive to service providers.

18. Credit-Seeking Businesses list: businesses actively seeking financing, great for loan, leasing, or financial service vendors.

19. Channel & Delivery Options:

20. Receive your data in flexible formats (electronic lists, print, mail house fulfillment)

21. Ready for postal, telemarketing, or email campaigns depending on your strategy

22. Turnaround and fulfillment options are competitive, with support from AmeriList’s list services team

Benefits & Use Cases:

✔ Boost Sales & Lead Generation: Use the database to identify potential customers in your target verticals, then build campaigns to reach them via email, direct mail, phone, or multi-channel strategies.

✔ Precision Targeting & Better ROI: Eliminate guesswork, segment by industry, revenue, business size, location, executive role, and more. Your marketing budgets go further with high-conversion prospects.

✔ Decision-Maker Access: Reach business owners, executives, and purchasing managers directly with accurate contact details that cut through gatekeepers

✔ Market Expansion & Competitive Intelligence: Find new markets or underserved geographies. Analyze competitive landscapes and business trends across industries.

✔ List Maintenance & Data Refresh: Ensure that your internal CRM or lead lists stay clean, up-to-date, and enriched, reducing bounce rates, undeliverables, and wasted outreach.

✔ Specialized Campaigns & Niche Targeting: Tap into industry-specific, interest-based, or buyer-behavior lists (e.g. credit-seeking businesses, start-ups, niche professionals) to tailor outreach campaigns.

Why Choose AmeriList:

• Quality-backed accuracy, every business record is phone-verified annually for freshness and deliverability.
• Wide multisource aggregation, combinations of public records, private data providers, trade publications, etc., offering superior coverage and depth.
• Comprehensive hygiene and data enrichment included options that many providers sell à la carte.
• Large specialty and vertical list portfolio, 65,000+ specialty lists to pair or layer with your base business data.
• Longstanding reputation, trusted by enterprises, agencies, and direct marketers since 2002.
• Flexible delivery and support, choose formats and fulfillment options that match your marketing infrastructure.

The AmeriList U.S. Business Database is the ultimate resource for marketers, sales teams, and agencies looking to connect with verified companies and decision makers across every industry. With over 20 million U.S. businesses, rich firmographics, executive contacts, and advanced segmentation options, this B2B database ...

Overview This collection of datasets is designed to provide a comprehensive overview of a retail business's operations, focusing on calendar information, customer demographics, order details, and product information. These datasets are ideal for performing in-depth sales analysis, customer segmentation, demand forecasting, and inventory management.

Dataset Descriptions Calendar.csv

Description: This file contains detailed calendar information to assist with time-based analysis. It includes important dates, such as holidays, weekends, and fiscal periods, which can be critical for analyzing sales trends, seasonality, and promotional impacts. Key Columns: Date: The specific date. Day of Week: The day of the week (e.g., Monday, Tuesday). Month: The month corresponding to the date. Quarter: The fiscal quarter (Q1, Q2, etc.). Year: The year of the date. Holiday Flag: Indicates if the date is a public holiday. Customer.csv

Description: This dataset contains demographic information about the customers. It’s useful for customer segmentation, lifetime value analysis, and targeted marketing campaigns. Key Columns: Customer ID: A unique identifier for each customer. Name: The full name of the customer. Age: The age of the customer. Gender: The gender of the customer. Location: The geographic location (city/state) of the customer. Loyalty Tier: The loyalty program tier of the customer (e.g., Bronze, Silver, Gold). Order.csv

Description: This dataset tracks individual customer orders, including transaction details. It is essential for sales analysis, order fulfillment tracking, and revenue analysis. Key Columns: Order ID: A unique identifier for each order. Customer ID: The ID of the customer who placed the order (linking to Customer.csv). Order Date: The date the order was placed. Product ID: The ID of the product ordered (linking to Product.csv). Quantity: The quantity of the product ordered. Total Price: The total price of the order. Product.csv

Description: This dataset provides detailed information on the products available in the retail store. It includes categories, pricing, and supplier information, making it useful for inventory management and product performance analysis. Key Columns: Product ID: A unique identifier for each product. Product Name: The name of the product. Category: The category under which the product falls (e.g., Electronics, Clothing). Supplier ID: The ID of the supplier providing the product. Unit Price: The price per unit of the product. Stock Quantity: The number of units available in stock. Usability These datasets can be utilized for various business analytics tasks, including:

Sales and Revenue Analysis: By linking the Order.csv and Product.csv, one can analyze sales performance by product category, identify best-sellers, and determine revenue drivers. Customer Segmentation: Using Customer.csv, segment customers based on demographics or purchase behavior to tailor marketing efforts. Demand Forecasting: Integrate Calendar.csv to model seasonality effects and predict future sales trends. Provenance These datasets are typically generated from an ERP system or CRM and are structured to support a variety of business intelligence applications. Users may need to perform data cleaning or transformation depending on the specific use case.

Licensing and Coverage The datasets are provided without a specific license. Users are encouraged to verify and attribute the source as needed. Coverage typically includes the entire operational history of the retail business, though users should check for any specific time range covered.

The objectives of the Smallholder Household Survey in Uganda were to: • Generate a clear picture of the smallholder sector at the national level, including household demographics, agricultural profile, and poverty status and market relationships; • Segment smallholder households in Uganda according to the most compelling variables that emerge; • Characterize the demand for financial services in each segment, focusing on customer needs, attitudes and perceptions related to both agricultural and financial services; and, • Detail how the financial needs of each segment are currently met, with both informal and formal services, and where there may be promising opportunities to add value.

Third-Party Data Enrichment for Insurance Market Outlook

According to our latest research, the global market size for Third-Party Data Enrichment for Insurance reached USD 2.13 billion in 2024, driven by the accelerated adoption of advanced analytics and digital transformation across the insurance sector. The market is set to expand at a robust CAGR of 14.6% from 2025 to 2033, with forecasts indicating the market will reach USD 6.15 billion by 2033. This remarkable growth is primarily attributed to the increasing demand for improved risk assessment, fraud detection, and personalized customer experiences within the insurance industry.

The growth of the Third-Party Data Enrichment for Insurance market is being fueled by the insurance sector’s urgent need for enhanced data-driven decision-making. Traditional insurance models are being disrupted by digital-first competitors and evolving customer expectations, compelling insurers to leverage third-party data to gain deeper insights into policyholders and prospects. This data enrichment enables insurers to augment internal datasets with demographic, behavioral, and technographic information, thereby facilitating more accurate risk profiling and underwriting. Furthermore, as regulatory bodies increase scrutiny over data accuracy and transparency, insurers are investing heavily in robust data enrichment solutions to ensure compliance and mitigate reputational risks.

Another key driver is the surge in fraudulent activities and sophisticated cyber threats targeting the insurance industry. As fraudsters employ increasingly advanced techniques, insurers are turning to third-party data enrichment solutions to bolster their fraud detection capabilities. By integrating external datasets—such as credit histories, social media activity, and device fingerprints—insurers can identify anomalies and suspicious behaviors more effectively. The growing adoption of artificial intelligence and machine learning in fraud detection workflows further amplifies the value proposition of data enrichment, enabling real-time analysis and proactive risk mitigation. This trend is expected to intensify as insurers continue to digitize their operations and expand their digital touchpoints.

The proliferation of digital channels and the shift toward customer-centric business models are also accelerating market growth. Insurers are increasingly focused on delivering personalized products and services, which requires a granular understanding of customer needs, preferences, and behaviors. Third-party data enrichment empowers insurers to build comprehensive customer profiles, segment audiences with precision, and tailor offerings accordingly. Additionally, the integration of enriched data into claims management processes streamlines workflows, reduces processing times, and enhances customer satisfaction. As digital adoption accelerates across emerging markets, the demand for scalable and flexible data enrichment solutions is expected to rise exponentially.

Regionally, North America dominates the Third-Party Data Enrichment for Insurance market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The presence of major insurance companies, advanced IT infrastructure, and stringent regulatory frameworks in North America are key factors driving adoption. Meanwhile, Asia Pacific is witnessing the fastest growth, propelled by rapid digitalization, increasing insurance penetration, and the emergence of insurtech startups. Europe’s market is characterized by a strong focus on data privacy and compliance, which is shaping the adoption of secure and compliant data enrichment solutions. Latin America and the Middle East & Africa are gradually catching up, with insurers in these regions increasingly recognizing the value of third-party data in enhancing operational efficiency and customer engagement.

Component Analysis

The Component segment of the Third-Party Data Enrichment for Insurance market is bifurcated into Solutions and Services. Solutions encompass a range of software platforms and analytical tools designed to aggregate, cleanse, and integrate data from various external sources. These solutions are essential for insurers aiming to automate data enrichment processes, improve data quality, and derive actionable insights. The growing complexity of insurance products, coupled with the need for rea