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

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.

Segments and demographic variables predicting Covid-19 protective behaviors.

Here's a step-by-step guide on how to approach user segmentation for FitTrackr:

Define your segmentation goals: Start by determining what you want to achieve with user segmentation. For example, you might want to identify the most engaged users, understand the demographics of your user base, or target specific user groups with personalized promotions.

Gather data: Collect relevant data about your app users. This can include demographic information (age, gender, location), app usage data (frequency of app usage, time spent on different features), user behavior (types of workouts, goals set, achievements unlocked), and any other relevant data points available to you.

Identify relevant segmentation variables: Based on the goals you defined, identify the key variables that will help you segment your user base effectively. For FitTrackr, potential variables could include age, gender, fitness goals (e.g., weight loss, muscle gain), workout preferences (e.g., cardio, strength training), and user engagement level.

Segment the user base: Use clustering techniques or segmentation algorithms to divide your user base into distinct segments based on the identified variables. You can employ methods such as k-means clustering, hierarchical clustering, or even machine learning algorithms like decision trees or random forests.

Analyze and profile each segment: Once the segmentation is done, analyze each segment to understand their characteristics, preferences, and needs. Create detailed user profiles for each segment, including demographic information, app usage patterns, fitness goals, and any other relevant attributes. This will help you tailor your marketing messages and app features to each segment's specific requirements.

Develop targeted strategies: Based on the insights gained from user profiles, develop targeted marketing strategies and app features for each segment. For example, if you have a segment of users who primarily focus on weight loss, you might create personalized workout plans or send them motivational content related to weight management.

Implement and evaluate: Implement the targeted strategies and monitor their effectiveness. Continuously evaluate and refine your segmentation approach based on user feedback, engagement metrics, and the achievement of your goals.

Overview The Mall Customers Dataset provides data on 200 individuals who visit a mall, including demographic information, annual income, and spending habits. This dataset is useful for exploratory data analysis, customer segmentation, and clustering tasks (e.g., K-means clustering).

Dataset Summary - Rows: 200 - Columns: 5 - No missing values

Columns Description - CustomerID: A unique identifier for each customer (integer). - Genre: The gender of the customer (Male/Female). - Age: The age of the customer (integer). - Annual Income (k$): Annual income of the customer in thousands of dollars (integer). - Spending Score (1-100): A score assigned by the mall based on customer behavior and spending patterns (integer).

Potential Use Cases - Customer Segmentation: Group customers based on their income and spending habits. - Behavioral Analysis: Explore how factors like gender, age, and income influence spending scores. - Clustering: Apply algorithms such as K-means to identify clusters of customers with similar characteristics. - Targeted Marketing Campaigns: Use the insights to create personalized promotions for different customer segments.

Exploratory Questions - What is the relationship between annual income and spending score? - Does gender or age influence spending behavior? - Which customers have high spending scores but low incomes, or vice versa?

Suggested Analysis Techniques - EDA: Visualize income distribution, age groups, and spending patterns. - Clustering Algorithms: Use K-means or hierarchical clustering for segmentation. - Correlation Analysis: Investigate correlations between age, income, and spending score.

Licensing & Citation - License: Open for public use, suitable for educational and research purposes. - Citation: If you use this dataset in your project or research, please reference this dataset appropriately.

This dataset provides a great starting point for hands-on learning in customer analytics, marketing strategy, and machine learning. Perfect for beginners and data enthusiasts looking to explore clustering or segmentation techniques!

Food festivals have been a growing tourism sector in recent years due to their contributions to a region’s economic, marketing, brand, and social growth. This study analyses the demand for the Bahrain food festival. The stated objectives were: i) To identify the motivational dimensions of the demand for the food festival, (ii) To determine the segments of the demand for the food festival, and (iii) To establish the relationship between the demand segments and socio-demographic aspects. The food festival investigated was the Bahrain Food Festival held in Bahrain, located on the east coast of the Persian Gulf. The sample consisted of 380 valid questionnaires and was taken using social networks from those attending the event. The statistical techniques used were factorial analysis and the K-means grouping method. The results show five motivational dimensions: Local food, Art, Entertainment, Socialization, and Escape and novelty. In addition, two segments were found; the first, Entertainment and novelties, is related to attendees who seek to enjoy the festive atmosphere and discover new restaurants. The second is Multiple motives, formed by attendees with several motivations simultaneously. This segment has the highest income and expenses, making it the most important group for developing plans and strategies. The results will contribute to the academic literature and the organizers of food festivals.

BackgroundSegmentation of heterogeneous patient populations into parsimonious and relatively homogenous groups with similar healthcare needs can facilitate healthcare resource planning and development of effective integrated healthcare interventions for each segment. We aimed to apply a data-driven, healthcare utilization-based clustering analysis to segment a regional health system patient population and validate its discriminative ability on 4-year longitudinal healthcare utilization and mortality data.MethodsWe extracted data from the Singapore Health Services Electronic Health Intelligence System, an electronic medical record database that included healthcare utilization (inpatient admissions, specialist outpatient clinic visits, emergency department visits, and primary care clinic visits), mortality, diseases, and demographics for all adult Singapore residents who resided in and had a healthcare encounter with our regional health system in 2012. Hierarchical clustering analysis (Ward’s linkage) and K-means cluster analysis using age and healthcare utilization data in 2012 were applied to segment the selected population. These segments were compared using their demographics (other than age) and morbidities in 2012, and longitudinal healthcare utilization and mortality from 2013–2016.ResultsAmong 146,999 subjects, five distinct patient segments “Young, healthy”; “Middle age, healthy”; “Stable, chronic disease”; “Complicated chronic disease” and “Frequent admitters” were identified. Healthcare utilization patterns in 2012, morbidity patterns and demographics differed significantly across all segments. The “Frequent admitters” segment had the smallest number of patients (1.79% of the population) but consumed 69% of inpatient admissions, 77% of specialist outpatient visits, 54% of emergency department visits, and 23% of primary care clinic visits in 2012. 11.5% and 31.2% of this segment has end stage renal failure and malignancy respectively. The validity of cluster-analysis derived segments is supported by discriminative ability for longitudinal healthcare utilization and mortality from 2013–2016. Incident rate ratios for healthcare utilization and Cox hazards ratio for mortality increased as patient segments increased in complexity. Patients in the “Frequent admitters” segment accounted for a disproportionate healthcare utilization and 8.16 times higher mortality rate.ConclusionOur data-driven clustering analysis on a general patient population in Singapore identified five patient segments with distinct longitudinal healthcare utilization patterns and mortality risk to provide an evidence-based segmentation of a regional health system’s healthcare needs.

Although the Chinese government has implemented a variety of measures, the gender wage gap in 21st century China has not decreased. A significant body of literature has studied this phenomenon using sector segmentation theory, but these studies have overlooked the importance of the collective economy beyond the public and private sectors. Moreover, they have lacked assessment of the gender wage gap across different wage groups, hindering an accurate estimation of the gender wage gap in China, and the formulation of appropriate recommendations. Utilizing micro-level data from 2004, 2008, and 2013, this paper examines trends in the gender wage gap within the public sector, private sector, and collective economy. Employing a selection bias correction based on the multinomial logit model, this study finds that the gender wage gap is smallest and most stable within the public sector. Furthermore, the private sector surpasses the collective economy in this period, becoming the sector with the largest gender wage gap. Meanwhile, a recentered influence function regression reveals a substantial gender wage gap among the low-wage population in all three sectors, as well as among the high-wage population in the private sector. Additionally, employing Brown wage decomposition, this study concludes that inter-sector, rather than intra-sector, differences account for the largest share of the gender wage gap, with gender discrimination in certain sectors identified as the primary cause. Finally, this paper provides policy recommendations aimed at addressing the gender wage gap among low-wage groups and within the private sector.

This dataset provides comprehensive information on global salmon populations, focusing on their decline in oceanic environments. It includes various data points collected over time to track and analyze trends in salmon populations. The key columns in this dataset are:

SERIES - Internal code for dataset indicating Domain, species, and Status Review data set year and when applicable, method.

NMFS_POPID - The unique numeric value for a population as determined by NMFS. This value will not change over time, even if the population name (NWR Population Name) does.

RECOVERY_DOMAIN - Discrete geographic areas for which comprehensive recovery plans are being developed: Puget Sound, Willamette/Lower Columbia, Interior Columbia (including the Mid-Columbia, Upper Columbia, and Snake River sub-domains), Oregon Coast, and Southern/Oregon Northern California Coast.

ESU - For populations listed under the federal ESA, this is the name of a defined Evolutionary Significant Unit (ESU) or Distinct Population Segment (DPS) as defined by NMFS Northwest Region or by USFWS.

MAJOR_POPULATION_GROUP - Major Population Group, as defined by the NWR. Groups of populations within an ESU/DPS that are more similar to each other than they are to other populations. They are based on similarities in genetic characteristics, demographic patterns and habitat types and on geographic structure.

POPULATION_NAME - Legal given name for a listed population within the ESU.

COMMON_POPULATION_NAME - Shortened population name

DISPLAY_ORDER - Geographically based display order within ESUs.

SPECIES - Salmon species name

RUN_TIMING - Run of fish, generally determined on the basis of the time of year at which adults enter fresh water to spawn. (Spring, Summer, Spring/Summer, Fall, Winter, early, or late)

STREAM_NAME - Name of the primary stream for the Population

YEAR - Calender year of return

NUMBER_OF_SPAWNERS - Estimated number of natural origin (parents spawned in the wild) spawners contributing to spawning in a particular year. Includes both adults and jacks of natural origin (except for SR fall chinook which typically does have jack returns)

FRACWILD - The fraction of the total spawners that are the progeny of naturally-spawning fish.

CATCH - Terminal fishery harvest

AGE_1_RETURNS - The fraction of fish who are defined as having an age of 1 that returned to spawn in a given year.

AGE_2_RETURNS - The fraction of fish who are defined as having an age of 2 that returned to spawn in a given year.

AGE_3_RETURNS - The fraction of fish who are defined as having an age of 3 that returned to spawn in a given year.

AGE_4_RETURNS - The fraction of fish who are defined as having an age of 4 that returned to spawn in a given year.

AGE_5_RETURNS - The fraction of fish who are defined as having an age of 5 that returned to spawn in a given year.

AGE_6_RETURNS - The fraction of fish who are defined as having an age of 6 that returned to spawn in a given year.

AGE_7_RETURNS - The fraction of fish who are defined as having an age of 7 that returned to spawn in a given year.

METHOD - Survey (spawning ground), Model (PIT tag data), or GSI (genetic stock inventory data), Ladder count (at dam)

CITATION - Data source citation

CONTRIBUTOR - Agency, Tribe or other entity responsible for these data that is the best contact for questions that may arise about this data record.

DOCUMENT_CITATION - Citation of the document this dataset archive informed.

CODE_LINK - Location of the code used to generate analysis for the document

You can access the dataset here

** Inputs related to Analysis for additional reference:** 1. Why do we need customer Segmentation? As every customer is unique and can be targeted in different ways. The Customer segmentation plays an important role in this case. The segmentation helps to understand profiles of customers and can be helpful in defining cross sell/upsell/activation/acquisition strategies. 2. What is RFM Segmentation? RFM Segmentation is an acronym of recency, frequency and monetary based segmentation. Recency is about when the last order of a customer. It means the number of days since a customer made the last purchase. If it’s a case for a website or an app, this could be interpreted as the last visit day or the last login time. Frequency is about the number of purchases in a given period. It could be 3 months, 6 months or 1 year. So we can understand this value as for how often or how many customers used the product of a company. The bigger the value is, the more engaged the customers are. Alternatively We can define, average duration between two transactions Monetary is the total amount of money a customer spent in that given period. Therefore big spenders will be differentiated with other customers such as MVP or VIP. 3. What is LTV and How to define it? In the current world, almost every retailer promotes its subscription and this is further used to understand the customer lifetime. Retailer can manage these customers in better manner if they know which customer is high life time value. Customer lifetime value (LTV) can also be defined as the monetary value of a customer relationship, based on the present value of the projected future cash flows from the customer relationship. Customer lifetime value is an important concept in that it encourages firms to shift their focus from quarterly profits to the long-term health of their customer relationships. Customer lifetime value is an important metric because it represents an upper limit on spending to acquire new customers. For this reason it is an important element in calculating payback of advertising spent in marketing mix modelling. 4. Why do need to predict Customer Lifetime Value? The LTV is an important building block in campaign design and marketing mix management. Although targeting models can help to identify the right customers to be targeted, LTV analysis can help to quantify the expected outcome of targeting in terms of revenues and profits. The LTV is also important because other major metrics and decision thresholds can be derived from it. For example, the LTV is naturally an upper limit on the spending to acquire a customer, and the sum of the LTVs for all of the customers of a brand, known as the customer equity, is a major metric forbusiness valuations. Similarly to many other problems of marketing analytics and algorithmic marketing, LTV modelling can be approached from descriptive, predictive, and prescriptive perspectives. 5. How Next Purchase Day helps to Retailers? Our objective is to analyse when our customer will purchase products in the future so for such customers we can build strategy and can come up with strategies and marketing campaigns accordingly. a. Group-1: Customers who will purchase in more than 60 days b. Group-2: Customers who will purchase in 30-60 days c. Group-3: Customers who will purchase in 0-30 days 6. What is Cohort Analysis? How it will be helpful? A cohort is a group of users who share a common characteristic that is identified in this report by an Analytics dimension. For example, all users with the same Acquisition Date belong to the same cohort. The Cohort Analysis report lets you isolate and analyze cohort behaviour. Cohort analysis in e-commerce means to monitor your customers’ behaviour based on common traits they share – the first product they bought, when they became customers, etc. - - to find patterns and tailor marketing activities for the group.

Transaction data has been provided for the period of 1st Jan 2019 to 31st Dec 2019. The below data sets have been provided. Online_Sales.csv: This file contains actual orders data (point of Sales data) at transaction level with below variables. CustomerID: Customer unique ID Transaction_ID: Transaction Unique ID Transaction_Date: Date of Transaction Product_SKU: SKU ID – Unique Id for product Product_Description: Product Description Product_Cateogry: Product Category Quantity: Number of items ordered Avg_Price: Price per one quantity Delivery_Charges: Charges for delivery Coupon_Status: Any discount coupon applied Customers_Data.csv: This file contains customer’s demographics. CustomerID: Customer Unique ID Gender: Gender of customer Location: Location of Customer Tenure_Months: Tenure in Months Discount_Coupon.csv: Discount coupons have been given for different categories in different months Month: Discount coupon applied in that month Product_Category: Product categor...