Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

Dataset

This dataset was created by Ashkan Ranjbar

Contents

This Dataset contains information related to web marketing analytics. it contains information such as sessions, session duration, bounces, time on page, unique page that gives insight into web performance

Analysis of ‘Marketing Series: Customer Lifetime Value’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/marketing-seris-customer-lifetime-value on 30 September 2021.

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

This automotive marketing dataset enables predicting lifetime value. Use the target variable “Customer Lifetime Value” in the training file dataset.

Acknowledgements

https://squarkai.com/download-free-machine-learning-sample-data-sets/#toggle-id-14

Inspiration

• Predicting lifetime value. Use the target variable “Customer Lifetime Value” in the training file dataset
• Customer Segmentation

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

Preprocessed Shopee marketing data from Shopee Code League - Marketing Analytics competition.

Preprocessing List

1. Encode categorical feature to integer.
2. Replace string in continuous feature to NaN
3. Perform left join between train/test data and user data
4. Remove unused columns

Source Code

https://www.kaggle.com/ilosvigil/scl-2020-8-preprocessing

License

https://www.kaggle.com/c/student-shopee-code-league-marketing-analytics/rules

Column description

https://www.kaggle.com/c/student-shopee-code-league-marketing-analytics/data

Analysis of ‘Digital Marketing | E-Commerce | Customer Behavior’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ermismbatuhan/digital-marketing-ecommerce-customer-behavior on 28 January 2022.

--- No further description of dataset provided by original source ---

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

Dataset

This dataset was created by Chandan Malla

Released under Data files © Original Authors

Contents

Analysis of ‘U.S. Supermarket Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sindraanthony9985/marketing-data-for-a-supermarket-in-united-states on 28 January 2022.

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

Supermarket XYZ has been operating since 2008 and business flourished until 2016. They have a large database but they do not use them to achieve better business solutions. Their annual revenues have declined 10% and it seems to stay that way every year.

These datasets are used to analyse a supermarket in United States for the purpose of increasing revenue.

1. 50_Supermarket_Branches.csv contains the information of 50 supermarket branches such as their spending on the advertisement, administration and promotion, states and profits.

2. Ads_CTR_Optimisation.csv is based on the Click-Through Rates (CTR) from 10000 users in 10 different advertisements.

3. Market_Basket_Optimisation.csv . This dataset contains 7500 sales transactions in a week.

4. Supermarket_CustomerMembers.csv . This dataset can be used for customer segmentation.

These datasets in 'U.S. Supermarket Data' are available and legal for everyone who needs it for any kind of analytics project.

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

Supermarket XYZ has been operating since 2008 and business flourished until 2016. They have a large database but they do not use them to achieve better business solutions. Their annual revenues have declined 10% and it seems to stay that way every year.

These datasets are used to analyse a supermarket in United States for the purpose of increasing revenue.

1. 50_Supermarket_Branches.csv contains the information of 50 supermarket branches such as their spending on the advertisement, administration and promotion, states and profits.

2. Ads_CTR_Optimisation.csv is based on the Click-Through Rates (CTR) from 10000 users in 10 different advertisements.

3. Market_Basket_Optimisation.csv . This dataset contains 7500 sales transactions in a week.

4. Supermarket_CustomerMembers.csv . This dataset can be used for customer segmentation.

These datasets in 'U.S. Supermarket Data' are available and legal for everyone who needs it for any kind of analytics project.

week_number - номер недели, в которую проводилась рекламная компания search_sessions - количество поисковых сессий с рекламой наушников search_purchases - количество покупок из поисковых систем ad_clicks - количество кликов из контекстуальной рекламы ad_purchases - количество покупок из контекстуальной рекламы social_views - количество просмотров рекламы в соцсетях social_clicks - количество кликов из рекламы в социальных сетях social_purchases - количество покупок из рекламы в социальных сетях audience_avg_age - средний возраст рекламной аудитории audience_male_percent - доля мужчин в рекламной аудитории ad_budget - бюджет на рекламную компанию category - категория наушников

Analysis of ‘Market Segmentation in Insurance Unsupervised’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jillanisofttech/market-segmentation-in-insurance-unsupervised on 28 January 2022.

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

WHAT IS MARKET SEGMENTATION?

In marketing, market segmentation is the process of dividing a broad consumer or business market, normally consisting of existing and potential customers, into subgroups of consumers based on some type of shared characteristics.

Objective :

This case requires developing a customer segmentation to give recommendations like saving plans, loans, wealth management, etc. on target customer groups.

Dataset

The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last 6 months. The file is at a customer level with 18 behavioral variables. Variables of Dataset Balance Balance Frequency Purchases One-off Purchases Installment Purchases Cash Advance Purchases Frequency One-off Purchases Frequency Purchases Installments Frequency Cash Advance Frequency Cash Advance TRX Purchases TRX Credit Limit Payments Minimum Payments PRC Full payment Tenure Cluster

The sample Dataset summarizes the usage behavior of about 9000 active credit cardholders during the last 6 months. The file is at a customer level with 18 behavioral variables.

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

Dataset

This dataset was created by Shokirjon Otamirzaev

Contents

Analysis of ‘Bakery Sales Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/bakery on 28 January 2022.

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

Context

We live in the era of e-commerce and digital marketing. We have even small scale businesses going online as the opportunities are endless. Since a huge chunk of the people who have access to internet is switching to online shopping, large retailers are actively searching for ways to increase their profit. Market Basket analysis is one such key techniques used by large retailers to to increase sales by understanding the customers' purchasing behavior & patterns. Market basket analysis examines collections of items to find relationships between items that go together within the business context.

Content

The dataset belongs to "The Bread Basket" a bakery located in Edinburgh. The dataset provide the transaction details of customers who ordered different items from this bakery online during the time period from 26-01-11 to 27-12-03. The dataset has 20507 entries, over 9000 transactions, and 4 columns.

Variables

• TransactionNo : unique identifier for every single transaction
• Items : items purchased
• DateTime : date and time stamp of the transactions
• Daypart : part of the day when a transaction is made (morning, afternoon, evening, night)
• DayType : classifies whether a transaction has been made in weekend or weekdays

Inspiration

The dataset is ideal for anyone looking to practice association rule mining and understand the business context of data mining for better understanding of the buying pattern of customers.

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

Analysis of ‘🗺️ Holiday_Package_Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/susant4learning/holiday-package-purchase-prediction on 13 February 2022.

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

Context

"Trips & Travel.Com" company wants to enable and establish a viable business model to expand the customer base. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information. The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being. However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. - Most important features that have an impact on Product taken: Designation, Passport, Tier City, Martial status, occupation - Customers with Designation as Executive should be the target customers for the company .Customers who have passport and are from tier 3 city and are single or unmarried, have large business such customers have higher chances of taking new package. - Customers monthly income in range of 15000- 25000, and age range 15-30, prefer 5 star properties also have higher chances of taking new package based on EDA.

Inspiration

We need to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Tasks to Solve :

To predict which customer is more likely to purchase the newly introduced travel package Which variables are most significant. Which segment of customers should be targeted more.

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

Analysis of ‘Credit Card Customer Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aryashah2k/credit-card-customer-data on 28 January 2022.

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

Context

A Customer Credit Card Information Dataset which can be used for Identifying Loyal Customers, Customer Segmentation, Targeted Marketing and other such use cases in the Marketing Industry.

A few tasks that can be performed using this dataset is as follows: - Perform Data-Cleaning,Preprocessing,Visualizing and Feature Engineering on the Dataset. - Implement Heirarchical Clustering, K-Means Clustering models. - Create RFM (Recency,Frequency,Monetary) Matrix to identify Loyal Customers.

Content

The Attributes Include: - Sl_No - Customer Key - AvgCreditLimit - TotalCreditCards - Totalvisitsbank - Totalvisitsonline - Totalcallsmade

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

Analysis of ‘Coffee shop sample data (11.1.3+)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ylchang/coffee-shop-sample-data-1113 on 28 January 2022.

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

Context

This sample data module contains representative retail data from a fictional coffee chain. The source data is contained in an uploaded file named April Sales.zip. Source: IBM.

We have created sample data for a fictional coffee shop chain with three locations in New York city. The chain has purchased IBM Cognos Analytics to identify factors that contribute to their success, and ultimately to make data-informed decisions.

Amber and Sandeep are the co-founders of the coffee chain. They uploaded their data in a series of spreadsheets and created a data module. From that data, they designed an operations dashboard and a marketing dashboard.

Inventory

Amber and Sandeep have created two dashboards and one data module that is based on nine spreadsheets:

• Coffee operations: This sample dashboard demonstrates operational data from a fictional coffee chain. Location: Team content > Samples > Dashboards.
• Coffee marketing: This sample dashboard demonstrates marketing data from a fictional coffee chain. Location: Team content > Samples > Dashboards.
• Coffee sales and marketing: This sample data module contains representative retail data from a fictional coffee chain. Location: Team content > Samples > Data.
• April Sales.zip: This sample data contains representative retail data from a fictional coffee chain. This ZIP file contains nine related CSV files. Location: Team content > Samples > Data > Source files > Retail.

Content

Data

The sample data module named Coffee sales and marketing can be found in Team content > Samples > Data. There are nine tables:

• Sales Receipts
• Pastry Inventory
• Sales Targets
• Customer
• Dates
• Product
• Sales Outlet
• Staff
• Generation

Acknowledgements

https://community.ibm.com/community/user/businessanalytics/blogs/steven-macko/2019/07/12/beanie-coffee-1113

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

Analysis of ‘Customer Clustering’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dev0914sharma/customer-clustering on 28 January 2022.

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

Customer Segmentation is the subdivision of a market into discrete customer groups that share similar characteristics. Customer Segmentation can be a powerful means to identify unsatisfied customer needs. Using the above data companies can then outperform the competition by developing uniquely appealing products and services. You are owing a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. You want to understand the customers like who are the target customers so that the sense can be given to marketing team and plan the strategy accordingly.

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

Dataset

This dataset was created by Max Duong

Contents

It contains the following files:

Dataset

This dataset was created by olaniyan ibukunoluwa

Released under CC0: Public Domain

Contents

Analysis of ‘Boat Sales’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/karthikbhandary2/boat-sales on 28 January 2022.

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

Context

You are working as a data analyst for a yacht and boat sales website. The marketing team is preparing a weekly newsletter for boat owners. The newsletter is designed to help sellers to get more views of their boat, as well as stay on top of market trends.

They would like me to take a look at the recent data and get some insights. The possible questions that we can ask ourselves is:

1. characteristics of the most viewed boat listings in the last 7 days
2. is it the most expensive boats that get the most views?
3. Are there common features among the most viewed boats?

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