Customer and Product Dashboard

Posted on Oct 22, 2018

Background and Introduction

Customer data is becoming extremely appealing to companies in order to make more impactful decisions that will drive better business outcomes. In order for company executives to make better decisions based on information gathered from customers, the visualization of data must be outcomes-driven, straightforward, and easy to understand.

For the purposes of this exploratory activity, it is assumed that an online UK retailer’s leadership team is looking to explore areas of opportunity for their customer-focused efforts and product-focused efforts based on customer-driven e-commerce data gathered over the course of one year. The sample data set utilized can be found here.Β 

Shiny App

The Customer and Product Dashboard has three tabs:

  • Customer
  • Product
  • Data

The Customer tab summarizes key customer-focused highlights and visualizes the spread of customers across the world. The info boxes containing customer-focused highlights (i.e., total customers, average customer spend, average number of invoices per customer) provide background on the current customer base. The map shows where customers are located across the world, which could be used to inform customer expansion efforts (e.g., increase hiring for account managers based on location). In future efforts, customer-specific drill-downs including top products ordered, types of products ordered, and frequency of orders could be incorporated. This could influence customer-specific marketing and upselling strategies.

The Product tab summarizes key product-focused highlights and displays how many products are sold to companies within each country. Similarly to the Customer tab, the info boxes (i.e., total products, average product revenue, average number of products purchased per customer) provide background on products offered from the online retailer. The plot showing total products sold by country visualizes where most products are distributed to, which could potentially inform operational and logistical decisions (e.g., where to open a new warehouse location). To further develop this tab, products most frequently sold together could be presented. This could influence product bundling tactics.

The Data tab contains the complete raw dataset. On the Data tab, users can explore the complete dataset if desired.


While having a complete and informative dataset is important, it is even more crucial to visualize data in a way that is clear and intuitive to the target audience. The Customer and Product Dashboard clearly portrays data differently based on the types of decisions (i.e., customer and product) that will be influenced.

The Customer and Product Dashboard discussed in this blog post can be found here.

About Author

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup music Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp