Art+Science Analytics Institute

Check out our latest video on our YouTube channel: https://bit.ly/4eYF5rI We're continuing our Spreadsheet Fundamentals series by teaching you how to master the Concatenate, Left, Mid, and Right functions in spreadsheets. These tools are essential for handling text data and will make your analysis more efficient and organized. Don't miss out on this new tutorial. What You’ll Learn: • Concatenate Function: Combine multiple text strings seamlessly. • Ampersand (&) Method: A quick alternative for text concatenation. • Left/Mid/Right Functions: Extract specific parts of text strings with ease. Why Watch This Video? • Improve Data Quality: Learn how to format and organize text data properly. • Boost Productivity: Automate repetitive tasks and streamline your data preparation. • Enhance Reporting: Make your reports more impactful with well-structured data. Watch the video, download the included step-by-step guide and practice dataset, and take your spreadsheet skills to the next level. Subscribe to our channel for more tutorials and tips on data analytics. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

#ThrowbackThursday, Data Visualization Style Today, we're revisiting a classic from the archives of data visualization history: William Playfair's groundbreaking chart from the late 18th century. In 1785, the Scottish engineer and political economist published a preliminary edition of his influential work, The Commercial and Political Atlas. This was formally released a year later and included a series of beautifully crafted charts that visually represented England's exports, imports, and overall trade. Playfair's brilliance went beyond just aesthetics. He invented both the line chart (also known as a time series plot) and the bar chart (as well as the pie chart, area graph, polar area diagram), revolutionizing how we present data. His line charts, which compared time to economic measures like exports and imports, set a new standard in chronographic forms and became some of the most recognizable visual tools for the next fifty years. The featured chart displays the balance of trade between England and Denmark & Norway from 1700 to 1780, highlighting the shifting balances between gains and losses for England. Playfair's ability to create such clear and impactful visuals is a testament to his skill and creativity. Besides being an engineer, draftsman, economist, and publicist at various points in his career, Playfair also had struggles with finances, but his contributions to data visualization remain unparalleled. His work paved the way for future generations of analysts and visual storytellers. Playfair's charts are not just relics of the past; they are timeless examples of how elegant design and innovative thinking can transform data into compelling narratives. Expect to see more of his pioneering work in upcoming #TBTDS posts. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

In case you were worried about how many hot dogs you ate over the July 4th holiday… Let’s take a sports analytics look at the legendary Nathan's Famous hot dog eating contest. In the “most gut-wrenching 10 minutes in sports”, the men's champion this year was Chicago’s Patrick "Deep Dish" Bertoletti, who devoured an impressive 58 hot dogs, while Miki Sudo became the new record holder for women with 51 hot dogs consumed. Sudo has been the top dog in the female competition since it started in 2011, securing her 10th win in 14 contests. On the men’s side, this was a change from previous years as Joey Chestnut was not allowed to compete due to his endorsement deal with plant-based food company Impossible Foods. Since 2007, Chestnut has dominated the competition and has only missed out on winning once. And even though he couldn't be at the official event, Chestnut still managed to devour an impressive 57 hot dogs at his own makeshift competition in Texas. Let this serves as a reminder that no matter how many dogs you piled on to your plate this weekend, you have plenty of work to do before reaching Championship Level. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Joining and parsing text is an essential skill for a data analyst. Whether you're organizing information or extracting specific data, here some valuable tips on utilizing the Concatenate and Left/Mid/Right functions. Why These Functions Matter - Efficiency: Transform raw data into a clean, well-organized format. - Accuracy: Reduce errors and increase clarity in your analysis. - Productivity: Automate repetitive tasks and streamline your data preparation process. Key Functions - Concatenate Function: Combine multiple text strings into one. Perfect for merging year, month, and day columns into a single date column. - Ampersand (&) Method: A quick alternative to concatenate text strings. - Left/Mid/Right Functions: Extract specific parts of a text string. Ideal for creating period values from date columns. Practical Applications - Date Formatting: Ensure consistency and standardization in your date columns. - Creating Periods: Simplify time-based data analysis with extracted year and month values. - Cleaning Data: Reformat and tidy text data for improved accuracy. - Custom Data Manipulation: Tailor text data to meet specific project requirements, like creating unique identifiers. Want to dive deeper? Our latest Art+Science newsletter goes into more detail, offering step-by-step guides and practice data to master these functions. Subscribe to our newsletter for in-depth tutorials and exclusive content. DM me if you'd like to get access to these materials or have any questions – I'm here to help you on your data analytics journey. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Excited about the launch of our YouTube channel! You'll find it here: https://lnkd.in/gP6bGSku

#ThrowbackThursday Dataviz Style Today’s installment features Florence Nightingale’s groundbreaking “Diagram of the Causes of Mortality in the Army of the East. In the midst of the Crimean War (1853-1856), Nightingale's emphasis on cleanliness greatly decreased the mortality rate at the Scutari hospital. She used her "rose" diagram to visually show that a majority of deaths were caused by preventable illnesses rather than combat injuries. The blue sections represented fatalities from disease, while red ones signified battle wounds and black indicated other causes of death. The powerful visualization not only emphasized the immediate necessity for improved sanitation, but also demonstrated the influence of data visualization in promoting change. The groundwork laid by Nightingale in the fields of nursing and public health has paved the way for modern advancements. Her influence serves as a reminder of how crucial accurate data and effective presentation can be. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #ThrowbackThursdayDataviz

You simply must know your consumer. Understanding their journey from prospect to loyal customer is a key for long-term business success. Understanding the complete customer journey is essential for minimizing churn and maximizing CLV. Based on my experience, McKinsey & Company’s Consumer Decision Journey (CDJ) framework provides the most coherent and practical method to grasp and impact customer choices at each phase, from initial consideration through to post-purchase. The CDJ framework is not simply a theory; it serves as a guide to improving marketing and business decisions. I’ve used it to identify new market opportunities, clarify business objectives, and isolate barriers to repeat purchase. By adopting the CDJ framework, companies can improve their strategic alignment and cultivate enduring customer relationships. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Cancel Culture has hit streaming services hard. Antenna’s latest data reveals a staggering 50.4M streaming service cancellations in Q1 2024. That’s an 80% surge compared to two years ago! But here’s the twist: Over half (56%) of new sign-ups are opting for lower-priced ad-supported tiers, according to Antenna. It seems the blend of affordability and traditional TV feel is striking a chord. The viewing options are limitless for consumers when it comes to streaming platforms. There are nine main choices, including Netflix, Apple TV +, and Disney+ Hotstar, along with an impressive 32 niche services like Hallmark Media Movies Now and Shudder for horror enthusiasts. Viewers can easily binge-watch their favorite shows or switch to something else whenever they please. And with ad-supported options, the cost to binge and bail has been reduced. Understanding Customer Lifetime Value is critical for building long-term profitable relationships. But unless subscription businesses can figure out the retention puzzle, they will look more and more like business models built on one-time fees. Is this the new normal for streaming? Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Let’s talk about Customer Lifetime Value (CLV). CLV is the net present value (NPV) of all variable profits and costs associated with a customer over their entire relationship with a company. It is the ultimate measure of a customer’s worth to a business and is, without doubt, one of My Favorite Analyses. Precisely determining CLV is essential when making strategic choices on how to attract and retain customers (as well as who to attract and retain). Companies can customize these strategies in ways that optimize profits when they understand the actual dollar value of each customer. The three main components of CLV are: • Historical Value (HV): NPV of all past profits. • Residual Lifetime Value (RLV): NPV of all future profits. • Post-Acquisition Value (PAV): Sums HV and RLV to show total value after acquisition. With these components in hand, CLV is calculated by subtracting the NPV of costs to acquire a customer (known as Customer Acquisition Costs, or CAC) from their PAV. Most of what I know about CLV comes from the work of Daniel McCarthy and Peter Fader, who have been making significant contributions in this field for quite some time. I am grateful to both of them, as their insights have greatly enhanced my ability to guide clients on the complexities of CLV. Subscribers to our Art+Science newsletter today received an R script to calculate CLV in a straightforward yet elegant way, using the BTYD and BTYDplus packages. BTYD was developed by Fader, Bruce Hardie, and Ka Lok Lee. The BTYDplus package, created by Moritz Steiner, Andreas Niekler, and Andreas Martin, builds on BTYD and extends some of its key capabilities. If you would like the R script and a practice dataset, subscribe to my newsletter using the link in the comments below and DM me. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling #MyFavoriteAnalyses #CLV