Temple Data Analytics QVC Challenge Team Jeff Diana

Temple Data Analytics QVC Challenge Team Jeff. Diana Emilie Doyle, Claire Durand, Rachel King, Miles Vendetti-Houser, Vaughn Wiernicki

Tools ● Git. Hub ● Python ○ Pandas Library ● Microsoft Excel ● Piktochart

QVC Challenge What are the products and product categories that sell best in the US market? How effective are QVC’s campaigns in driving product sales? How effective are QVC’s on-air broadcast and on-air personalities in driving product sales? What is the impact of QVC’s social networking presence on driving product sales?

Our Perspective Marketing perspective Perspective employee/host Sales and campaigns Social media

Campaign Spending by Category Python Started with various expenditures on various time-stamped campaigns Read in each row of data as dictionaries Sorted and summed each row based on month and product category Wrote resulting sums by month, then by category

Product Category Sales per Month Python Cross referenced product and order master lists Sorted the product master list by product number Matched every ordered product to product number using a binary search O(n log n) runtime

Top Selling Product : Lock & Lock 10 pc. Nesting Tupperware January Sales Top Category : Home Decor

Finding the Top Selling Products Excel Made a histogram of orders by product number Parsed only necessary information to a separate sheet for python/piktochart Python Replicated our results from excel implementing a frequency count Piktochart Used parsed excel sheet to

Finding the Effectiveness of Social Media Excel Found frequency of each sentiment Python Parsed through and sorted by form of social media Piktochart Created pie charts based on overall sentiments as well as the sorted mediums

Hosts That Sold the Top Three Selling Product: 1. Rick Domeier 2. Amy Stran 3. Carolyn Gracie

Analysis - Git. Hub Pros Keeps everything documented Makes restoring versions easier (backups) Octocat is adorable Cons Various branches could lead to merge conflicts due to different organization of code Compromising between different code organization styles

Analysis - Python - Pandas Pros Built-in functionality involving data analysis Pandas great for big data sets and CSV files Dynamic language Pandas are très cute Cons Had to become (more) familiar with Pandas are actually very aggressive creatures

Analysis - Excel Pros In some aspects, easier to use than to learn how to do it in Pandas Cons Had trouble dealing with the size of some data (Would freeze/crash when running processes on all rows)

Analysis - Piktochart Pros Excellent tool for handling visualization of data Variety for graphs as well as potential for customization Color theme created uniformity for infographic Cons Limited ability to manipulate charts

Thank you!