[Provided Data - US] Hang Le
- 1. US E-Commerce DATA STORY-TELLING WITH QUICKSIGHTS PRESENTOR: Hang Le (Hangphuong.le@gmail.com)
- 2. CONTENTS Dataset Facts Analysis Process Business Overview Who are Customers? All about Orders Further Conclusion
- 3. DATASET FACTS Topic: E-Commerce in USA Timeline: from 13 Sep 2013 to 14 Jan 2014 break into weeks Dataset could be divided into Sales Measures Customers’ Profile Product Details Transaction Facts
- 4. Analysis Process BUSINESS OVERVIEW CUSTOMER PROFILE DEEP INSIDE PRODUCTS TRANSACTION CONTROL Products • Favorite Choice • Cross-Selling Customers • Demographics • Behaviors Transactions • Total Orders • Analysis
- 5. BUSINESS OVERVIEW HOW OUR MONEY WAS MADE? - Sales trend - Sales distribution - Location - Product Category
- 6. SALES TREND • Sales increased moderately from about $1.5M daily in the last week of Sep 2013 and surged during the end of November to the first two weeks of December, up to $6M in Dec 11-12. • Sales sharply decreased to under $1M per day in the last two week of Dec to the rest. It is expected that the business would be running marketing campaign for Xmas holiday, therefore customers make the most purchases during October and November. As people early started to shop for the holiday since the end of September, they limit their spending about 10 days before Xmas to prepare the party.
- 7. SALES VS GEOGRAPHY • Sales were from 3 cities : Seattle (Washington), New York City (New York), Los Angles (California). • The vast Sales were from Seattle (61%) following by Los Angles (34%). However, there was no data for Seattle after Dec 13, 2013. • It was the up trend in Sales for both Seattle and Los Angles from Sep 20,2013 to Dec 16,2013. • The spending was limited after Dec 16, 2013 in all cities • New York City might be the new market with the Sales under 0.3M per day.
- 8. SALES VS CATEGORY • People splurged on Fashion ($127.5M) and Clothing ($98M). • Even when the total spending dropped significantly, they still spent the majority on Clothing and Fashion. It might because shopping for Fashion and Clothing is easier via online platform while Electronics or Vehicle are needed to be more considered.
- 9. CUSTOMER ANALYSIS Q: WHO WERE OUR CUSTOMERS? - Gender - Location - Login Profile
- 10. CUSTOMER PROFILE - GENDER • Men were the majority in total number of customers (57%). They also dominated when customers were distributed geographically, especially in New York City, male took the major portion of 70%. Only in Los Angles, the share between male and female seemed to be equivalent. • It is not surprised that men contributed 57% towards the total sales with more than $153M while women only spent $115M. • The shares in gender also presented via total spending in each city.
- 11. CUSTOMER PROFILE - SALES VS GENDER • Women started shopping and spent most in the last half of October to the first week of November then decreased their spending still the end of November waiting for the men when began their spending towards the first 13 days of November. • Both decreased their spending after the week of December 8. Women seems to prepare for holiday earlier but men dominate the spending towards total Sales.
- 12. CUSTOMER PROFILE – LOGIN STATUS • The majority of customers was Member with the larger share was male. • We have only 30 New customers (29 F 1M) and more than 180 First SignUp (185 total with 80% F) • A number of Guests log into system everyday, these profiles also jumped the time rushed for Holiday from the end of November to about Dec 13.
- 13. LOOK INSIDE PRODUCTS - WHICH WERE THE TOP CHOICE? - Quantity vs revenues - Distributed by geography - Distributed by customers
- 14. TOP CHOICES OF PRODUCTS - Top 3 products towards the Total Sales were Fairness Cream($92M), Shirts ($64M) and Jeans ($29M). - All of 3 had the uptrend in sales during the time preparing from Xmas holiday and also dropped after Dec 13-14.
- 15. TOP CHOICES OF PRODUCTS - CITY - There was the differences in the preferable products across cities. - While Top 3 for Seattle was Fairness Cream, Shirts and Jean. The people in Los Angle most picked Shirt, Fairness Cream and Books (surprised!). New York City spent most on shirts, spectacles and Fairness Cream (again!) - People in 3 cities shared the preferences over Fairness Cream!
- 16. PRODUCT SALES DETAILS - Fairness Cream was known as the top choice products, here it stated that both Male and Female love the Fairness Cream. - Besides that, it is ridiculous that only Female bought Jeans while Shirts were all owned by Male. - Others to be noted, Male dominated the Electronics Sales and Books (huh?) while of course, women took all Shoes and Accessories. - Anyway, the data is quite not completed as most products are dominated by one gender.
- 17. TRANSACTION MANAGEMENT WHAT LEADED TO THE SUCCESSFUL TRANSACTIONS? - Started and Done - Timeline factors - Distributed by customers - Distributed by geography
- 18. GENDER- TIMELINE • The shopping transactions seem to be never stopped all over a day. - It had no major differences for Male and Female chosen their time for shopping. Again, in all time range, men took the vast on Sales. - However, while people started over 62K transactions, only 86.6% were processed successfully. This ratio is not dissimilar by gender that the business need to collect more information about the reasons behind these kind of transactions.
- 19. THE MOST BUSY TIME RANGE - The heat map presented the most busy time range with the most traffic days. - It confirmed that the most traffic days were around October and November – there, again, had no distinctions in time range during a day.
- 20. DELIVERY CHOICE • There was no gender effects on the choice of delivery type. • The time line affected the choice of delivery that around the end of September and early October, major people chose the normal delivery but after December 13, rushed for the holiday, there was the sharply drop in the transactions with normal delivery but the majority preferred the one-day delivery.
- 21. DELIVERY CHOICE (cont.) • The people in Seattle seemed to prefer Normal Delivery while other cities likely picked the one-day delivery. • When people using Web Platform, they preferred the Normal Delivery but the people surfing on Mobile picked more one-day delivery. • It is important to note that there was vast majority of transactions on Web Platform compared to Mobile one. This should be analyzed more carefully as Mobile is the important part of ecommerce.
- 22. MY CONCLUSIONS - The dataset is quite small that could lead to unappropriated insights about US E-ecommerce. - The total of about 100 days is quite short to forecast the near future of business. - We could see the changes in sales during the time before Xmas and some interest insights about preferable products of men and women. However, I doubt the accurateness of these insights. THANK YOU