- Background and Problem Statement
- Data Collection and Cleaning
- Exploratory Data Analysis
- Model
- Conclusions
Rent the Runway is an e-commerce platform that allows users to rent, subscribe, or buy designer apparel and accessories. It was founded by Jennifer Hyman and Jennifer Fleiss, who launched the company in November 2009.
The customer success department at Rent the Runway would like to be able to predict which customers are more likely to not be satisfied with their rental. Those customers then can be reached directly with proactive “Win Back” promotions to ensure repeat service and a chance for a positive rental experience to take place in the future.
Using historic data for 160,000 dress rentals, a classification model was built to determine whether a customer will be satisfied with the rental with 93% accuracy.
The website offers 7,980 different dresses for rent. After a completed rental, each customer has a chance to leave an overall satisfaction rating for the rental as well as their measurements and demographic information. I scraped the garment description for each dress as well as the ratings and customer data for all reviews.
The cleaned data set has about 160,000 observations.
Features describing the customer: Type_of_customer, Size of the garment customer rented, Size the customer usually wears, Height, Age, Bust Size, Body Type, Weight, Season the garment was rented, Reason the garment was rented, Overall fit of the garment
Features describing the dress: Retail Price of the garment, Rent price of the garment, Number of Reviews left for that Garment, Dress Style, Sleeves and Neckline
Our representative customer is:
36 years old
5’4’’ and 140 pounds
Size 10
Renting mostly for special occasions like dates, vacations and weddings.
Our representative garment is:
Sleeveless
Has a v-neckline
Has an hourglass shape
Target Variable: 1: Not Satisfied with Rental (Satisfaction Rating of 1,2,3 stars) 0: Satisfied with Rental (Satisfaction Rating 4,5 stars)
The model I used in this study is Catboost Classification. CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features.
Accuracy - 0.93
ROC AUC - 0.81
We built a model that classifies whether a customer will be happy with their dress or not with 93% accuracy.
It is recommended that those customers should be reached out to with a “Win Back” promotion even if they do not leave a negative review.
| Variable | Description | Values |
|---|---|---|
| Type of Customer | The customer is desginated as "Top Contributor" if they review items often | Top Contributor, unknown |
| Size | The size of the dress rented. | Sizes 0 to 24 |
| Rented_for | the type of event for which the garment was rented. | Vacation, Wedding, Everyday, unknown, Party, Date, Work, Formal Affair |
| Size_usually_worn | the size usually worn by the customer. | various sizes |
| Height | the reported height of the customer in inches. | various heights in inches |
| Age | the reported age of the customer in years. | various ages in years |
| Band_Size | The band size of the customer's Bust Size | Integer values betwen 28 and 48. |
| Cup_Size | The cup size of the customer's Bust Size | capital letter for Bust Size |
| Body_type | what body type the customer thinks they have | athletic, hourglass, pear, straightnarrow, petite, unknown, fullbust, apple |
| Weight | the reported weight of the customer in pounds. | various weights |
| Brand | the brand of the dress | various brands |
| Dress_Description | the specific garment name | various dress descriptions |
| Retail_price | the retail price of the dress | dollar amount |
| Rent_price | the rent price of the dress | dollar amount |
| Number_of_reviews | the number of reviews left for the particular dress | integer value |
| Sleeves | the type of sleeves on the garment has | sleeveless , long_sleeves, short_sleeves, three_quarter_sleeves, cap_sleeves, flutter_sleeves strapless, other |
| Neckline | the type of neckline on the garment | v_neckline, crew_neckline, high_neckline, square_neckline, off_shoulder, shirt_collar_neckline, boat_neckline, scoop_neckline, sweetheart, halter, straight_neckline, mock_neckline, cowl_neckline, turtleneck, other |
| Dress_style | the cut of the bodice of the dress | hourglass,sheath, shift, gown,sleeveless, maxi,wrap, blouson, full skirt, short sleeves, a-line, long sleeves,empire_waist, cap sleeves, strapless, shirtdress, other |
| Fabric | A column was created for each of 13 most commonly occuring fabrics | A number between 0 and 100 designating the amount of given fabric in blend |
| Rating | the number of stars out of five stars that the customer left rated the garment with. | out of five stars |