Understanding Betabrand Using Data Science to Develop a Content Recommendation Engine and Predict Customer Preferences

What is ?

Retail Clothing and Crowd-Funding Platform Highly Social Unusual Clothing Items Interesting Customer Base Goofy Marketing Campaigns

Every item has a unique “Story”

Problem: Can We Make Recommendations for “Similar Items” based on Story Descriptions?

The Data Around 1600 Clothing Products and Story Descriptions for Each Product in excel from Betabrand Facebook Likes (along with “Category” of Likes) of Users Who Purchased Betabrand items Types of Analysis Content Recommendation Engine Cross tab in Pandas for raw counts K Means clustering analysis

Code: Remove Duplicates and Reset the Index

Use of Vectorizer and Cosine in NLP

Recommendations and Scores

Top Facebook Like Categories of Users Who Bought a Particular Product? Use of Cross Tab Use of Lift When Facebook Likes are too “generalized” across different products Results were interesting! They were actually informative with different results per product. Can see the how the Category of Facebook Like for a User who bought a product made sense based on the designer’s profile *FUTURE EXPLORATION: NLP analysis of designer profile and whether story description text of product can be correlated with Facebook Likes? It would be interesting to examine the linkage.

Code: Top Facebook Like Categories for Executive Ponte Top

Dataframe: Top Facebook Like Categories for Executive Ponte Top Science, Medical, Health School Shopping & Retail Education Society/Culture Professional Services Health/Wellness

Compare this to the Toaster! Aerospace/Defense Performance Venue Song, Concert, Record Label, Musical INstrument Food Computers/Internet Webiste Internet/Software

What About K-Means Clustering? Analyze Category of Facebook Likes to develop User Personas Map those Personas to Clothing Preferences

K Means Analysis

Plot to find the best number of clusters and identify labels

Identified 5 Clusters

Results K means had too small of a sample size to identify any meaningful trends in persona clustering Content Recommendation Engine delivered useful results once duplicates were removed. It might be helpful to do additional NLP analysis on designer profiles to remove items that are similar because of the designer from the analysis Cross Tab- simplest analysis of raw counts, but perhaps most informative

Impact and Future Directions Results of Content Recommendation Engine can be used for upsell opportunities in Betabrand – identifying products that are similar to suggest to users at the point of check out on the Betabrand website Pandas Crosstab can be used for better Facebook advertisement targeting and we can better market certain products, via email campaigns or other channels for certain customer segments K Means will need to be refined to identify meaningful user clusters for collaborative filtering. In combination with other methods, Betabrand could do some powerful targeting for key demographics and encourage designers to design for certain audiences.