This document discusses using data science techniques to build a content recommendation engine and predict customer preferences for the clothing company Betabrand. It analyzes data on Betabrand product descriptions, Facebook likes of customers, and uses vectorization, clustering, and cross-tabulation to develop recommendations and identify common interests among customers of specific products. The results provided some useful recommendations but also opportunities for improving predictions through additional natural language processing and increasing sample sizes for clustering analysis.

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

2. What is ?

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

4. Every item has a unique “Story”

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

6. 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

7. Code: Remove Duplicates and Reset the Index

8. Use of Vectorizer and Cosine in NLP

9. Recommendations and Scores

10. 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.

11. Code: Top Facebook Like Categories for
Executive Ponte Top

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

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

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

15. K Means Analysis

16. Plot to find the best number of clusters and
identify labels

17. Identified 5 Clusters

18. 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

19. 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.