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 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.