Assignment 

We will do analysis of the website http://www.StichFix.com

**What is Stitch Fix?**
"Stitch Fix is an online styling service that delivers a truly personalized shopping experience, just for you. Fill out your Style Profile and a personal stylist will hand pick pieces to fit your tastes, needs and budget-and mail them directly to your door. Each box contains five items of clothing, shoes and accessories for you to try on at home. Keep what you love, send the rest back in a prepaid USPS envelope. Shipping and returns are free-even for exchanges! "

Scenario Design 

Who are the target users?
* People who prefer to get well fitted and customized clothese
* People who value convenience and want quality clothese 
* Working People who might not have time to deal with personal stylists at Brick and Mortar stores

What are their key goals?
* The most important goal would be to look their best
* Anther goal would be to save time  
* These people also want to save money as personal stylists elsewhere would be expensive 

How can you help them accomplish their goals?
* The site stylist would be intuned with current fashion trends and would know what fashion trend would go well with which customer. 
* These people would understand the customer and deliver them thue fashion advise at very minimal cost.

Reverse Engineering - Algorithms 

* "First, the machines perform a variety of algorithms to produce rank-ordered lists of the inventory." 
* "A filtering step removes styles from consideration that the client has received in a previous shipment or that have attributes which the client has asked to avoid."
* "For each of the remaining styles, the machines then try to evaluate the relative likelihood that this particular client will love that particular style. This is a difficult problem, and we approach it in many different ways-only a few of which we'll discuss here. But in general, note that we tag each item multiple times with match scores from different algorithms, and then rank them."
* "In some ways, the problem is a classic collaborative filtering problem: given different clients' feedback on different styles, we must fill in the gaps in the (sparse) matrix to predict the result of sending a style to a client who has not yet received it. As such, we do use some standard collaborative filtering algorithms (e.g. those who have liked what you have liked have also liked ...). "
* "However, unlike most collaborative filtering problems, we have a lot of explicit data, both from clients' self-descriptions and from clothing attributes. This helps with the cold start problem and also allows for greater accuracy if we employ algorithms that consider this data."
* "One such approach is mixed-effects modeling, which is particularly useful because of the longitudinal nature of our problem: it lets us learn (and track) our clients' preferences over time, both individually and as a whole. "
* "And in addition to the many explicit features available, there are some particularly pertinent latent (unstated) features of both clients and styles that we can infer from other data (structured and/or unstructured) and use to improve our performance."

Additional recommendations about how to improve the site’s recommendation capabilities 

* I think the site is doing a great job of using algorithms like Collaborative Filtering for analysis and recommendation, but their selection of brands is very limited and since they are new, their customer selection is also limited. I feel the site can be more effective once they have bigger selection of customers and products.

References 

1. https://support.stitchfix.com/hc/en-us/articles/204222994-What-is-Stitch-Fix-How-Does-it-Work-FAQ
2. https://support.stitchfix.com/hc/en-us/articles/203317264-The-Stitch-Fix-difference
3. https://www.behance.net/gallery/7664825/SuitUp-Clothing-Recommendation-System
4. https://www.sciencedirect.com/science/article/pii/S0262407912628784
5. https://repository.library.northeastern.edu/files/neu:rx914n60r/fulltext.pdf