Stitch Fix is a personal styling service that uses data from customer surveys and feedback to match them with clothing items from over 1000 brands. Customers fill out an extensive survey about their style preferences and are sent curated boxes of 5-12 clothing items selected by a stylist. Customers keep what they like and return the rest. The company uses algorithms and data scientists to analyze customer information and determine their "latent style" to better predict what they will like. With over 3 million active customers, 17% year-over-year growth, and a $3 billion valuation, Stitch Fix has been successful by using data science to personalize the shopping experience.

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Ken Leaver
September 2019
Analysis by Ken Leaver
September 2019

2. 2
About the company
How it works
Their Strategy
My Thoughts

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The model
• Mail order, personal styling service using an expert stylist and lots of data from algorithms
- Customers asked to first fill in an extensive survey
- Customers receive parcels with items picked for them at intervals they choose and keep only
what they want
- cost of stylist is taken from the clothes they purchase
• Why customers love it?
- less about the clothes and more about the ‘look’ (ie. how multiple items come together)
- suits customers’ tastes and price range
- range of popular brands (1000+)
- they can try things out in the comfort of their home
• The technology that drives it
- AI algorithms to use answers from quizzes and feedback on previous orders to determine what
customer will most like.
- Try to help buyers predict what will be in style months in the future
- Used to determine how a piece of clothing goes with others
- Algorithms also pair stylists up with customers
- Algorithms decode the ‘latent style’ ie. the type of clothing a client likes regardless of how they self-
label their style and their ‘latent size’

4. 4
The figures
• Based in: USA
• Began: 2011
• Value of company: $3billion+ (public
in 2017)
• Revenues:
- 2019 run rate of $1.6bn
- 7 consecutive quarters of
20%+ growth
• Net income: $7m in Q2 2019
• Active subscribers: 3.1m people
(+17% y-y)
- 90% of customers are
repeat buyers
• Brands: 1000+
• Inventory of items: millions
• warehouses: 6
• Employees: 5000+
- Stylists: 3900+ in the US
- 100 data scientists

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About the company
How it works
Their Strategy
My Thoughts

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How it works
1. Customer fills out lengthy survey about themselves and their tastes in fashion
2. They choose a frequency of receiving a delivery of items
3. They receive 5-12 items in the mail that the service deems meets their tastes
- These are selected by a professional stylist
- stylists create an ‘outfit card’ around each item that goes into a client’s box, which shows how
that piece could be styled into a complete outfit
- these outfit cards that are hand-curated by stylists are then scaled into a database of many
outfits that show the versatility of that item
4. They try them out and keep what they like
- pay only for what they like
- styling fee of $20-40 redeemed from what they purchase
5. Give feedback
- 85% of customers give feedback on whether they like what was sent to them

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New customer flow (1 of 7)
1. Choose ‘get started’ 2.Choose gender

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New customer flow (2 of 7)
3. Answer some questions that describe you 4. Provide data about your size

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5. Answer questions about how you like things to fit
New customer flow (3 of 7)
6. Answer questions about how often you
purchase and wear certain types of clothing

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7. Answer questions about the brands you like
New customer flow (4 of 7)
8. Answer questions about the colors you like

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9. Choose which outfits you like / don’t like
that they have generated for you
New customer flow (5 of 7)

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10. Choose how often you want deliveries
New customer flow (6 of 7)
11. Book first delivery date and pay

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11. Provide feedback on whether you liked
what they sent you
New customer flow (7 of 7)

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About the company
How it works
Their Strategy
My Thoughts

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Their focus is on using data for everything
• Company has a culture of trying to use
data for everything
• Strong belief that personalization is the
key to the future of fashion
• 100+ data scientists

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They create an ecosystem
• Brands love working with them as they share some of their
data with them
- has helped them sign more than 1000 brands
• Collaborations (affiliate marketplace)
- Rebecca Minkoff collection - capsule collection
timed to her NY Fashion Week show
◦ consists of 8 styles that leveraged Stitch Fix’s
expertise in data science

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Provide insight value-added content on styles
• Looks like it is geared
towards the average person
• Information seemed truly
relevant and useful

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I loved their “Tinder for fashion’ idea..
• “The Style Shuffle Game” is kind of like
Tinder for clothes…
- customer can vote yes or no on
complete outfits
• creates product ratings to fuel algorithms
that pair customers with items that fit their
tastes

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Style Pass seems like a good way of getting consumers active
and loyal
• Started in 2018 and only available to
select clients
• Pay $49 for unlimited number of
deliveries (‘fixes’) for a year
- can put this amount towards a
purchase

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The ‘box’ competitors
• Trunk club (Nordstrom) - subscription service
• Outfittery - EU online shopping club for men
• Zalon - Zalando’s subscription app service
• Lookiero - online shopping service in France and Spain
for women
• Thread - EU free online personal styling service
• Viume - Spanish startup

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Acquisition & Expansion
• Geographic Expansion:
- US (2011), UK (2019)
• Acquisitions in 2019 of Finery, a garment-tracking platform
- uses customer e-receipts from clothing purchases to
auto-populate a personal virtual wardrobe accessible
via an app
- assembles outfits
Finery

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About the company
How it works
Their Strategy
My Thoughts

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I love their use of data as i agree that it is absolutely
key to adding efficiency to the completely inefficient old
fashion model.
Data for matching stylists to customers, data for
figuring out what users like, data for matching outfits,
data data data
I like that they seem to develop deep open
relationships with brands.
Their willingness to share data and not try to heavily
discount seems to open the door to most popular
brands and provide the foundation to a win-win
relationship.

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I like that they try to make fashion ‘fun’, and
leverage that to add to their data chest..
Ideas like the “Style Shuffle” game where users can
just pass time saying whether they like outfits is a lot
like a game and lots of people probably love it.
They seem to take a very long-term approach to
their customers.
Their policies, like flexible returns, all seem geared
around getting customers that stay. Which means a
long customer lifespan, which helps pay for higher and
higher customer acquisition costs. Allowing them to
pay top dollar on the marketing channels and outbid
many competitors.

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In a world where the human figure is getting more and more diverse, models like
Stitch Fix will thrive.
As a person who has travelled the world and has seen a consistent pattern of how the
human body is changing in various countries, i can say confidently that the people of the
world are, and will continue to evolve, in a similar way as Americans, ie. they will get more
obese, have strange sizes, etc. And fashion companies that can make wiser size
decisioning will ride this wave. As it will become less and less frequent that your average
customer can just pick something off the shelf that fits.
I think there is a major opportunity for them to get
more into the design/production side.
It’s only natural for them to eventually want to start
using this horde of data they are sitting on to create
their own outfits and clothing. Perhaps as white labels
or as collaborations like the one they have with
Rebecca Minkoff.