DATA BLITZKRIEG: THE GEEK'S GUIDE TO MERCHANDISING, WAREHOUSING & OPERATING, Mike Smith, COO, Stitch Fix

1. COMMERCISM
March
21,
2014
The
Geek’s
Guide
to
Merchandising,
Warehousing
and
OperaAng

2. 1
“There is a tectonic shift going on in an
industry (retail) that represents a large chunk of
GDP and I’m not sure the industry knows how
deep or fast it’s going to be” – a recent entrepreneur
Most companies at this conference will FAIL to capitalize
on the tectonic shifts because they won’t truly differentiate
Introduc)on

3. 2
Agenda
Overview of Stitch Fix (very quick)
Tactics for success
Real life examples of using data with high impact
What I wish I knew?

4. 3
S)tch
Fix
brings
scalable
personaliza)on
to
the
mass
market

5. 4
Proprietary
tools
blend
the
science
of
data
with
the
art
of
styling
to
achieve
a
truly
personalized
customer
experience
• Leverage
structured
data
• Simultaneously
weight
many
features
• Leverage
the
unstructured
data
(e.g.
pinterest,
images,
video,
etc.)
• Foster
relaAonships
(notes,
explanaAon,
style
Aps,
etc.)
Styling
Algorithm
Stylists
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67891
32165
85236
42345
67891
32165
85236
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67891
32165
85236
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67891
32165
f (Ÿ)

6. 5
Agenda
Overview of Stitch Fix (very quick)
Tactics for success
Real life examples of using data with high impact
What I wish I knew?

7. 6
Four recommended tactics for success
• Invest in data science
• Recruit a data science leader that works well with functional leaders in
business
• Ensure that metrics coming from data science are true and meaningful
levers for driving business impact
• Develop an approach for HOW TO DO IT – the “It” being making data a
differentiator
1
2
3
4

8. 7
Tactic 1: Invest in data science
Investing in data science is not just investing in “analytical” people – it requires
understanding the art & science mix
Find the Eigen value Which image has a dog?
Impact of tactic: Higher AOV

9. 8
Four critical rules to follow when hiring a great data science leader…
• Candidates can’t operate in a box
• Should not have a big ego
• Should be a great communicator
• Data science should report directly to founder / CEO
Tactic 2: Recruit a great data science leader
Impact of tactic: data science becomes an extension of business
functions enabling greater accountability on P&L levers
1
2
3
4

10. 9
Too
many
data
points
DON’T
drive
impact
–
focus
on
those
that
do!
Tac)c
3:
Ensure
data
drives
real
business
impact
Impact of tactic: better contribution margin and LTV
How much inventory has been stolen by
employees?
How do we get the bottom five customer
service agents to go from 8 emails per
hour to 10 emails per hour?
Data can help answer these
questions, but they are low impact
Data should be leveraged to answer
questions with high business impact
Are there high LTV “Romantic” customers
that have a lower quality fix score?
What is the true “quality” of the inventory
relative to our current client mix? Do we
have enough medium “Classic” dresses in
inventory?

11. 10
How do you do it if you don’t have the
leader of algorithms for Google, Pandora,
Amazon or Netflix?
How do you know what questions to ask?
Tactic 4: Develop an approach for HOW TO DO IT
Impact of tactic: data that we collect and use drives improvement
in our business
Key challenges for startups…
…are addressed
with the following approach
Start with end in mind
• What do you think the drivers of
performance?
• What are the key metrics?
Truly put yourself in the customer’s shoes
• How do you shop?
• What are the most important things to
you?

12. 11
Agenda
Overview of Stitch Fix (very quick)
Tactics for success
Real life examples of using data with high impact
What I wish I knew?

13. 12
Real life example: Merchandising / inventory management
~30 questions that provide value to both the customer and to us really help
us manage capital efficiently
Sample range of annualized inventory turns

14. 13
Real
life
example:
Opera)ons
Strategic
handling
of
returns
or
restocked
items
The Old World: no organization or
ability to prioritize returns
The New World: bags prioritized by
impact on inventory
High priority
returns –
processed first
Low priority
returns –
processed last

15. 14
Agenda
Overview of Stitch Fix (very quick)
Tactics for success
Real life examples of using data with high impact
What I wish I knew?

16. 15
What
I
wish
I
knew
…
three
key
things
• For now, we don’t see value in measurements
• This business is very capital intensive
• Data can really risk reduce the “art” side of retail
1
2
3