This document discusses how Shein uses data and algorithms to disrupt the fast fashion industry. It summarizes that Shein uses an engagement algorithm in its app to gather data on customer preferences and only produces items that generate high engagement. This allows Shein to produce smaller batches tailored to customer needs with less capital and faster production. The document advocates that companies flip their production model to first generate customer data and then produce items, with the user experience designed to optimize data generation and a self-learning algorithm that improves the experience over time.

1. The Data Driven Company

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Every board room ever…
“What are we doing with DATA?”

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Data is the new oil, right?

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Data is like sand…
All around us.
Cheap and even without value of itself.
Unless you use it in your production process.

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And I’ll use this as an example:

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Chances are, unless you have a
teenage daughter,
you won’t know this company.

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But
fi
rst, a little detour.
(And yes, you also need a teenage daughter
to know this app.)

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TikTok
disrupted social
media, by using a
new kind of
algorithm based on
engagement data.
Not determining reach and spread of content
based on our social network (number of
‘friends’ we have) but on the engagement
each piece of content generates.

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100
high engagement
low engagement
1.000
drop
high engagement
low engagement
10.000
drop
high engagement
low engagement
100.00
0
drop
Better ‘
fi
t’ for viewers > addictive app.

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TikTok: algorithm.
Engagement data in the core of the product.
Thus delivering a better user experience.
And using this feedback loop to create a better product.

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Back to Shein.
Shein is the TikTok of fast fashion.
Using an engagement algorithm
to disrupt fashion retail.

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100
high engagement
low engagement
1.000
drop
high engagement
low engagement
10.000
drop
high engagement
low engagement
100.000
drop
Showing products to a limited set of users, and using digital
engagement as decision engine to produce or not.

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Data as start of the product.
Using engagement data in the app to predict the success
of fashion items.
Consequences:
• better
fi
t to user need as engine learns
• only producing what is virtually sold, creating less stock
• predicting raw material need, needing less capital
• faster & smaller batches, allowing vastly more SKU
• serious price advantage, only improving with algorithm

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design & produce items
(‘guesstimating numbers’)
‘Normal’ fashion companies:
distribute to
warehouses & stores
marketing & sales
gauge digital intrest
Shein:
produce exact
number of items
ship directly
to consumer
end user
end user

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Flipping the supply chain.
Putting user experience & data
fi
rst.
Creating a disruptive advantage.

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Caveat!
• I am not endorsing fast fashion!
• Shein is also exploiting loopholes in trade agreements & taxations.
• Always be wary of Chinese companies & privacy.
• Not easy to integrate your total supply chain.
• D2C not always an option.

18. The Big Flip
What can we learn from Shein?
1. Flip your production chain
2. UX as data generator
3. Self-learning engine

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Flip your production chain.
First generate the data of what your customers will buy,
and only then produce the items.
1

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UX as data generator.
• UX of the Shein app is mostly aimed at generating
engagement data and insight in your taste.
• Conversion to sale comes second.
• As a new user, your engagement data is more valuable
in powering the
fl
ywheel then your initial sale.
2

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The algorithm catches user data, which makes the
user experience better, which attracts more users,
which generate more data.
3
Create a self-learning loop.

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Data in your core.
Data is not something that is generated as a by-product of your core business;
and afterwards tortured to generate some magical insight.
Data should be embedded in the core and at the start of your production line.

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Questions to ask yourself.
• Can we generate user data?
• Can we put that data at the start of our production process?
• Can we build a self learning feedback loop to enhance the user experience?
• Can we engineer the user experience to generate more and better data?
Build a data driven company.

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Questions to ask Duke & Grace.
How can Duke & Grace help you engineer your user experience?
bart.dewaele@dukeandgrace.com

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Questions?
Shoot.