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CASE STUDIES IN DATA-
DRIVEN MERCHANDISING
Erin Shellman — Nordstrom Data Science
Data Science Salon, Seattle
October 17, 2019
E-COMMERCE IS CHANGING THE LANDSCAPE
➤ Evolving customer expectations pose
challenges to brick-and-mortar
operations. e.g.
➤ Online shoppers expect broad
assortment and inventory availability.
➤ Online shoppers expect fast shipping and
convenience.
➤ Customers can shop directly with
brands.
➤ Customers want to try things on at
home and have frictionless returns.
E-COMMERCE IS CHANGING THE LANDSCAPE
➤ Evolving customer expectations pose
challenges to brick-and-mortar
operations. e.g.
➤ Online shoppers expect broad assortment and
inventory availability.
➤ Online shoppers expect fast shipping and
convenience.
➤ Customers can shop directly with
brands.
➤ Customers want to try things on at
home and have frictionless returns.
Inventory Management!!
MODERN INVENTORY MANAGEMENT IS MORE THAN EXCEL CAN HANDLE
➤ Many brands and retailers struggle to
adopt modern inventory management.
➤ In 2016 Ralph Lauren fell victim to the
long tail of inventory assortment that
diminished focus on core brands1.
➤ In 2018 H&M and Under Armour
collectively sat on ~$6B in unsold
inventory in part because of too many
product offerings2,3.
➤ Store in-stocks are suffering and driving
customers to shop more online.
“There are so many [items of clothing]
that a power plant in Vasteras, the
town where H&M founded its first
store, relies partly on burning
defective products the retailer cannot
sell to create energy.
Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
InventoryTrends
Distribution
to size
Distribution
to location
Views, add-to-bags, purchases
NORDACE
Customers
H
istoricalsales
Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Data Science
H
istoricalsales
Views, add-to-bags, purchases
Customers
ASSORTMENT PLANNING
Buying the right mix
ASSORTMENT PLANNING THEN
➤ Buying the right product assortment is
all about anticipating emerging trends and
estimating customer demands.
➤ Historically this is an art involving:
➤ Trend forecasting
➤ Trade fairs
➤ Wholesale showrooms and fashion
shows
ASSORTMENT PLANNING NOW
➤ Modern assortment planning is
a combination of fashion
knowledge and authority, and
data-driven tools for portfolio
management.
➤ Our strategic partnership with
NuOrder yields rich data that’s
transforming how we select
product.
Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases
C
atalogs
REPURPOSING PERSONALIZATION FOR INVENTORY ASSORTMENT
➤ Cross-catalog search
➤ Identify multiple price points
for similar styles.
➤ Measure duplicative inventory.
➤ Performance estimation
➤ Forecast sales performance
prior to commitment to buy.
➤ Use segmentation to estimate
addressable audience size for
product.
< $150
$150 - 200
> $ 200
CASE STUDY: IDENTIFYING DUPLICATIVE INVENTORY
➤ Looks gives customers styling ideas
and inspiration on product detail
pages.
➤ Looks substitution uses a
combination of text and visual
features to swap in similar
product.
➤ We applied the algorithm on
historical inventory to support
quick adjustments made by our
merchandisers.
Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases
C
atalogs
Assortment
Planning
UNIT QUANTIFICATION
Finding the in-stock sweet spot
UNIT QUANTIFICATION IS HARD TO GET RIGHT
➤ Operating both a large brick-and-
mortar and online business adds
enormous complexity to inventory
management.
➤ Getting it right requires:
➤ Knowing the intended product lifecycle
➤ Forecasting demand at many levels of
granularity e.g. channel and location
➤ Supply chain innovation helps too!
➤ “This approach allows us to provide our stores
with an initial allocation of inventory and
then to dynamically reallocate in-season to
stores needing it most, reducing markdown
risk, out-of-stocks and helping to drive
fashion and newness"
InventorySalesvelocity
CASE STUDY: FORECAST-DRIVEN BUY PLANNING
➤ From a labor-intensive, Excel / VBA-based workflow to one that is guided by forecasts.
➤ Our approach allows us to blend in-store and digital signals as well as product features.
➤ We’re controlling change management complexity by delivering outputs in identical
formats, allowing us to decouple the methods from the delivery path.
Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Quantification
Assortment
Planning
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases
PERSONALIZATION
Moving high risk inventory
PERSONALIZATION IS A LAST-MILE TOOL TO MOVE INVENTORY
➤ Products don’t always move as
planned!
➤ Broken inventory is a normal
part of the product lifecycle.
➤ Personalization usually centers
on customer experience, but
can be used to move broken
inventory before it becomes a
markdown.
CASE STUDY: SIZE AWARE SEARCH AND BROWSE
➤ We found that top ranked
items in search and browse
pages frequently contained
‘broken’ products.
➤ Now making adjustments to
allow product rank to decay as
they become broken.
➤ Offset markdown risk by
boosting broken products
when we’re confident in
customers’ sizes.
1 2 3 4
Brokenness
Time
Vendors
Buyers
PO
Fashion Office
Stores
Distribution center
L
M
S
I need 100
units!
Purchase
O
rder
Inventory
Trends
Historical sales
Distribution
to size
Distribution
to location
Quantification
Personalization
Assortment
Planning
Data Science
Customers
H
istoricalsales
Views, add-to-bags, purchases
CLOSING TIPS FOR DRIVING MASSIVE CHANGE
➤ It’s a big ecosystem, but focus on a small, self-contained
piece.
➤ Tackle one change at a time, i.e. you don’t need to change the
methods and the process at once.
➤ Look for opportunities for data and algorithm reuse, e.g.
purchase ordering management solution as a product data
source, or digital algorithms to characterize product.
➤ Learn how your business works by building relationships with
your stakeholders.
THANK YOU!
REFERENCES
➤ What Are The Challenges Facing Ralph Lauren? https://
www.forbes.com/sites/greatspeculations/2016/06/14/what-are-
the-challenges-facing-ralph-lauren
➤ H&M, a Fashion Giant, Has a Problem: $4.3 Billion in Unsold
Clothes. https://www.nytimes.com/2018/03/27/business/hm-
clothes-stock-sales.html
➤ A massive shift in American fashion is causing a $1.3 billion
problem for Under Armour. https://www.businessinsider.com/
under-armour-inventory-problem-2018-7

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Case studies in data-driven merchandising

  • 1. CASE STUDIES IN DATA- DRIVEN MERCHANDISING Erin Shellman — Nordstrom Data Science Data Science Salon, Seattle October 17, 2019
  • 2. E-COMMERCE IS CHANGING THE LANDSCAPE ➤ Evolving customer expectations pose challenges to brick-and-mortar operations. e.g. ➤ Online shoppers expect broad assortment and inventory availability. ➤ Online shoppers expect fast shipping and convenience. ➤ Customers can shop directly with brands. ➤ Customers want to try things on at home and have frictionless returns.
  • 3. E-COMMERCE IS CHANGING THE LANDSCAPE ➤ Evolving customer expectations pose challenges to brick-and-mortar operations. e.g. ➤ Online shoppers expect broad assortment and inventory availability. ➤ Online shoppers expect fast shipping and convenience. ➤ Customers can shop directly with brands. ➤ Customers want to try things on at home and have frictionless returns. Inventory Management!!
  • 4. MODERN INVENTORY MANAGEMENT IS MORE THAN EXCEL CAN HANDLE ➤ Many brands and retailers struggle to adopt modern inventory management. ➤ In 2016 Ralph Lauren fell victim to the long tail of inventory assortment that diminished focus on core brands1. ➤ In 2018 H&M and Under Armour collectively sat on ~$6B in unsold inventory in part because of too many product offerings2,3. ➤ Store in-stocks are suffering and driving customers to shop more online.
  • 5. “There are so many [items of clothing] that a power plant in Vasteras, the town where H&M founded its first store, relies partly on burning defective products the retailer cannot sell to create energy.
  • 6. Vendors Buyers PO Fashion Office Stores Distribution center L M S I need 100 units! Purchase O rder InventoryTrends Distribution to size Distribution to location Views, add-to-bags, purchases NORDACE Customers H istoricalsales
  • 7. Vendors Buyers PO Fashion Office Stores Distribution center L M S I need 100 units! Purchase O rder Inventory Trends Historical sales Distribution to size Distribution to location Data Science H istoricalsales Views, add-to-bags, purchases Customers
  • 9. ASSORTMENT PLANNING THEN ➤ Buying the right product assortment is all about anticipating emerging trends and estimating customer demands. ➤ Historically this is an art involving: ➤ Trend forecasting ➤ Trade fairs ➤ Wholesale showrooms and fashion shows
  • 10. ASSORTMENT PLANNING NOW ➤ Modern assortment planning is a combination of fashion knowledge and authority, and data-driven tools for portfolio management. ➤ Our strategic partnership with NuOrder yields rich data that’s transforming how we select product.
  • 11. Vendors Buyers PO Fashion Office Stores Distribution center L M S I need 100 units! Purchase O rder Inventory Trends Historical sales Distribution to size Distribution to location Data Science Customers H istoricalsales Views, add-to-bags, purchases C atalogs
  • 12. REPURPOSING PERSONALIZATION FOR INVENTORY ASSORTMENT ➤ Cross-catalog search ➤ Identify multiple price points for similar styles. ➤ Measure duplicative inventory. ➤ Performance estimation ➤ Forecast sales performance prior to commitment to buy. ➤ Use segmentation to estimate addressable audience size for product. < $150 $150 - 200 > $ 200
  • 13. CASE STUDY: IDENTIFYING DUPLICATIVE INVENTORY ➤ Looks gives customers styling ideas and inspiration on product detail pages. ➤ Looks substitution uses a combination of text and visual features to swap in similar product. ➤ We applied the algorithm on historical inventory to support quick adjustments made by our merchandisers.
  • 14.
  • 15. Vendors Buyers PO Fashion Office Stores Distribution center L M S I need 100 units! Purchase O rder Inventory Trends Historical sales Distribution to size Distribution to location Data Science Customers H istoricalsales Views, add-to-bags, purchases C atalogs Assortment Planning
  • 16. UNIT QUANTIFICATION Finding the in-stock sweet spot
  • 17. UNIT QUANTIFICATION IS HARD TO GET RIGHT ➤ Operating both a large brick-and- mortar and online business adds enormous complexity to inventory management. ➤ Getting it right requires: ➤ Knowing the intended product lifecycle ➤ Forecasting demand at many levels of granularity e.g. channel and location ➤ Supply chain innovation helps too! ➤ “This approach allows us to provide our stores with an initial allocation of inventory and then to dynamically reallocate in-season to stores needing it most, reducing markdown risk, out-of-stocks and helping to drive fashion and newness"
  • 18. InventorySalesvelocity CASE STUDY: FORECAST-DRIVEN BUY PLANNING ➤ From a labor-intensive, Excel / VBA-based workflow to one that is guided by forecasts. ➤ Our approach allows us to blend in-store and digital signals as well as product features. ➤ We’re controlling change management complexity by delivering outputs in identical formats, allowing us to decouple the methods from the delivery path.
  • 19. Vendors Buyers PO Fashion Office Stores Distribution center L M S I need 100 units! Purchase O rder Inventory Trends Historical sales Distribution to size Distribution to location Quantification Assortment Planning Data Science Customers H istoricalsales Views, add-to-bags, purchases
  • 21. PERSONALIZATION IS A LAST-MILE TOOL TO MOVE INVENTORY ➤ Products don’t always move as planned! ➤ Broken inventory is a normal part of the product lifecycle. ➤ Personalization usually centers on customer experience, but can be used to move broken inventory before it becomes a markdown.
  • 22. CASE STUDY: SIZE AWARE SEARCH AND BROWSE ➤ We found that top ranked items in search and browse pages frequently contained ‘broken’ products. ➤ Now making adjustments to allow product rank to decay as they become broken. ➤ Offset markdown risk by boosting broken products when we’re confident in customers’ sizes. 1 2 3 4 Brokenness Time
  • 23. Vendors Buyers PO Fashion Office Stores Distribution center L M S I need 100 units! Purchase O rder Inventory Trends Historical sales Distribution to size Distribution to location Quantification Personalization Assortment Planning Data Science Customers H istoricalsales Views, add-to-bags, purchases
  • 24. CLOSING TIPS FOR DRIVING MASSIVE CHANGE ➤ It’s a big ecosystem, but focus on a small, self-contained piece. ➤ Tackle one change at a time, i.e. you don’t need to change the methods and the process at once. ➤ Look for opportunities for data and algorithm reuse, e.g. purchase ordering management solution as a product data source, or digital algorithms to characterize product. ➤ Learn how your business works by building relationships with your stakeholders.
  • 26. REFERENCES ➤ What Are The Challenges Facing Ralph Lauren? https:// www.forbes.com/sites/greatspeculations/2016/06/14/what-are- the-challenges-facing-ralph-lauren ➤ H&M, a Fashion Giant, Has a Problem: $4.3 Billion in Unsold Clothes. https://www.nytimes.com/2018/03/27/business/hm- clothes-stock-sales.html ➤ A massive shift in American fashion is causing a $1.3 billion problem for Under Armour. https://www.businessinsider.com/ under-armour-inventory-problem-2018-7