How DSW Redefined Shrink and EBR to Drive Loss Prevention Success

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How DSW Redefined Shrink and EBR to
Drive Loss Prevention Success
JORDAN RIVCHUN, DSW
GUY YEHIAV, Profitect
SPONSOR
How DSW Redefined Shrink and EBR to Drive
Loss Prevention Success

2. 6/28/2017
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Introductions
JORDAN RIVCHUN
Director, Loss Prevention
DSW
GUY YEHIAV
CEO
Profitect
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Our Journey
508 stores | 43 states
396 leased departments for other retailers
Co-owners in Town Shoes of Canada (188 locations)
LP Team of 16 (10/16 are in the field)
Profitect client since 2014
Inventory module (2014- current)
LP, Inventory Control, Quality, and Merchandise Planning
and Analytics.
Sales Module (May 2017)
Brick and Mortar and E-Comm data
About DSW

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Traditional EBR vs. Prescriptive Analytics
Push vs Pull
Big Data vs Actionable Data
Eliminate false positives with feedback and
machine learning
Accountability and task
management
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Give Me My Data
Examples of data we have integrated:
While not all data is useful—Feed
everything, decide what you need
Flexible platform, flexible partner
Incident reports LP Assessments
Customer Loyalty Data Non-Receipted returns
Charge/send declines, in addition to traditional
TLOG feeds, etc.

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Key Focus Areas
Inventory Data
Damages/RTV’s
Under/Over Allocated
Items
Empty Box Loss
Analysis
Shrink Analytics
POS – Sales/E-Comm
Shrink
Margin – Discounts, coupons, loyalty
Operational controls and efficiencies
HR Details/Loyalty Details
Predictive Shrink Modeling
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Change
Your users MUST to be willing to change.
Gone are the days of digging for data and
analysis paralysis
Allow the system to detect patterns based on
conditions your users set, or automatic
User feedback is imperative for system
success.
Prescribed actions ensure
consistency in execution and
resolution.

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Result examples
A few cases since implementing prescriptive
analytics solution for EBR and Inventory:
1 Empty Box Log Reduction
2 Return To Vendor Quality Compliance
3 Discounting/Fraud E-Comm
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Timberland Styles w/ High ShrinkTrend
Building shrink trend in real time of Timberland boots
identified by Profitect Patterns Opportunities.
▪ Further product drilldown in Profitect helped identify that 82% of
our Timberland shrink was coming out of Styles (Tan) that only
made up 27% of our store on hand inventory ownership.
82% OF SHRINK
WAS FROM A STYLE THAT ONLY MADE UP
27% OF STORE ON HAND
INVENTORY
Created a temporary procedure to move the left boot to the
back of the stores due to such high shrink % of sales.
▪ A comp time period after the left boot was pulled saw a 64%
reduction in Shrink $ while Inventory ownership and sales
were more than double in this Timberland Style.

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RTV / Damages
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E-Comm / Call Center Fraud
PATTERN:
CSR’s with lost stolen appeasements and zero dollar
orders over 50% of the call center benchmark.
CASE IDENTIFIED:
￭ $18,500 in Free orders in a 4 week time period
￭ Identified in day two of system training / rollout
￭ New patterns created based on interview admission
￭ Few more cases of this behavior since

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Q&A Discussion
▪ What has been the feedback from your team of moving from a push to pull model?
▪ “If you had one recommendation to someone looking for an EBR solution what would it
be?”
▪ What has been the level of adoption from other departments?
▪ What was your IT department’s role and reaction to adopting prescriptive analytics?
▪ What surprised you the most since implementing prescriptive analytics?
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Questions?