1. Burglary Analysis
December 2016

2. Today’s objectives Share Ulta’s burglary data
What do the analytics tell us?
Where do we see correlation with risk factors?
Can we predict burglary risk?
What’s worked for Ulta in impacting burglary losses?

3. Burglary loss background
Burglaries at Ulta follow similar pattern regardless of location
Middle of night (12AM – 5AM) – breaking front door/window glass for entry
Multiple thieves generally in and out within 2 – 3 minutes
Police can’t respond to alarm call and arrive fast enough to apprehend thieves
Have good video/photos but few apprehensions
Burglaries will generally continue in market until thieves apprehended
Fragrance/Prestige Cosmetics are primary target – readily convertible to cash
Cash/safes aren’t a target
Past 21 months – 127 incidents at 70 stores
Average product loss approx. $4,000 at cost
Physical damage to store
Impact to store team – particularly for recurring incidents
Negative brand image to customer from store damage and out-of-stocks
21 month history

4. Burglary analytics
Reviewed burglary losses for correlation to a wide variety of risk factors, including:
Age of store
Market density
Center type
Crime risk scores
Time between burglaries in market
Found that burglaries are generally too random to build a burglary risk profile
Market risk is primary factor
Center type is secondary factor – shopping centers and open mall locations
Crime risk scores are not a good predictor of risk

5. Where are burglaries occurring?
Over 80% of burglary incidents occur in Texas and California
Incidents in other states are relatively random
Focused analytics on Texas and California
Consistent with prior years
Why Texas and CA – active ORC allows easy conversion of product to cash, but we all know that ORC not limited to these markets. For example, Phoenix and Chicago more established markets for Ulta.

6. Does store location matter?
Burglaries concentrated in suburban markets
No incidents in conventional enclosed malls or urban/street locations – thieves need fast access to vehicle to maximize product they can take
Two Dallas
stores

7. Are crime risk scores good predictors?
Slight negative correlation between rising crime risk score and burglary incidents
Our burglaries are concentrated in low-medium risk suburban markets where the vast majority of our stores are located
Burglars may avoid higher risk locations as assume added security
Burglars are mobile and will travel to stores where they perceive a lower risk of apprehension – will occasionally hit more than one store in market per night

8. Preventive measures
Roll down gates on new high risk stores – particularly where other retailers in center use the gates
Shortened alarm entry delay from 60 seconds to 30 seconds
Exterior cameras with strobe/siren on stores in high risk markets – existing and new stores
Adding 3M protective film to front of high risk stores – more difficult to break glass
Energy Mgmt. tied into Alarm (lights turn on if alarm triggers)
GPS tracking devices in high frequency markets – 3SI
Product designed to look and feel exactly like normal product
Alerts vendor 24/7 when GPS inside product leaves the store geo-zone and they notify police
Successful in stopping Dallas area burglaries this spring
Great strategy for markets with frequent burglaries