This document discusses trends in retail productivity and strategies for maximizing the return on labor dollars spent. It covers: 1) Key developments in retail from the 1950s to today that have impacted productivity, such as the rise of big box stores and e-commerce. 2) How retailers measure productivity in sales per labor hour and the goal of increasing this metric over time. Labor is the largest retail expense so foot traffic and product touches must be optimized. 3) Emerging challenges like omnichannel fulfillment and rising minimum wages that are pressuring retailers to find new efficiencies through technology and data-driven labor allocation.

1. Productivity Evolution and Harnessing Every Dollar
1
Brandon Wetzstein
Lead Business Partner- Order Pickup Planning and
Implementation
~20Years in Retail
Target
Best Buy
Audio King
Improviser
Beginning blacksmith?
Linkedin.com/brandonwetzstein
Twitter @curiousbrando

2.  1950’s – Post WWII – increased specialization and DIY- Home Improvement centers-
auto parts and tire stores- expansion and growth of the supermarket- Notably the TV
Dinner
 1960’s – Apparel rebellion (think tie-dye), Big Box hit’s the scene, Discounting becomes
a thing.
 1970’s-Office Retailers, Restaurant growth, Discounting theme continues, discount clubs
 1980’s- Rise in fashion and designer styles, Brand names, more office services and
discount clubs. Home shopping network (new channel) – computer use start to lend
efficiencies- Hardware growth, Decade of the chicken wing, fast casual born, premium
grocery
 1990’s- Internet retailers, comfortable casual, prescription filling computer system,
Growing fast casual and gourmet coffee, grocery mergers and superstore
 2000’s- Fashion all over the place (crocs), tech disruption, organic healthy and on te go.
Grocery merges
 Today:Tech everywhere, omni-channel flexibility, personalization, Save time,
recommendation engines.
2From Chain Store Guide's "Through the Ages” series.

3.  Basic measurement- very similar to ROI
 In retail, expressed in sales dollar per labor hour (or employee in a sales role)
 Ideally, should increase over time
 Store labor is largest component of SG&A Expense (For Big Box Retail)
 To oversimplify- Footsteps and Touches (Maximize here!)
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4.  3 Steps
 1.5 Seconds each
way
 5 pots/day
 $8/hr
 $12.17/yr
 7-Eleven
 8273 stores
 Coffee Filter
Fetching Cost =
$100,654
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5.  Assortment Complexity (Specialization & Localization)
 Shortened product lifecycle (technology, seasonal, trend)
 Product touches
 Footsteps
 Wage Rate (increases over next 5 yrs)
 Inventory & Supply Chain
 Accuracy
 Availability
 Turn
 Fulfillment
 Cube (holding)
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6.  Increased selection
 Localized product
 Immense variety
 Decreased product lifecycle time
 Technology
 Increased trends in non traditional trend areas
 Sriracha Ketchup?
 Cannibalization of the core
 More products = more facings = more touches
 Increased inventory risk
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9.  Omni-channel fulfillment
 Inventory accuracy is key- but challenging
 Stores as fulfillment centers
 Shipping from stores
 Shop online- pick up in store
 Buy online- ship to store (expanded assortment)
 Hard to find items (clothing – jorts)
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10.  Expected spikes in
minimum wage rate
over the next 5-10
years
 Some Retailers have
already expanded
wages
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19.1%
18.9%
19.2% 19.2%
20.1%
18.0%
18.5%
19.0%
19.5%
20.0%
20.5%
21.0%
21.5%
22.0%
22.5%
23.0%
0
100
200
300
400
500
600
2012 2013 2014 2015 2016
Walmart
Rev SG&A Percent
20.1%
20.3%
20.2%
19.8%
19.5%
18.0%
18.5%
19.0%
19.5%
20.0%
20.5%
21.0%
21.5%
22.0%
22.5%
23.0%
0
10
20
30
40
50
60
70
80
2012 2013 2014 2015 2016
TGT
Rev SG&A Percent
20.2%
21.1%
19.7%
18.8%
19.3%
18.0%
18.5%
19.0%
19.5%
20.0%
20.5%
21.0%
21.5%
22.0%
22.5%
23.0%
0
10
20
30
40
50
60
2012 2013 2014 2015 2016
BBY
Rev SG&A Percent
22.7%
22.1%
21.1%
20.2%
18.8%
18.0%
18.5%
19.0%
19.5%
20.0%
20.5%
21.0%
21.5%
22.0%
22.5%
23.0%
0
10
20
30
40
50
60
70
80
90
100
2012 2013 2014 2015 2016
Home Depot
Rev SG&A Percent
Source: Marketwatch.com

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13.  Technology- RFID, ESL, Self Checkout
 Localized predictive analytics (Machine learning)
 Precise allocation of resources based on Return on Labor (ROL)
 Knowledge sharing and interdepartmental cooperation
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14.  RFID- Inventory Accuracy, Location accuracy
 ESL- quick price change ability (dynamic, accurate pricing)
 Self Checkout
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15.  Combine location with historical sales and
promotional info
 Dive into location events, news, weather (open data)
 Football games
 Political rally’s
 Delegate/plan labor appropriately-
 Plan smart localized promotions
 Machine learning to truly understand variable impact
 Combine with qualitative EXPERTISE!
 Crowdsourced employee info?
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16.  Not quite productivity- but valuation on ROI by labor dept
 Minimum bases that need to be covered-
 Cashier,
 Customer Service
 Putting product away,
 Open and close the doors
 After base levels are met, determine best ROI per labor dollar spent by store
 Assortment changes
 Promotion management
 Clearance price management
 Example: $100 of labor to spend in a day-
 $60 is table stakes, where does the remaining $40 go?
 The same place as last year?
 Divided by department?
 OR does it go to the best possible resource available?
 In December, service in electronics yields the best return
 In June, It should be applied to assortment change
 In February, it should be applied to additional clearance markdowns
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17.  Connecting usually unrelated departments to find internal synergies
Marketing and operations?
Call center and product development?
Synergies exist, they just need to be found.
Example?
Cartwheel
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