1. What is it?
   1. Ability to locally adjust retail prices
      1. Drive Demand
      2. Protect margin
   2. Levels of execution
      1. Item
      2. Store
         1. Store Cluster
2. Why do it?
   1. Manage margin
      1. New York City bottled watter
      2. Hawaii
         1. At Target Alaska/Hawaii really exacerbated the issue
   2. Competitive adjustments
      1. Walmart examples
   3. Optimize seasonal/location opportunititeis
      1. Cameras at resorts
      2. WInter supplies
      3. Black Friday
   4. Create personlized experiences
   5. Manage inventory
3. How is it done?
   1. Types of price optmization
      1. Regular price
         1. Every Day Low Price
         2. Local price sensitivity
      2. Promotional Price
         1. Sales and special offers
      3. Clearance pricing
         1. Move inventory to liquidation
         2. Where it all started
            1. Set an out date for set of items, monitor sales and adjust price to meet the out date
   2. New Ideas
      1. Electronic shelf labels have opened an opportunity to do daily dynamic pricing
         1. Jury is probably still out from the customer perspective
         2. I paid 50C more at lunch than in the afternoon
      2. Geofencing
         1. Pushing offers to mobile devices based on where they are
            1. Esri has that capability
         2. Lingering
            1. Third trip to the TV dept this week... would 15% off push your decision
4. Vendors
   1. Lots of vendors providing this
      1. Issues
         1. Scale
            1. Passing massive amounts of very sensitive data
            2. Running algorithms against data sets with the sophistication in modeling
         2. Automation
            1. How automated are the algorithms?
         3. Store Execution
            1. How automated are processes to push prices to the stores
            2. the stores execute the results
         4. Models
            1. Need repeatable, predictable selling patterns to make it work
            2. Discretionary categories have proven difficult
   2. Math
      1. The arguments for the math become religion
      2. What algorithm do you like better?
      3. Different vendors have different strenghts
         1. Promo vs Reg
   3. Providers
      1. DemandTEc
      2. Khimetrics
      3. SAS
5. Retailers value
   1. KPI's in Pricing
      1. Sales rate and margin preformance
         1. Regular
            1. Demand signal
            2. Measuring rate of sales
            3. Managing seasonal spikes
            4. Margin preformance
            5. Brand perception
            6. Was I competitive
         2. Promo
            1. Making ad goals
            2. Growing ad sales over LY
            3. Maximizing sales while preserving margin
            4. How much did an extra 15% mean in my ad pricing for traffic and volume?
         3. Clearance
            1. Moving through excess inventory in the most profitable way possible
            2. Did I meet my out dates
            3. Did I maximize margin dollars
            4. Xmas giftwrap example
      2. Inventory movement
      3. Brand
6. When is it being done right?
   1. When the retailer is able to meet their KPI's around sales and margin
   2. When they're able to successfully manage pricing at very granular level of detail
   3. When their customers perceive value in their offerings and their prices