1. Agenda Introduction and background Cobbler talk/ The athletic footwear industry and what it takes to make a shoe The perpetual use of analytics in a consumer product life cycle Data used in brick and mortar and DTC Milestone pods: a simple case study

2. Introduction: It’s really quite simple Question: “What do I do when I go to work?” Answer: “You try to find ways to make people buy more shoes.”

3. The shoe business Highly competitive Low(er) barriers to entry Multiple demos Diverse distribution Est. to reach $18B in U.S. $$ by 2018

4. “I’ll never use this in real life”

5. Subjective vs. Objective To what extent is the industry of athletic footwear and apparel data driven? In other words, to what extent does data determine the fashion influence in consumers purchases? Let’s explore both sides

6. The use of data in a competitive and constantly-evolving marketplace and how it informs actions requires balance What we know the consumer wants What the consumer doesn’t know they want F&B’sAestheticsDesign innovationBuying behavior Data: pricing, demand, messaging, advertising, promotions, etc.

7. Why does a fashion-driven consumer product need data to inform design?

8. But it’s just a shoe? Shoe brief Pricing and Demand elasticity Historical perspective Fashion attributes Trends Production planning Inventory turns Promotions Distribution strategy SalesForecasting Competitive analysis

9. Product critical path, How long? Plan/BriefBuildDeliver

11. Product Lifecycle and perpetual need for data Plan/Brief/Design Forecast & Production DeliverAnalyzeProduction Plan/Brief/Design Forecast & Production DeliverAnalyzeProduction Plan/Brief/Design Forecast & Production DeliverAnalyzeProduction Year 0 Year 1 Year 2

12. What are we looking at for brick and mortar sales? Inventory turns or liquidation % – Inventory carrying costs Sales trends by styles, sku’s, price points Issues with Cannibalization Effectiveness of the merchandising plan

13. Direct to consumer sales: As much data as you want Immediate and perpetual scorecards monitored Key Performance Indicators: open rates, abandon rates, abandon locations, UPT’s, AOV Cross sell and up sell opportunities AB testing opportunities

15. Customer service metrics

16. Simple Case Study: Sports wearable’s

17. The customer journey

18. Data for the runner 18

19. Consumer data for the brand 19