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Data Science Opportunity Assessment

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The SVDS Data Science Opportunity Assessment identifies—and concretely defines—the most valuable data science opportunities for your organization, and lays out the best path forward to realizing that value.

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Data Science Opportunity Assessment

  1. 1. DATA SCIENCE OPPORTUNITY ASSESSMENT Defining the ROI to Confidently Invest in Data Science
  2. 2. 2 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience
  3. 3. © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience YOU ARE BEING ASKED: Where can we drive value from data science? • What are opportunities with both long and short- term potential benefits? • What are the risks and trade-offs to be aware of? How do can justify this investment? • What is the ROI? • How can we prioritize and champion projects?
  4. 4. 4 @SVDataScience The key to maximizing investment in data science projects is prioritizing opportunities based on value.
  5. 5. 5 @SVDataScience Zara’s inventory distribution pilot suggests that a new allocation process increased sales by 3% to 4%, equivalent to $275M in additional revenues. (1) 1) http://faculty.london.edu/jgallien/ZaraOR2010.pdf
  6. 6. 6 @SVDataScience Target grew e-commerce sales by 34% from their integrated inventory solution, breaking a number of records and outperforming rival Walmart. (2) 2) http://www.marketwatch.com/story/target-has-taken-e-commerce-lead-against-bricks-and-mortar-rivals-analysts-2016-02-24
  7. 7. 7 @SVDataScience A P&G study estimates the impact of reducing out of stock rate on sales by 5% can drive about a $20M uplift in revenues. (3) 3) A Comprehensive Guide To Retail Out-of-stock Reduction In the Fast-Moving Consumer Goods Industry
  8. 8. 8 @SVDataScience How do you instill confidence in the value of your data science opportunities?
  9. 9. 9 @SVDataScience A RETAIL EXAMPLE Increasing Revenue from Online Sales
  10. 10. 10 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience A RETAIL EXAMPLE A hypothetical fashion brand, specializing in high-end apparel and other fashion merchandise, with stores around the world and a growing digital presence, is facing increased pressure from online competitors; wants to take advantage of integration across warehouse, in-store, and online. • Market capitalization: ~$20B • Annual revenue: ~$1.5B • Retail outlets (in-store) : represents 60% of revenue • Online eCommerce: represents 40% of revenue with 30% annual revenue growth
  11. 11. 11 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience WHERE TO START? • Qualify potential business benefit for opportunities • Explore the data’s fit for the opportunities • Prioritize investment in data science projects
  12. 12. 12 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience INVENTORY DISTRIBUTION FLOW IN-STORE ONLINE3rd Party Distributors Are we unintentionally overselling online? Are we optimizing the use of our inventory across all channels? Customers How much are we offering for sale online and can we offer more? Is the customer satisfied with their shopping experience? Will they buy again? Warehouse How do we incent customers to buy more? What’s the most cost-effective way to ship to customers?
  13. 13. 13 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience BENEFIT TO BUSINESS Dist. Channel / Data Source Share of Inventory / Business Potential Revenue Uplift In-store 60% 3% Warehouse 30% 1.5% 3rd Party 10% 0.5% In-store has a greater share of inventory and thus potential benefit
  14. 14. 14 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience SAMPLE ROI ESTIMATES Improving inventory availability via predictive methods will lead to more sales, based on market studies ~$1.5B Sample Annual Revenue 1.5% POC Revenue Uplift ~$22.5M Incremental Revenue ~$1.5B Sample Annual Revenue 5% Integrated Solution Revenue Uplift ~$75M Incremental Revenue Illustrative Warehouse Channel All Channels
  15. 15. 15 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience Validate expected impact from your data: • Confirm problem / opportunity statements • Assess data fit for the opportunity • Generate initial insights • Estimate effort for feature engineering and modeling EXPLORATORY DATA ANALYSIS
  16. 16. 16 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience SUMMARY: DATA RISKS Dist. Channel / Data Source Latency Quality History Shipping In-store ~ 1 hour Medium— suffers from miscounts, missing stock 2 years None Warehouse Real-time Very High—no major issues 5 years Fine-grained 3rd Party ~ 24 hours Low—could not assess 6 months Some Warehouse channel has the least data risk
  17. 17. 17 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience DATA RISKS Inventory Feeds • Latency—real time vs. batch? • Quality—were risks exposed via Exploratory Data Analysis? • History—How far back can we go for model training? Shipping Data • From which distribution locations can we ship to customers?
  18. 18. 18 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience QUICK WIN VS. BIGGER PAYOFF? • In-store has a bigger percentage of inventory and potential business than warehouse HOWEVER: • Data risk is much lower with warehouse, thus ability to predict is more feasible CONCLUSION: Start with warehouse distribution channel to better predict inventory availability, then expand to other distribution channels later on
  19. 19. 19 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience WHICH OPPORTUNITY FIRST? # Opportunity 1 Predicting inventory will improve accuracy of what is available to sell 2 Optimizing the distribution channel will reduce out-of- stock rates and improve overstock 3 Incentive messaging when stock is low will compel customers to buy 4 Customer behavior profiling will provide more engaging features to improve incentive messaging 5 Individual customer attributes will personalize results leading to greater conversion rates for sales
  20. 20. 20 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience PRIORITIZING OPPORTUNITIES • Are some opportunities higher value than others? • Can some be more easily implemented than others? • Is there an order in which they must be implemented due to pre-requisites?
  21. 21. 21 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience OPPORTUNITY POTENTIAL VALUE DATA AVAILABILITY EXISTING CAPABILITY TECHNICAL FEASIBILITY SKILL AVAILABILITY ARCH FIT EFFORT RISK SCORE Predict Inventory $$$ 1 Optimize Distribution Channel $$$$ 2 Incentive Messaging $$$ 3 Customer Behavior Profiling $$ 4 Personalization $$$$$ 5 ASSESSING PRIORITIZATION FACTORS
  22. 22. 22 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience COMMON THEMES • Most opportunities require an accurate view of inventory • Complicating factors exist, such as: • Multiple distribution channels • “Lost”/misplaced stock • Latency between purchase time and allocation • Improving real time inventory accuracy benefits all • We can predict inventory availability using data to overcome these complicating factors
  23. 23. 23 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience ROI: DATA SCIENCE PRIORITIZATION OPPORTUNITY POTENTIAL VALUE (BENEFIT-COST) RISK SCORE OVERALL “ROI” SCORE Predict Inventory $$$ 1 3 / 1 = 3 Optimize Distribution Channel $$$$ 2 4 / 2 = 2 Incentive Messaging $$$ 3 3 / 3 = 1 Customer Behavior Profiling $$ 4 2 / 4 = 0.5 Personalization $$$$$ 5 5 / 5 = 1 Over time, priorities may change as organizational capabilities mature.
  24. 24. 24 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience SEQUENCING OF OPPORTUNITIES Increase Revenue from Online Sales 1. Improve Inventory Accuracy 2. Optimize Distribution Channel 4. Personalize interactions . . . 3. Provide Incentive Messaging 5. Profile Customer Behaviors We need to know what we have to sell first Availability needed for urgency messaging Optimizing channels increases availability Test & refine with real customers
  25. 25. 25 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience DATA SCIENCE OPPORTUNITY ASSESSMENT Learn more: https://svds.com/what-we-do/data-science-opportunity-assessment/
  26. 26. 26 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience Silicon Valley Data Science is a specialized consulting firm focused on transforming your business through data science and engineering.

The SVDS Data Science Opportunity Assessment identifies—and concretely defines—the most valuable data science opportunities for your organization, and lays out the best path forward to realizing that value.

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