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. DATA SCIENCE OPPORTUNITY ASSESSMENT Defining the ROI to Confidently Invest in Data Science
- 2. 2 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience
- 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 @SVDataScience The key to maximizing investment in data science projects is prioritizing opportunities based on value.
- 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 @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 @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 @SVDataScience How do you instill confidence in the value of your data science opportunities?
- 9. 9 @SVDataScience A RETAIL EXAMPLE Increasing Revenue from Online Sales
- 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 © 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 $$ 4 Personalization $$$$$ 5 ASSESSING PRIORITIZATION FACTORS
- 22. 22 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience COMMON THEMES • Most opportunities require an accurate view of inventory • Complicating factors exist, such as: • Multiple distribution channels • “Lost”/misplaced stock • Latency between purchase time and allocation • Improving real time inventory accuracy benefits all • We can predict inventory availability using data to overcome these complicating factors
- 23. 23 © 2017 SILICON VALLEY DATA SCIENCE LLC. ALL RIGHTS RESERVED. @SVDataScience ROI: DATA SCIENCE PRIORITIZATION OPPORTUNITY POTENTIAL VALUE (BENEFIT-COST) RISK SCORE OVERALL “ROI” SCORE Predict Inventory $$$ 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 © 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 © 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 © 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.
Notas do Editor
- In our customer conversations, for example with someone who’s been put in charge of doing something with your company's data (you're sitting on some data) (we also get people who want a magic box to solve some specific problem) many people wonder ...and we hear it so often that we’ve developed an offering; you hear the success stories, not the failures, but lots of people struggle. So what we want to do today is help people understand… <other wordings/ Questions> I will get value, or how much value might I get What problems might derail an effort How might I know or assure myself or be more confident that… How do I know I should make the jump? Where might I get started in my DS efforts? What actions could I take to make DS more valuable? <transition> Today we’ll be talking about our approach to answering these questions, starting with some views about data strategy, but really emphasizing the data science components.
- Many ideas—where to start?
- A couple of ways to proceed…
- POC ASSUMPTIONS: - % of Sales are Online versus in Store # of products featuring urgency messaging Product sales channel mix All figures are global Distinction between mobile versus web based sales 5%: Based on overall P&G study for reducing out-of-stock rates
- Now that we’ve picked a use case, we want to further asses what the ROI will be and surface any risks related to data that could impact the expected return. Furthermore, this use case can be applied across multiple distribution channels such as from the warehouse, from stores, or from 3rd parties, so we could benefit from additional prioritization. Channel – distribution versus where the customer goes to purchase the item. For example, there are likely a few key areas where we would want to make sure that we have data available. Most important – is the inventory data, it will be difficult to determine what is available for sale if we don’t even have this to begin with! But there may be some other interesting areas like shipping-can we determine t
- How technically feasible is building a system to execute the use case? if it takes a PhD because it’s cutting edge, you may decide not to go that direction. How accessible, both within your company and in the market at large, are the skills that are needed to develop the use case? These first 2 are outward facing, there are others that are more inward facing How well does the use case fit into your existing technology and operations architectures? How much effort will a full-scale production roll out of the new technology require? how will you need to support this, what are the politics, etc This is also an illustrative, simplified version vs something we might actually create
- For example, there are likely a few key areas where we would want to make sure that we have data available. Most important – is the inventory data, it will be difficult to determine what is available for sale if we don’t even have this to begin with! But there may be some other interesting areas like shipping-can we determine t
- We first need to know what we even have for sale. Then we can optimize our distribution. Then we can see how customers react. Then we can anticipate and personalize. Say you have a good amount of data on customers that you’re already using for targeted marketing, now you want to test interactions with your online store <tie back to ROI>