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Machine Learning in Retail: ML in the Retail Sector
The document discusses the impact of machine learning (ML) in the retail sector, highlighting its applications in areas such as operations, marketing, and customer experience. It emphasizes the importance of ML for improved efficiency, decision-making, and business performance, while noting the growing adoption and potential of predictive applications. Key takeaways suggest that ML should support rather than replace personnel, and that successful implementation requires a focus on business problems rather than infrastructure.
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Machine Learning in Retail: ML in the Retail Sector
- 1. Atakan Cetinsoy VP ofPredictive Applications Machine Learning in Retail Nov 3, 2021 Intelligent Mobility
- 2. BigML,Inc Machine Learning inRetail 2 Omnichannel Automation Personalization Retail Industry Megatrends x2 Speed Post Pandemic
- 3. BigML,Inc 3 Retail ML BusinessImpact CRITICAL SUCCESS FACTORS *SOURCE: NRF Real-time Insights Better Efficiency Task Automation Improved Decisions Anticipating Needs Increased Security BUSINESS PERFORMANCE ML Operational Expenditure 7% Annual Revenue 10% Machine Learning in Retail
- 4. BigML,Inc STORE OPERATIONS PURCHASING INVENTORY MANAGEMENT DISTRBUTION & LOGISTICS SALES & MARKETING RetailML Use Cases • Assortment Planning • Forecasting • Automated Reconciliation 4 • Replenishment Optimization • Dynamic Pricing • Markdowns • Same Day Delivery • Route Optimization • Smart Packaging • Returns Management • POS Automation • Security & Fraud • Loyalty & Promotions • Cross Sell & Up • Recommendations Machine Learning in Retail
- 5. BigML,Inc 5 Problem Statement Examples CLASSIFICATION REGRESSION TIMESERIES FORECASTING CLUSTER ANALYSIS ANOMALY DETECTION ASSOCIATION DISCOVERY Should these fresh produce be marked down? What will this customer’s LTV will be? How many cases of this wine will sell next month? Are these customers similar? Is the product review real or fake? What are the necessary conditions to ensure high NPSs? Machine Learning in Retail
- 6. BigML,Inc 6 Retail ML Adoptionby Function 0% 22,5% 45% 67,5% 90% Supply Chain Planning Distribution & Logistics Customer Experience Store Operations 41% 41% 43% 49% 33% 29% 27% 36% Actual (2019) Planned (2021) Machine Learning in Retail *SOURCE: NRF
- 7. BigML,Inc Machine Learning inRetail 7 Companies with 5+ Machine Learning applications in production choose to license commercial solutions at an even higher rate than ML Newbies. Enhancing Existing Products and Services Creating NEW Products and Revenue Streams Top Objectives of AI/ML Initiatives ML Platform — Build or Buy? * SOURCE: Deloitte Insights, State of AI in the Enterprise, 3rd Edition, 2020 Lowering Costs | Making Processes More Efficient
- 8. BigML,Inc 8 Real-life Example UseCase Machine Learning in Retail *SOURCE: https://blog.bigml.com/2020/07/15/machine-learning-fights-cannibalization-in-the-retail-industry/
- 9. BigML,Inc Machine Learning inRetail 9 WORKFLOWS DATA SOURCES ALERTS BIGML PLATFORM Machine Learning platforms provide a strong foundational layer by packaging the best algorithms, workflows, scripts in an easily consumable manner Thus, allowing Customers to scale thousands of models across a multitude of predictive use cases. End users interface with easy- to-digest alerts, dashboards and recommendations. SOURCE SERVER DATASET SERVER MODEL SERVER PREDICTION SERVER EVALUATION SERVER SAMPLE SERVER WHIZZML SERVER R e p e a t a b l e a n d programmable workflows handle individual use cases in a Connectors for multiple data sources make it easy to integrate ML platforms with client IT systems. Building Scalable Predictive Apps Faster time to market Minimized technical risk Ability to focus on business use cases — not infrastructure
- 10. BigML,Inc 10 Key Takeaways • SuccessfulMachine Learning applications in retail are not about replacing personnel but supporting decision making. Frontline employee participation is critical to achieve operational agility. • There are some great examples from early adopters pushing the envelope but Machine Learning adoption in retail is far from saturation. • Think big but start small. Done right, smart applications can be a significant competitive differentiators. • Machine Learning platforms can help the next wave of adopters accelerate their initiatives by helping them concentrate on solving business problems instead of building infrastructure. Machine Learning in Retail
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