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Future of Data and AI in Retail - NRF 2023
- 1. Rob Saker Global VP Retail & Manufacturing, Databricks What’s Next in Retail Data & AI? Predictions for 2023
- 2. Labor wage & capacity Labor wage growth has increased, and staffing remains a challenge
- 3. Out of stocks near record levels Consumers say 1 in 5 items are out of stock in supermarkets.
- 4. Margin Challenges with Delivery E-commerce fulfillment in stores is having a major adverse effect on retailer margins. McKinsey 2022: https://www.mckinsey.com/industries/retail/our-insights/achieving-profitable-online-grocery- order-fulfillment 4.4% Net P&L -12.9% Basket Margin 27.9% Gross Margin
- 5. Pursuing New Revenue Opportunities Retailers are vying for new revenue sources and bigger share of ad budgets with retail media networks.
- 6. Shifting Promotional Funding Suppliers are shifting budgets from trade to digital for greater measurement and flexibility. 90 days 120 days 150 days Grocery Convenience Drug Before promotion During promotion E-commerce Promotion Start
- 7. Inflation Around the Globe Food, Energy and transportation led record inflation in all regions. • IMF projected inflation to reach 6.6 percent this year in advanced economies and 9.5 percent in emerging market and developing economies • Upward revisions of 0.9 and 0.8 percentage points respectively from three months earlier.
- 8. I came to NRF 2023 and saw the latest pie rates of the Caribbean. * Not a selfie
- 9. What Drove Priorities in 2022 Conflicts disrupted supply of raw materials to finished goods. Geopolitical Conflicts Inflation rost in all markets around the globe. Inflation Rail and ship based logistics continued to struggle with disruptions Shipping Disruptions Despite strong wage growth, retailers struggled to fill ranks. Labor Costs & Resource Availability
- 10. The State of Data + AI in Retail in 2022 ● Fine grained forecasting ● ML based personalization ● Location based targeting ● Real-time Supply Chain Visibility ● Price & promo optimization ● Labor scheduling ● New store location ● Data marketplaces Major Investments in 2022 Widespread Adoption ● Migration to real-time data processing ● Consolidation of all data in one data platform (images, video, structured, streaming) ● Unified Smart Forecasting Services ● Shift to demand sensing vs forecasting ● Revenue growth management ● Advanced customer segmentation ● Automated warehouses
- 11. Predictions for 2023 1. Last Mile Optimization
- 12. Last Mile Optimization: From “Real-time” to Right Time Balancing customer needs with resource availability to improve profitability • Consumers have demand faster options for delivery. • Retailers have been buying market share by subsidizing delivery. But… • Retailers often lose money on delivery from stores due to labor inefficiencies. Why? * Still not a selfie
- 13. Using Economic Drives to Balance Demand Unbalanced Balanced
- 14. Last Mile Pain Points 1 4 55% - Manual processes for planning/dispatching 61% - Lack of real-time visibility once delivery starts 46% - Scheduling Delivery Times 44% - Multiple fulfillment channels & tech 41% - Working with multiple 3rd parties 8% No Pain Points Biggest Pain Points when Scaling Delivery Models 24% - Travel Distance 36% - Real-time order visibility/tracking 23% - # of Drivers/Size of Fleet 10% - Routing 6% 2% No Pain Points Cost Biggest Pain Point in Delivering on Time
- 15. Last Mile Management Suppliers Distribution Retailers Consumers Delivery Consumer Insights Ad Programming Depletions/Demand Signals Replenishment Reordering Location Availability Traffic Jobs Orders Drive time arrival Product Pricing Inventory Order Status
- 16. Solution Accelerator Companies can now scale out hundreds of thousands of routes generated for single and multi-step journeys in advance of route optimization https://www.databricks.com/solutions/accelerators/scalable-route-generation Scalable Route Generation
- 17. Solution Accelerator Retailers can combine real-time data with analysis to consolidate orders and reducing picking costs. https://www.databricks.com/solutions/accelerators/order-picking-optimization The Buy-Online-Pick-Up-in-Store Retailing Model: Optimization Strategies for In-Store Picking and Packing, Order Consolidation & Picking
- 18. 1. Ensure data is available in real-time and integrated for when you run analysis. 2. Leverage machine learning algorithms to continuously look for ways to consolidate orders, optimize driving distance, and measure performance. 3. Incorporate performance feedback into your models. How to Prepare Steps to prepare your business for data led last mile optimization
- 19. Predictions for 2023 1. Last Mile Optimization 2. Generative AI for personalization
- 20. Prediction #2: Generative AI in Consumer Engagement Remember our friend? * Still not a selfie
- 21. Generative AI for Images Image generation quality is dependent on breadth and accuracy of training data. Thousands or millions Images are annotated to define features (color, width, style, shapes, face, pieces of clothing) Models are trained with images and the additional context. This enables computers to automatically recognize images. Additional training enables creation, with feedback when creation is accurate.
- 22. “Show Mac wearing a pink Burberry trench coat”
- 23. Cost to Perform Generative AI is Decreasing
- 24. Solution Accelerator Image based search and personalization Image Recipe Search
- 25. How to Prepare Steps to get started with AI Images 1. Capture all relevant images in the Lakehouse 2. Start labeling by using an automated labeling system such as Labelbox. 3. Generate immediate wins with image search, personalization. 4. Work long-term towards image generation.
- 26. Predictions for 2023 1. Last Mile Optimization 2. Generative AI for personalization 3. Composable CDPs
- 27. Prediction: Composable CDPs Adoption of Composable CDP brings best of breed with integration flexibility and scale. • Retailers are using this downturn to invest in consumer engagement to drive stronger marketing ROI and capture market share. • Composable CDPs are foundational to Retail Media Networks. Wat’s driving the change? • 75% of CDP customers expect 5x or But higher ROI, and most see a positive ROI in the first year of adoption. (Twilio) • • Two top-reported CDP benefits include a unified customer view (88%) and analytics (54%). (CDP Institute)
- 28. What’s Driving Customer Data Platforms • Drive stronger loyalty & lower CAC • Improve incrementality • Efficiency across multiple promotion channels • Desire to monetize customer engagement https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our- insights/commerce-media-the-new-force-transforming-advertising
- 30. Solution Accelerator Match millions of customers from multiple systems using AI. Customer Entity Resolution
- 31. Solution Accelerator Deliver far more accurate engagement with CDP + personalization. Over a dozen recommendation models
- 32. How to Prepare Steps to get start on your CDP 1. Focus on data source connectivity 2. Leverage machine learning for customer entity resolution 3. Use a Composable CDP approach to maximize scale, accuracy and flexibility.
- 33. Predictions for 2023 1. Last Mile Optimization 2. Generative AI for personalization 3. Composable CDPs 4. Peer-to-peer secure data collaboration
- 34. Prediction: Peer-to-Peer Secure Data Sharing Why is it attractive? • Improved collaboration around data can reduce response times by days. • Enables person to person collaboration, even across companies. • The value in data monetization is action, not licensing. But… • Data marketplaces only enable broadcast of common data sets. • Existing data sharing requires costly data warehouse licenses or forces companies to choose the same technology.
- 35. Data Sharing is No Longer Expensive or Exclusive • Delta Sharing is open source • Works across all clouds or on-premise. • Enables users to consume data from Excel, Tableau, web and other data systems. Retailer Partner Any use case Any tool Any cloud On-premises And many more Data science Reporting Analytics Access Permissions Real-time Data Sharing in Excel Love it or hate it, Excel is the most popular data tool used by end users. Exponam has built a plug-in for Excel that allows users to pull data in directly from Delta Sharing repositories to update their local analysis.
- 36. How to Prepare for Data Sharing Data sharing is no longer expensive or exclusive. Delta Sharing 1. Centralize your data in one location to manage permissions. 2. Leverage OSS Delta Sharing or Databricks (with Delta Sharing pre-configured). 3. Share secure links to partners.
- 37. Predictions for 2023 1. Last Mile Optimization 2. Generative AI for personalization 3. Composable CDPs 4. Peer-to-peer secure data collaboration 5. Localized Large Language Models
- 38. Prediction: Narrow Large Language Models Why is it attractive? • Taps into rich product, chat and call center transcripts to provide retailer specificity. • Cost to train models is rapidly falling. • Reduce customer service cost/time while improving quality. • Powers AI chat bots • Streamline new message creation for new purchases
- 39. Authoritative but not Accurate ChatGPT sounds confident, even when it’s wrong.
- 40. ChatGPT is still too costly • Some estimates that ChatGPT cost $14M to train. • Data used to train ChatGPT was over 1 year old.
- 41. Cost to Perform Training is Decreasing
- 42. Localized Industry LLMs are appearing • Startups are training LLMs against narrow sets of information • Provides much higher accuracy and less likelihood of false positives
- 43. How to Prepare Delta Sharing 1. Bring all structured and unstructured information into the Lakehouse, including call center audio, transcripts, online reviews, product information and more. 2. Monitor the environment for retail specific OEMs focusing on narrow LLMs. 3. Start small scale projects
- 44. The Future of Data + AI in Retail in 2023 ● Autonomous drone delivery Next 12 Months 2025 1-3 Years Widespread today ● Retail Media Networks ● Generative AI personalization (match the look, outfit on person) ● Localized LLMs ● Grab and go stores ● Automated replenishment to home ● Real-time data processing ● Data platform modernization ● Unified Smart Forecasting ● Revenue growth management ● Personalization ● Location based targeting ● Real-time Supply Chain Visibility ● Price & promo optimization ● Labor scheduling ● New store location ● Data marketplaces ● Composable CDPs ● Demand Sensing ● Last mile optimization ● Automated warehouses ● Drone delivery (pilots) ● Personalized Pricing ● Peer-to-peer data sharing Predicted date of widespread adoption
- 45. World’s largest Data + AI conference
- 46. Thank you Rob Saker VP Global Industry Leader, Retail and Manufacturing https://www.linkedin.com/in/robsaker/ https://twitter.com/robsaker
Hinweis der Redaktion
- Today we are here to talk about the Lakehouse for Retail, but before we jump into that, I would love to take a step back and talk about what is happening in the retail and consumer goods industries
- In one interesting study, they looked at the price increases in desserts across several tropical islands. In US dollars, they found that the average price of a slice of coconut cream pie in Jamaica had increased to $1.30. In Puerto Rico, that same slice was 1.92. And in the Bahamas, that same slice was over three dollars at $3.02. So when you go back home to your kids, you can tell them.
- What are the innovations that we expect to see over the next several years in retail? And what’s realistic today? While we think there are many new and unimagined innovations that AI will bring to retail, the reality is that AI is delivering incredible benefits to retail today. Fine grained personalization and forecasting refreshed frequently are delivering much higher accuracy for retailers today. This is leading to substantial cost savings and revenue growth. Retailers are keen to respond to COVID and know when to ship products, schedule staff and more. We’re seeing retailers leverage alternative data sets to predict foot traffic for the coming days, and optimize activities accordingly In the next 12 months, we expect to see the next wave of innovation move from piloting to widespread adoption. Possibly the most exciting trend is the adoption of unified forecasting services. Companies previously forecasted separately for commercial, supply chain and finance divisions, and then brought this together in a clunky format. They’re now using The biggest driver of investment in retail over the next 12 months will be in reducing the cost to serve e-commerce. Robotic curbside pickup, using AI to reduce returns, and even instituting new ways of handling returns will all start appearing as retailers seek to improve profitability in the digital channel. In 1-3 years, companies will advance and begin to introduce capabilities that redefine their business around AI. We’re seeing experimentation around this now, but these are the types of capabilities that require years of development. Apparel retailers will move from match the look, where you upload your favorite photo to an apparel site and it makes suggestions to you, to the ability to show what clothing will look like on your body. Using generative adversarial networks (similar to computer graphics in movies), web sites and apps will be able to take photos and videos of customers and render clothing on them. Grab and go stores will start to see adoption as 5G networks come into effect and reduce the cost of cabling. And we predict that we’ll see the first few drone delivery pilots launch in major cities. And by 2025, we should see innovations that fundamentally change the industry. Customers will treat retail as a subscription service, leaving much of their routine ordering to the retailers. Retailers will build smart algorithms that learn and anticipate needs, and automatically replenish items in a customers home. And by 2025, we expect to see drone delivery in widespread usage.
- https://www.aei.org/economics/chart-of-the-day-declining-ai-training-costs/
- https://www.aei.org/economics/chart-of-the-day-declining-ai-training-costs/
- What are the innovations that we expect to see over the next several years in retail? And what’s realistic today? While we think there are many new and unimagined innovations that AI will bring to retail, the reality is that AI is delivering incredible benefits to retail today. Fine grained personalization and forecasting refreshed frequently are delivering much higher accuracy for retailers today. This is leading to substantial cost savings and revenue growth. Retailers are keen to respond to COVID and know when to ship products, schedule staff and more. We’re seeing retailers leverage alternative data sets to predict foot traffic for the coming days, and optimize activities accordingly In the next 12 months, we expect to see the next wave of innovation move from piloting to widespread adoption. Possibly the most exciting trend is the adoption of unified forecasting services. Companies previously forecasted separately for commercial, supply chain and finance divisions, and then brought this together in a clunky format. They’re now using The biggest driver of investment in retail over the next 12 months will be in reducing the cost to serve e-commerce. Robotic curbside pickup, using AI to reduce returns, and even instituting new ways of handling returns will all start appearing as retailers seek to improve profitability in the digital channel. In 1-3 years, companies will advance and begin to introduce capabilities that redefine their business around AI. We’re seeing experimentation around this now, but these are the types of capabilities that require years of development. Apparel retailers will move from match the look, where you upload your favorite photo to an apparel site and it makes suggestions to you, to the ability to show what clothing will look like on your body. Using generative adversarial networks (similar to computer graphics in movies), web sites and apps will be able to take photos and videos of customers and render clothing on them. Grab and go stores will start to see adoption as 5G networks come into effect and reduce the cost of cabling. And we predict that we’ll see the first few drone delivery pilots launch in major cities. And by 2025, we should see innovations that fundamentally change the industry. Customers will treat retail as a subscription service, leaving much of their routine ordering to the retailers. Retailers will build smart algorithms that learn and anticipate needs, and automatically replenish items in a customers home. And by 2025, we expect to see drone delivery in widespread usage.