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Tastes, Trends, Touch Points - Understanding Shoppers Through Machine Learning
The document discusses how machine learning and sensors can provide insights to retailers by analyzing customer shopping journeys and behaviors. It describes how sensors in stores, online, and in the supply chain can track inventory, sales, and consumer preferences. The presentation then shows examples of how a retailer could use sensor data and machine learning to improve operations, pricing, promotions, and assortment planning.
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Tastes, Trends, Touch Points - Understanding Shoppers Through Machine Learning
- 1. Tastes, Trends, TouchPoints - Understanding Shoppers Through Machine Learning ShiSh Shridhar (@5h15h) Director, Retail Analytics Microsoft Corp Su Doyle (@sudoyle) RFID Applications Director Checkpoint Systems
- 3. Listening to thevoice of the customer 0 5 10 15 20 25 30 1 2 3 4 5 6 7 8 Sales Forecast in 1999 Series1 Series2 0 5 10 15 20 1 2 3 4 5 6 7 8 What Really Happened Series1 Series2
- 4. Retail has amultitude of devices that generate petabytes of potential insights Monitoring and mining social media data enables retailers to enhance customer insights Open data sources and internal sources enable retailers to better understand customers Democratization of data
- 5. The Opportunity + SENSORS AREPROLIFERATING EXPECTATIONS ARE INCREASING THE RETAILER WITH ACTIONABLE DATA WINS! • In the Supply Chain • In Stores • For Consumer Apps • Shoppers expect more convenience and selection than ever • Faster Omni-Channel Fulfillment • Better Assortment Planning • Increased On-Shelf Availability • Browse vs. Buy Insights • Faster Design to Delivery Store Pickup
- 6. Sensor Evolution inRetail + Manufacturing Retail StoresDC Supply Chain & Shopper Journey: PROCESS AUTOMATION: RESPONSIVE RETAIL: 1 3 4 2 Mobile & Online Social Media Search S U P P L Y C H A I N J O U R N E Y S H O P P E R J O U R N E Y !Sensors Process Information Alert ! !! !
- 7. Sensors & TheShopper Journey + 1 2 3 4 5 6 7 8:00AM Friend shares photo w/ link on Social Media “…Jenny, I think these are ur shoes 4 the big event!” 9:15AM Jenny searches a local store online to see if they have the shoes in her size – they do! Jenny Reserves the Shoes to Try On In Store after 4pm that Day. • RFID-Enabled Inventory Mgmt. Search in Store 10:45AM Store Associate Receives Jenny’s Request, Locates Shoes and Sets them Aside. The shoes are a new style, so the store associate receives an alert on her phone to put a pair on display. • RFID Pick – Pack –Reserve • RFID Display Compliance 5:20PM Jenny Checks In at the Store with her Loyalty App. Her favorite jeans are on sale -- and her size is in stock! • Beacons integrated with Loyalty Apps • RFID-Enabled Inventory Mgmt. Recommendations Jenny tries on the jeans with the new shoes and gets recommendations on other items (in stock at the store) to go with the outfit. • RFID Magic Mirror • RFID-Enabled Loyalty Apps • RFID-Enabled Inventory Mgmt. Jenny purchases the shoes, jeans, a new top and a bag to go with the outfit. RFID at the point of sale – mobile or checkout counter makes processing the sale much faster and automatically decrements inventory and connects Jenny’s purchase to her browsing & buying behavior. • RFID-Enabled POS The retailer is able to connect the dots between what Jenny browsed and purchased across channels and what sorts of promotional offers she responded to. Building personas like Jenny helps retailers predict which products, services and merchandise locations shoppers are most likely to respond to. • Sensor-based Analytics • Machine Learning 40% Off
- 8. Sensor Business Cases SUPPLYCHAIN EFFICIENCY DEMAND-DRIVEN SUPPLY NETWORK SHELF AVAILABILITY RFID, RTLS, GPS, PLCs Process Automation & Exception Handling Dynamic Assortment Planning Distributed Order Management Omni-Channel Fulfillment Demand-driven Replenishment & Assortment Planning Productivity Tools for Sales Associates STORE OPERATIONS & CUSTOMER-FACING LOGISTICS Omni-Channel Fulfillment Automated Pick / Pack & Ship from Store Task Management Store-to-Store Transfers SHOPPER INSIGHTS Platform for responsive retail, continuous improvement Browse v. Buy Heat Maps Platform for Predictive Analysis ! #105 Order Fulfillment Time & Accuracy by Store #101 #251 #479 91% 84% 99%79% Shipment A7849 is Incorrect! Route to Rework Area How quickly and accurately can we fulfill store orders & respond to demand? How do we ensure replenishment products are in stock and on the shelf? How do we leverage the world of sensors, customer-facing & in the supply chain? How can we automate customer facing logistics?? +
- 9. Sensors Informing anOperations Dashboard +
- 10. Imagine if… Imagine aseamless, personalized experience for your customers, in stores and online. Imagine understanding your customer’s needs and supplying the right products at the right time. Imagine sales associates spending more time with customers, providing personalized assistance and incentives, and increasing sales. Imagine anticipating demand and effectively scheduling staff. Imagine optimizing operations, reducing waste, and enabling your employees to make better decisions. 10
- 11. What product or serviceto recommend to Joe ?
- 12. Will Sarah come backwith us next month?
- 13. What targeted incentiveto retain your customers?
- 16.
- 17. 17 Sales for Northwest Territoryretailer Problem: Seattle is not selling to forecast 1 2
- 18. 18 When we drill downto Seattle, we can see a problem in soft drinks Click and see further details of Seattle sales 1 2
- 19. 19 Sales driver analysis –build a model that explains what drives sales Sales delta analysis – use the model to see problems 3. How can we fix sales? – apply the model to fix the problems 21 3
- 20. 20 25.6% variations explained Internal transaction andmarketing data include variables as: - Stock Up - Price Elasticity - Radio Advertising - TV Advertising - SKU presence Transaction dataset in AML experiment 1 2 3
- 21. 21 Variations explained improves to near50% External weather, demographic, and competitor data include variables as: - Temperature - Precipitation - Household size - Annual Income - Competitor Price Gap Transaction dataset in AML experiment External dataset enters the model in AML experiment 2 1 3 4
- 22. 22 IoT dataset enters themodel in AML experiment Variations explained improves to 89% New IoT, research and online activity data include variables as: - Survey research - Web traffic - Social media traffic - Mobile traffic - Store traffic - Shelf traffic Transaction dataset in AML experiment External dataset enters the model in AML experiment 2 4 1 3 5
- 23. 23 Monthly ∆ bysales driver Let’s first zero in on the sales impact of price gaps, as they are the biggest problem Competitor price gap caused 7,598 less units sold than previous month Click one of the controllable variables to see what would happen if we take some actions 21 3 4
- 24. 24 See the impact onphysical sales if we reduce the price gap by different levels See the impact on profit if we reduce the price gap by different levels. When it is reduced by 15%, we would be able to achieve 4.5K incremental profit. Select competitor price gap as it is a controllable variable It would be recommended to decrease the competitor price gap 2 1 3 4
- 25. 25 See the impacton physical sales if we increase social media engagement by different levels See the impact on profit if we increase social media engagement by different levels. When it is increased by 20%, we would be able to achieve 12.6K incremental profit. Select Social Media Engagement as it is a controllable variable It would be recommended to increase social media engagement 2 1 3 4
- 26. 26 See the impacton physical sales if we increase advertising by different levels See the impact on profit if we increase advertising by different levels. When it is increased by 20%, we would be able to achieve 7.1K incremental profit. Select Own Brand Advertising as it is a controllable variable It would be recommended to increase our own brand advertising 2 1 3 4
- 27. 27 We can usethe SKU sales lift to see where we have ”dog” SKUs that decrease overall sales, and were we have high potential “hidden stars” to replace them with “Dog” SKUs with negative sales lifts can be replaced as they decrease total sales because of cannibalization “Hidden Star” SKUs with high sales lift but currently have low distribution Each dot represents a different SKU. The Y axis placement of each dot indicates the physical volume increased by each SKU in the store-weeks where it is present2 1 3 4
- 28. 28 Click PRD013 as ithas the lowest sales lift We could achieve the highest incremental physical sales if replace PRD013 with PRD014 See more details about PRD013 See what the recommended replacements are 2 1 3 4
- 29. 29 Click PRD016 as ithas the second lowest sales lift We could achieve the highest incremental physical sales if replace PRD016 with PRD018 See more details about PRD016 See what the recommended replacements are 2 1 3 4
- 30. 30 Click PRD015 as itis another “dog” SKU We could achieve the highest incremental physical sales if replace PRD015 with PRD021 See more details about PRD015 See what the recommended replacements are 2 1 3 4
- 31. 31 See the overallimpact on physical sales if we take the recommended actions The sales will continue sliding down if no actions are taken 1 3 Within the budget constraints, select the recommended actions 2
- 32. How Can YouMake This Real Learn Machine Learning http://bit.ly/1OwTYO6 Experiment with Machine Learning http://bit.ly/236rODf What are some of the questions you wish you could answer about your operations/customer ?