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In production in one week: forecasting affluence in supermarkets during lockdown by Chiara Biscaro, Senior Data Scientist @ Carrefour-Google AI Lab

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“In production in one week: forecasting affluence in supermarkets during lockdown” by Chiara Biscaro, Senior Data Scientist @ Carrefour-Google AI Lab

Abstract: The Coronavirus and the lockdown that have been implemented in many countries have drastically changed people's shopping habits in a matter of hours or days. Restrictions on movements and limitations on gatherings have created a challenge for our supermarkets, that quickly needed to know whose hours would attract the most customers. I'll share how we quickly designed and put in production an affluence prediction with ~80% forecast accuracy in a matter of days, and its limitations.

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In production in one week: forecasting affluence in supermarkets during lockdown by Chiara Biscaro, Senior Data Scientist @ Carrefour-Google AI Lab

  1. 1. Carrefour-Google Lab Forecasting affluence in stores during lockdown Chiara Biscaro Senior Data Scientist
  2. 2. From stars to…. stores 2010-2014: PhD in Theoretical Physics at University of Basel (CH) 2015-2016: Postdoc fellowship at Dark Cosmology Centre, Copenhagen (DK)
  3. 3. From stars to…. stores Aug 2016: S2DS bootcamp, London Nov 2016-2019: Software Engineer and Data Scientist at Criteo, Paris (FR)
  4. 4. Welcome to the Carrefour-Google AI Lab!
  5. 5. 2020 brought us an uninvited guest... Credit: Leo Ortolani Source
  6. 6. … with an international impact on the way people shop Lockdown Italy Lockdown France
  7. 7. … which created problems for our customers Patrick Zagar Garnier Chiara Biscaro Anh Nguyen Van
  8. 8. Problem description: groceries during a lockdown ● To avoid queues (or at least manage them), we need our supermarkets to know when the affluence is going to be high ● Two predictions are useful: a. Number of clients per day of week b. Affluence (low/medium/high) for each hour of the day (8-20) ● Every Monday morning the store directors receive a communication mail. Predictions for the entire week need to be sent in order to communicate in a timely manner Patrick Zagar Garnier Chiara Biscaro Anh Nguyen Van
  9. 9. Usual approach Time series problem -> EASY! -> ARIMA/Prophet/XGBoost/RNNs…. … some caveats... ● Lack of data: at this point (beginning of April) we have ~3 weeks of data!! ● Lack of time: we need the model to be ready to send predictions on the field ASAP.
  10. 10. Different days of the week are now very similar to each other...
  11. 11. The life cycle of a (ML?) model What if we don’t use ML here?
  12. 12. An “average” validation Three models are considered: 1. Arithmetic average per hour/store 2. Arithmetic average per hour/day/store 3. Arithmetic average per hour/store with a double weight on the day considered With the following setup ● train period : 19 days (2020-03-19 to 2020-04-09) ● validation period: 4 days (2020-04-10 to 2020-04-15, excluding Easter Monday) Forecast accuracy (FA) =
  13. 13. In production in one week, what it looks like
  14. 14. Monitor performance - Forecast accuracy per hour across time 70%
  15. 15. Let’s try some ML - Prophet Forecast accuracy per day - Average vs Prophet
  16. 16. Let’s try some ML - Prophet Forecast accuracy per hour for 15th April - Average vs Prophet
  17. 17. Limitations and next steps ● The model is too simple for the post-lockdown phase ● We are now working with Proximity stores to create a more powerful model for prediction of affluence in post-lockdown conditions 70%
  18. 18. Conclusions ● Take a LOT of time talking to business people about what they need and on which time scale ● Don’t over complicate simple requests with the risk of not delivering ● You need to know SOTA ML in order to know when NOT TO USE it
  19. 19. Credit: XKCD xkcd.com/1838/ Thanks!

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