“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.

1. Carrefour-Google Lab
Forecasting affluence in stores during lockdown
Chiara Biscaro
Senior Data Scientist

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. From stars to…. stores
Aug 2016: S2DS
bootcamp, London
Nov 2016-2019:
Software Engineer
and Data Scientist at
Criteo, Paris (FR)

4. Welcome to the Carrefour-Google AI Lab!

5. 2020 brought us an uninvited guest...
Credit: Leo Ortolani
Source

6. … with an international impact on the way people shop
Lockdown Italy
Lockdown France

7. … which created problems for our customers
Patrick Zagar Garnier
Chiara Biscaro
Anh Nguyen Van

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. 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. Different days of the week are now very similar to each other...

11. The life cycle of a (ML?) model
What if we
don’t use ML
here?

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. In production in one week, what it looks like

15. Monitor performance - Forecast accuracy per hour across time
70%

16. Let’s try some ML - Prophet
Forecast accuracy per day - Average vs Prophet

17. Let’s try some ML - Prophet
Forecast accuracy per hour for 15th April - Average vs Prophet

18. 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%

19. 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

20. Credit: XKCD
xkcd.com/1838/
Thanks!