Receipt2Nutrition

Motivation



Our research project “Receipt2Nutrition” will be the world’s first large-scale voluntary panel that contributes digital receipt data from loyalty cards anonymously to assess the data’s potential to predict dietary behaviour automatically.


Description


The Receipt2Nutrition research project aims to develop a novel and scalable approach for delivering individuallevel dietary monitoring by applying data science on automatically captured digital receipts. Although early-stage research promise potential to monitor dietary behavior from assessing purchasing data records (Brinkerhoff et al. 2011; Chidambaram et al. 2013; Coll 2013; Eyles et al. 2010; Illner et al. 2012), to this date, there has not yet been a large-scale study on automated, individuallevel dietary monitoring or interventions using digital receipts. Until today, different barriers for such an automated assessment have been missing product composition data, or lack of data interfaces from retailers’ loyalty card systems.


Driven by the recent introduction of online food composition declaration (EU-1169/2011 2014), curated food composition databases containing detailed nutritional information on products sold in a retail environment are now available. This information becomes particularly useful when combined with a consumer’s shopping history. By using a loyalty card at checkout, a consumer automaticaly extends her electronic purchasing history including dates, locations, and individual items purchased in elec-tronic and machine-readable form. A total of 80% of Swiss retailers’ revenue is covered by loyalty cards (Accarda 2005; Handelszeitung.ch 2004), thus providing a widely adopted basis for our research model. Thanks to the also recently introduced General Data Privacy Regulation (GDPR), users of such a loyalty card system can now request their own data from data processers (such as loyalty card system providers) and decide if they want to share it with research institutions or diet applications.


Due to the advances in machine learning (Frey et al. 2015; Pentland 2008), the opportunity to leverage automatically generated digital receipts as a proxy for dietary behaviour becomes graspable and could increase transparency between retailers, food producers and customers alike, and last but not least lead to scalable dietary monitoring, potentially mitigating the spread of dietary-related diseases.


If you are interested in working together with us on a similar application, please feel free to contact us: Klaus Fuchs

Links

Stakeholders

Retailers, manufacturers, customers, app developers, data aggregators

Keywords

Digital receipts, data science, deep learning, nutrition

Working group