What’s for dinner tonight?


Help us answer a question asked by millions of Danes every day: “What’s for dinner tonight?”
Dive into the data jungle of Denmark’s largest grocery retailer and figure out how to provide the everyday Dane with intelligent, personal and healthy dinner recommendations.

Keywords: Food innovation, Big Data, Supermarkets, Dinner recommendations, Health, Decision processes.

Branche: Food Stores/Grocery Retailer, Dairy, Food Processing & Sales

Data set: Yes

Focus Area
Research shows that while Danes have time for food inspiration and elaborated meals in the weekends, they have trouble just choosing what to cook for dinner on weekdays. Many never decide what to eat before they enter a supermarket. Many give up and choose fast food.

The focus of this challenge is how to use data to crack the everyday decision making process happening in – or on the way to – the store, to ensure a home-cooked meal, or alternatively a semi- or fully prepared C-product* (and definitely not a pizza).

All the while presenting products and offers relevant to the recipe and to the user making the entire decision-making process easier.

*C are Coop’s convenience products, read more here: http://kvickly.dk/vores-varer/c-maaltidsmarked/

We know a lot about our member customers, from purchase history to where they live. We can spice this up with online behavior on recipe sites and search history. Maybe even social connects.

How do we link all this data to recipes, product data and offers to create a valuable end-user experience? And what external data sources can further strengthen the relevance of our suggestions?

How will this link be executed in-store?

The moment of truth in this challenge is in-store and only on weekdays. The decision depends on a multitude of factors, and this is what makes it so complex: what’s on sale, what do I have (or think/remember I have) in the fridge, what I usually cook, what would my spose/roommate/the children like, allergies and diet preferences, etc.

Our competitors are just as much the local pizza and sushi guys or 7eleven as it is other grocery chains – maybe more so. A big part of dinner inspiration will be to provide alternatives to the typical shortcuts we all take, when planning and cooking a meal ourselves just isn’t going to happen.

How can we use data to decrease complexity in the decisioin-making process and make it faster, easier, funnier and/or more inspiring?

Coop data:

  • Purchase history (household)
  • Recipe data
  • C-products data
  • Product data
  • Shop API (store locations and opening hours)
  • Weekly offers data
  • Member data
  • Social connects
  • Online behavior
  • Search history
  • Mad-O-Meter

Household or member segmentation.
All sensitive data will be anonymized.

External data (be creative, find more):
Weather data, forecasts, temperatures (upcoming changes in weather, shopping patterns)