ICA – How to Personalise Offers and Recipe Recommendations?

Transformation from Rule-based to Model-based

There was no AI in any of the existing systems, since they were all rule-based. Avaus was brought on board to first migrate the existing rule-based systems to a new environment, and subsequently, replace the rules with AI to boost efficiency.

ICA chose Avaus over off-the-shelf solutions, because ICA wanted to tailor their solution more than what would have been possible with those solutions.

Eventually, a number of machine learning models were designed and implemented to predict user behaviour and preferences. They could be leveraged into different offer types to extend and improve the current offer type portfolio into an ensemble of models tailored for each customer. 

Since 2018, over the course of a few years, the RecEngine’ team has provided undeniable business results. Embodying the Data x Algo x Action framework, several models were connected straight to the systems sending communication to customers. 

Education and Cross-competence Collaboration is Key

A large part of Avaus’ mission has been to educate the organisation on how data science can be incorporated in marketing. Avaus has focused on ICA’s long-term viability and self-sustainability, by constantly teaching individuals who interact with the RecEngine team to promote data literacy across the organisation. On various occasions, the organisation’s marketing knowledge was incorporated into AI algorithms to improve their performance.

“It is beneficial to involve as many people from the organisation as possible in AI projects. Because employees are the ones that know the most about their organisation’s products and customers, including their knowledge into the models yields the greatest outcomes,” Eric Hörberg mentions.

On the Fast Track to Results

ICA went from no AI models into a 10+ people team of Data Scientists and Data Engineers. “A leading star for how an Analytics Team should be run,” describes Martin Lennartsson, former Delivery Lead at ICA.

The outcome was predictive modelling and a recommendation engine, which allowed ICA to provide a more personalised user experience for its customers. ICA has established a significant lead in personalisation capabilities, and has shown that personalised marketing at the household level is a highly effective approach to both in the short and long term:

• Improve Sales
• Increase Loyalty 
• Grow Profit 

This proves that the models did not only create “coupon hunters”, they increased customers’ overall spend.

With several AI models, tweaked in even more A/B tests, their aggregate earnings were estimated to be a large increase in yearly sales across all channels, where the models are employed. A different, independent team at ICA calculated the later results and their statistical significance. Sales increased so much that the effect of the model could be statistically proven.