Laura Sáez (UB) — Pilot test of quantum algorithms for market segmentation and consumer behaviour prediction

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💡⚛️Can quantum computing help businesses find the right customers today?⚛️💡

Marketing teams work with massive volumes of customer data, but predicting who will respond to a campaign is still a hard challenge. Classical machine learning methods are hitting their limits and are not powerful enough to capture the full complexity of real consumer behaviour. 

The team led by Laura Sáez, Santiago Forgas Coll and Massimiliano Ferrara tackled this challenge by performing a feasibility study of a different approach: running quantum machine learning algorithms on MareNostrum-ONA.

🔎𝐖𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐟𝐨𝐮𝐧𝐝?🔎 

🔵 The quantum model had a recall of 86%, which means that the model identified 86% of the relevant customers in the sample. That is up to 18% more than classical equivalents.
🔵 The model's AUC was 83%, which represents the general accuracy of the model separating relevant customers from non-relevant customers. That makes it particularly suited for campaigns focused on maximising reach.
🔵 The circuits used are shallow and compatible with today's quantum hardware, not just a potential promise. This is the first published article using Quantum resources via RES: https://lnkd.in/eD27K7iB

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