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EDP: AI‑driven personalised energy recommendations

4 June 2026
    AI models personalise next‑best actions and offers to score customer energy data

    Context

    Customers increasingly expect personalised recommendations that go beyond products, enabling more efficient energy use through data‑driven insights.

    Challenge

    Delivering relevant, tailored energy‑saving recommendations requires combining customer data and usage behaviour with predictive models and decision rules, while ensuring the recommendations remain actionable and useful for customers.

    AI Solution

    Moonshot is an integrated NBAO (Next Best Action / Offer) and Moonshot programme that provides personalized recommendations to customers. It scores the relevance of each recommendation per customer using predictive models and prescriptive business rules, leveraging customer data and behavioural insights within the EDP App. It includes collaborative filtering models learning from similar customer profiles, with a continuous learning loop based on customer interactions and feedback.

    Impact & evidence

    The solution enables customers to achieve energy savings while driving efficiency improvements and reinforcing a strong commitment to sustainability.

    Implementation / Adoption

    Replication requires customer profiling data (with consent), predictive AI with business‑rule layers, robust data engineering, digital channel integration (e.g., the app), and strong product/data‑science capabilities.

    Implementation timeline includes the development of predictive models, business‑rules layer and data pipelines.

    The deployment timeline would include integration with the digital channel, and controlled rollout to profiled customers with value realisation progressing across waves 1 and 2 as adoption and recommendation relevance increased.

    Data

    The solution requires customer profiling data (with consent), energy consumption and usage data, household characteristics data collected via the App, behavioural interaction data from the digital channel, and historical product and recommendation performance data.

    Future enhancements

    The planned future enhancements include expansion of recommendation catalogue and use‑case coverage and improved personalisation through richer household and behavioural data.

    Additional information:

    https://eco.sapo.pt/2026/01/13/edp-lanca-app-que-ajuda-a-gerir-melhor-o-consumo-de-eletricidade

    https://www.edp.pt/particulares/apoio-cliente/perguntas-frequentes/pt/area-de-cliente-edp/area-de-cliente-edp/quem-e-o-eddy-e-qual-o-seu-papel-na-jornada-de-eficiencia/faq-35118

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