Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations

TL;DR

This study leverages monthly demand profiles, activity sectors, and locations to indirectly identify electricity customers, enabling targeted marketing strategies that significantly improve customer acquisition efficiency.

Contribution

It introduces a novel approach combining demand profiles with activity and location data for customer identification in the electricity sector.

Findings

Identified 100,000 customers uniquely using demand profiles and metadata.

Reduced distance from target profiles by 40% with the proposed marketing strategy.

Demonstrated the effectiveness of indirect customer identification for targeted marketing.

Abstract

The increasing competition in the electric sector is challenging retail companies as they must assign its commercial efforts to attract the most profitable customers. Those are whose energy demand best fit certain target profiles, which usually depend on generation or cost policies. But, even when the demand profile is available, it is in an anonymous way, preventing its association to a particular client. In this paper, we explore a large dataset containing several millions of monthly demand profiles in Spain and use the available information about the associated economic sector and location for an indirect identification of the customers. The distance of the demand profile from the target is used to define a key performance indicator (KPI) which is used as the main driver of the proposed marketing strategy. The combined use of activity and location has been revealed as a powerful tool for indirect identification of customers, as 100,000 customers are uniquely identified, while about 300,000 clients are identifiable in small sets containing 10 or less consumers. To assess the proposed marketing strategy, it has been compared to the random attraction of new clients, showing a reduction of distance from the target of 40% for 10,000 new customers.