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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.04776 |
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| _version_ | 1866912749171245056 |
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| author | Luque, Joaquin Carrasco, Alejandro Personal, Enrique Perez, Francisco Leon, Carlos |
| author_facet | Luque, Joaquin Carrasco, Alejandro Personal, Enrique Perez, Francisco Leon, Carlos |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04776 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations Luque, Joaquin Carrasco, Alejandro Personal, Enrique Perez, Francisco Leon, Carlos Computational Engineering, Finance, and Science 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. |
| title | Customer Identification for Electricity Retailers Based on Monthly Demand Profiles by Activity Sectors and Locations |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2512.04776 |