Name
Abstract | ||
|---|---|---|
In spite of their growing maturity, telecommunication operators lack complete client characterisation, essential to improve quality of service. Additionally, studies show that the cost to retain a client is lower than the cost associated to acquire new ones. Hence, understanding and predicting future client actions is a trend on the rise, crucial to improve the relationship between operator and client. In this paper, we focus in pay-as-you-go clients with uneven top-ups. We aim to determine to what extent we are able to predict the individual frequency and average value of monthly top-ups. To answer this question, we resort to a Portuguese mobile network operator data set with around 200 000 clients, and nine-month of client top-up events, to build client profiles. The proposed method adopts sliding window multiple linear regression and accuracy metrics to determine the best set of features and window size for the prediction of the individual top-up monthly frequency and monthly value. Results are very promising, showing that it is possible to estimate the upcoming individual target values with high accuracy. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86230-5_45 | PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021) |
Keywords | DocType | Volume |
Business intelligence, Business analytics, Data science, Linear regression, Sliding window, Telecom operator | Conference | 12981 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro Miguel Alves | 1 | 0 | 0.34 |
| Ricardo Ângelo Filipe | 2 | 0 | 0.34 |
| Benedita Malheiro | 3 | 0 | 0.34 |