This study focussed on a specific target group: customers who have been on a default tariff for more than 3 years. Default tariffs are energy tariffs customers are switched to at the end of their contract with an energy supplier that are often not always the most competitive on the market. The aim was to identify attributes of long-term disengaged customers that could be used to predict long-term disengagement.
The dataset contained over 600,000 customers on default energy tariffs from five suppliers which were anonymised to Nala, Mufasa, Timon, Pumba, Simba. The dataset also contained a number of attributes relating to customers’ payment and consumption behaviour.
Using the open-source data science tool AutoML from h2o and the data platform built by data services in Ofgem, these data were pre-processed and run through a series of supervised machine-learning models. Supervised machine-learning models attempt to learn a mapping function (a formula) from input data to output data. In this case study these input data were features about a customer and these output data were whether they are long-term disengaged (on a default tariff for greater than 3 years) or not (on a default tariff for less than three years). The approach used here not only optimises each type of model in its ability to predict these output data, it compares each individual model to combinations of models leveraging new data science methods and technology to determine the most representative model of that function.