Variations in household behaviour often lead to a mismatch between actual and estimated energy performance at home. More detailed information on behavioural variables could help in improving the prediction of energy consumption and enabling policy interventions responding to different household groups. This research aims to identify household archetypes and behavioural patterns in order to allow a targeted approach in energy-saving policy and retrofit improvement. It employed a statistical approach to cluster households based on empirical data collected from a household survey in Cambridge, UK. Factor analysis was used to identify behavioural factors. Based on the commonalities of variables under each factor, five factors were defined: (1) main space heating, (2) auxiliary space use, (3) main space use, (4) auxiliary space heating and (5) use of appliances. Statistical pattern analysis was then applied to develop behavioural patterns. These patterns were derived based on their factor scores. Finally, non-parametric correlation analysis was carried out in order to determine the relationship between behavioural factors and the following: household or dwelling characteristics, comfort and energy use for creating household archetypes. After significant correlations were found between behavioural factors and other variables, five archetypes were identified: (1) active spenders, (2) conscious occupiers, (3) average users, (4) conservers and (5) inactive users. Among these archetypes, households with a larger house, higher energy use and more complex household composition tended to have longer hours of main space heating, while larger and more complex households tended to use the main space of their dwellings for longer. Using these archetypes allows for a better integration of occupant behaviour into the technically oriented efficiency paradigm. This tailored approach provides a gateway to developing more effective policies and low energy strategies geared towards specific households.