Predictive analytics move research or evaluation beyond merely measuring existing attitudes or behaviour to predict what changes can have the most impact on outcomes which you are trying to achieve.
Predictive analytics is the process of learning from current or historical data in order to make predictions about the future. For example, it can answer questions such as ”which factors in the workplace (e.g. flexible working arrangements, career development and training opportunities, salary level) are the greatest predictors of overall job satisfaction and intention to stay with current employer?” or ”which factors around a person’s health (e.g. age, gender, lifestyle, alcohol or cigarette consumption, exercise levels, co-morbidities) best predict how they will respond to a particular treatment?”
The predictive model can be constructed from data specifically gathered for the purpose (e.g. a new survey) or it may be possible to use historical data if this is available (e.g. from records which an organisation may keep). Qualitative data can also be useful in supplementing statistical insights which the model may provide.
Whatever the data used, the objective is to create a robust and reliable model which can predict what actions will have the most impact on the outcome variable you are interested in. For example you might find from modelling survey data that improving flexible working arrangements will have a much greater effect on overall job satisfaction than a 5% salary increase, or that a health treatment or intervention would be twice as successful if a particular lifestyle aspect (such as amount of exercise) could be changed.
Using advanced regression techniques, we will build a model for you to create powerful insights to inform decision-making, allowing resources to be targeted at those factors which can have the greatest impact on the outcomes of interest (e.g. retaining staff, improving overall job satisfaction or performance or improved success levels for a health treatment or intervention).