Providing analytics-driven insight does not guarantee that people will take note of it and change their decision-making behaviours. For example, did a customer identified as a churn risk respond to the intervention recommended; or did loan officers in a bank actually base their lending decisions on the output of risk models? Measuring the success of any analytics-driven project, therefore, requires looking beyond the insights that are produced to ways in which these insights are communicated and delivered to ensure that behavioural change takes place. This research will benefit any company that wishes to make sure that the application of analytics results produces the required or expected outcome. For example, did a customer identified by a supermarket as a user of baby products take advantage of a promotion on nappies that was on offer that week? If not, can a message be tailored for that customer to encourage them to take advantage of such a promotion the next time? Using tailored messages, our research is working on encouraging the greatest level of change in a customer/user’s behaviour .

NudgeAlongs works via an integrated set of mechanisms includingl - User Monitoring, which would come from a company’s own customer data, and would include details on a customer’s interaction with the company’s systems, etc. - A Personalised Incentive Design, which would require finding an incentive by which a customer may change their behaviour. There are a range of approaches that can be taken e.g. including details on money-saving methods, running promotions, etc. - Personalised Communication Strategy. The communication strategy should take into account the medium used (e.g. text message, email, etc.), the content of the message itself, the communication frequency (how often recommendations are made to customers) and the communication variety (should the messages change from one communication to the next). - Recommendation Engine that produces personalised communications, e.g. text or email messages, by taking in account customer usage profiles, relevant external data sources (e.g. typical weather conditions, etc), any past communications that customer has previously had with the system and the outcome of these prior communications.

This research will benefit any company which wishes to make sure that their communications with customers produce the required or expected outcome. For example, the ability to change user behaviour is of interest to any company that wishes to see the effects of a marketing campaign, or the effects of an attempt to get staff to save energy in their offices by switching off lights, etc. As a single example, studies have shown that by changing user’s behaviour electricity use can be reduced by 2%. Such a reduction of energy use at peak demand time would be of great interest to utility companies who could save money by flattening the peak demand profile (that is, reducing the need to occasionally bring on-line less energy efficient oil-powered power stations, etc.).