Case Studies


Marketing Response Model Improvement using Behavioural AI 

Project partners: Nationwide Building Society 


Context: Nationwide Building Society is a British mutual financial institution, and the largest building society in the world with over 15 million members.  It offers a wide variety of financial products.

Challenge: Most Marketing Response Models do not take into account the personal characteristics of individual customers; these characteristics are critical for Personalisation, which is known to drive success. Such information could be collected (e.g. personality questionnaire) but places a burden on the user. In order to remove that burden, there is a need for automatic extraction of linguistic and psychological characteristics from users from text samples. Scaled Insights’ Behavioural AI tools can do this! 


Aims: To use natural language responses from a customer survey to predict responses to a selection of marketing campaigns so a predictive model can be built to drive Personalisation. 


Project so far: Scaled Insights’ Behavioural AI was able to extract a variety of text and user

characteristics from  anonymised language samples. The predictive models which used these characteristics significantly outperformed the non-text baseline model and doubled the correct identification of customers who responded positively to campaigns. This means that Scaled Insights’ segmentation model using linguistic and psychological characteristics derived by our Behavioural AI from customer panel text samples can increase the performance of Marketing Response Models.  



Personalising Nudges for Motivating Learners in Healthcare 

Project partners: Health Education England, University of Leeds 


Context: Health Education England (HEE) is the national organisation responsible for the education, training and workforce development in the health sector. HEE’s Technology Enhanced Learning (TEL) Programme uses the most effective evidence informed technology and techniques to benefit health and care education. Their portfolio includes the design and development of e-learning resources for health and care professionals and the public.


Challenge: Health and care professionals at every level need to continuously develop and update their skills. This includes digital upskilling of the workforce that is necessary to realise the potential of state-of-the-art technologies like genomics and artificial intelligence in clinical practice. There is a need for effective and scalable e-learning solutions which caters to busy health and care professionals.


Aims: To use Scaled Insights Behavioural AI technology to automatically infer user characteristics from a natural language sample collected by asking a person to answer some open ended questions. Then to use these characteristics to personalise motivational messages (‘nudges’) to increase e-learning take-up.  


Project so far: Scaled Insights has collaborated with University of Leeds academics to co-design two studies to investigate the feasibility of using Scaled Insight’s AI to infer user characteristics and to explore the relationships between these user characteristics and the effectiveness of different types of ‘nudges’ as motivational messages. To date we have we have established that the effectiveness of various types of ‘nudges’ differs between users with different characteristics that Scaled Insights’ AI has inferred. This means that Scaled Insights AI can be used for personalisation in the health education space.  

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