WHAT WE DO
Our Data Science practice helps our clients extract value from their data assets and help guide them in delivering effective data products.
Through in-depth analysis of your data we uncover hidden insights to help you understand your customers and business better, enable improved strategic decision making and identify new opportunities to enhance business value.
We can help you develop or enhance customer segmentation models, designing and optimising effective targeted marketing campaigns and measuring the ROI on these campaigns.
Our predictive analytics suite helps you predict customer churn/lapses, dormancy, take-up rates, loan prepayments and credit defaults. Predictive analytics can also be used to develop fraud and anti-money laundering transaction screening models and to improve credit-risk underwriting.
By modelling trends in workforce demographics, Lux helps you identify key factors that contribute towards employee and business performance. We also use analytics to estimate employee lifetime value leading to retention of valuable talent as well as analysis of employee attrition.
Loyalty and rewards programmes (if well designed) generate a significant amount of very valuable data on your customers. We help you analyse and extract value from this data and identify opportunities for further value creation.
The effectiveness of any analytics solution depends to a large extent on the availability of data. We help you design an effective data strategy to ensure that the correct data is being collected in the right way to enable you to maximise the potential of your data analytics efforts.
We provide you with training to improve data literacy and up-skill your employees in advanced analytical skills.
Lux Actuaries has developed the Lux Conflict, War and Strife (CWS) Predictive Model which has been designed to assess the risk of future armed conflicts globally, considering a multitude of indicators that cover all areas of development, governance and overall socio-economic strength of countries across time. This blog post discusses feature importance analysis - a key strength of the Lux CWS model - which helps to elucidate some of the black-box workings typical of most machine learning models.
East Africa and Kenyajoseph.email@example.com
North Africa - Egyptahmed.firstname.lastname@example.org
North Africa - Francophonemohammed.email@example.com
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