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Our experience has identified three-core customer needs that our engagement process seeks to address. Customers who come to us typically seek:

  • To achieve a quick advantage or address an immediate shortcoming based on analysis of their historical data; this typically leads to longer term investment once the immediate needs are addressed.
  • To achieve long term data-driven guidance that can support strategic investments and decisions in future; this typically will require some transformation of the company’s technology and processes to collecting and using data
  • To support independent in-house data-driven decision systems without relying on the need for external data experts; this is typically achieved through the implementation of customized software solutions  

A sample of our work in various industries

Industry Business problem Method used Result
Personal vehicle insuranceA top 5 insurance company wishes to improve the profitability of its vehicle insurance product while offering dynamic pricing to its current & new policy holdersUsed past and present customer features and claims data to define clusters using a Machine Learning approach.Under implementation
Financial ServicesA large regional bank wanted to increase the purchase probability of three advertised financial products: loans, mortgages and credit cards.
The team used a combination of Naive Bayes and Logistic Regression techniques to calculate propensity scores and estimate the prediction accuracy.
Under evaluation. Expected success rate increase of 25% compared to previous method used.
Electrical Power distributionThe national electricity company aimed at reducing energy losses along the power generation, distribution and transmission network.Data collected from power meters was used to instruct Deep Neural Networks.Under evaluation
Product DistributionA regional distribution company wanted a better planning method for routing of trucks for delivery of non-perishable goods.Predictive analytics and clustering analytics was used to determine best time/location sequence.The total average percentage reduction in travel distance was 59% and the total average percentage frequency reduction in visits was 15%.
Electrical Power distributionThe national electricity company wanted to accurately predict short term load.Deep Neural Networks was used to determine load forecasting.Under evaluation
Import/ExportA regional car sales company wanted to optimize vehicle import to ensure high demand SKUs are available and low moving SKUs are kept at a minimumExponential Smoothing was used to assist Inventory Forecasting.Decrease in the average time spent on lot by 55 days whilst maintaining the probability of a vehicle existing on the lot at one.
Personal InsuranceA large regional insurance company wanted to increase the product attach rate for their existing clients.Created a Recommender System using past product sales and client features to automatically determine most suitable product.Under evaluation
DIV Solutions

Analytics for Business

Data - Insight - Value