Data Science & Analytics
We deliver intelligent solutions, harnessing the latest innovations in predictive modelling, machine learning, and artificial intelligence. Our team has extensive experience in the practical application of data science solutions to solve any business problem.
Examples of Our Work with Clients
Institute of Golf
AI Driven Golf Player Performance Optimisation
Challenge – Build a sports performance calculation engine to power Circles - the Institute of Golf’s new player intelligence software.
Solution – Our data science team developed bespoke machine learning models trained on ten years of player performance data (over 5.8 million shots). These models leveraged a range of modelling methodologies including Random Forest, Gradient Boosted Machines, Neural Networks and Autoregressive forecasting.
Outcomes - The models accurately determine characteristics of player performance and now benchmarks, predicts and provides personalised coaching recommendations in real time to golf players right around the world at www.drawmorecircles.com
Real Estate Institute of NZ
Real Estate Automated Valuation Model (AVM)
Challenge – REINZ required an advanced computer-generated property valuation model to provide fast, accurate and automated property price guides for buyers, sellers and agents in the New Zealand market.
Solution – Our team of data scientists utilised state-of-the-art machine learning paradigms to develop a novel method of estimating the value of all residential properties. From urban apartments to rural lifestyle blocks, the model takes a range of market, location, sale and property data as inputs to produce up-to-date accurate estimates of a property’s current value.
Outcomes - The model provides accurate results, far outperforming existing models in the market and is on par with cutting-edge literature in the field. The AVM now powers REINZ’s property price estimates and is publicly available at www.realestate.co.nz.
Westpac New Zealand
Workforce Optimisation Forecast Model
Challenge – Westpac needed an automated solution to improve its ability to manage branch network resourcing requirements.
Solution – We developed a machine learning model trained on three years of hourly transaction and branch demand patterns using two modelling algorithms (decomposition and temporal pattern isolation). The model is fully automated and self-evaluating, ingesting new data and re-training itself monthly.
Outcomes – The model provides an accurate and robust two-month forecast of expected branch transaction volumes and FTE requirements at an hourly level. This has allowed Westpac to save time, optimise rostering and scheduling decisions and provide a much better customer experience across its retail branch network.
Our bespoke data science solutions can optimse outcomes for almost any business problem including:
Customer churn and retention.
Sales, leads and customer conversion.
Optimal staff, location, and resource allocation.
Targeted next best conversation, product upsell and cross sell.
Pricing elasticity, price performance and margin optimisation
Network distribution and logistics optimisation.
Customer segmentation and growth.
Customer lifetime value.
Risk minimisation and fraud reduction.
Credit risk scoring, expected loss, capital and portfolio stress testing
Digital engagement, A/B testing and digital recommendation engines.
Business, Industry, Market and Economic benchmarking.