Services
Advanced Analytics
Building models to predict, optimise, recommend and segment complex behaviours.
What we do.
We apply machine learning, statistics and operations research modelling to support behavioural prediction, process optimisation, recommendation, and segmentation. Our understanding of the importance of commercial impact leads to our work being highly applied in nature.
Prediction
We use machine learning and advanced statistics to model customer and financial data to predict behaviour and performance. Recent applied data science engagements we’ve worked on include: Lead scoring, churn prediction, demand prediction, CLTV prediction, and store-level demand forecasting.
Optimisation
We help optimise existing processes to maximise efficiency and improve profitability. Our methodologies are applied to a variety of problems in commercial operations, sales, and marketing including: Macro space optimisation, pricing and discounting, Cost Per Acquisition (CPA) budget allocation, and stock availability optimisation.
Recommendation
We build bespoke recommendation systems where off-the-shelf solutions are impractical or inaccurate. Much of this is first focused on identifying a specific commercial solution that has a measurable outcome. Recent examples include: customer-specific email product content, email timing recommendation based on customer lifecycle, and SKU-level ranging recommendation.
Segmentation
Volume and complexity of customer datasets can make segmentation hard. We apply machine learning to identify high-value segments in your customer base and use this to power predictive models. Recent examples include: modelling customer missions at a UK grocer and using this to predict demand, differentiating between value-driven customers and VIPs, identifying customers with propensity to upsell.
