Data Science

We are firm believers talent is key in Data Science - people with the right Skills, Tools, Mindset and Track-record. We are experts in unlocking the value of data, driving business-relevant insights and providing lasting solutions.

We cover the full spectrum of data management within an organization. We combine datasets from various internal and external sources and create predictive models using R, Python & other software to better signal trends and implications. We deliver agile data science as a service and can quickly ramp up teams, answer questions, test hypotheses and roll-out production-level models.

We are a full-service provider:

 

Applications

Market Modelling

Analyze key drivers for sales and market share performance. Model pricing scenarios. Estimate the size of adjacent industries and new opportunities.

Retention/ Churn Analysis

Retention / Churn analytics to improve customer value in Service-based industries (telecoms, insurance companies, banks and non-banking financial institutions).

 

Purchase Decision Analysis

Map and prioritize decision makers, influencers and purchase drivers in complex purchase funnels, enabling the sales team to target the right stakeholders with the right messages and propositions.

Capacity and Resource Planning

Monitor and analyze current demands on resources. E.g. call center: analyze drivers for calls and propensity of occurrence considering customer, product, service life cycles and volumes.

 

Multi-Channel Digital & Offline Attribution

Identify contribution to conversion from digital and offline touchpoints (channels). Maximize the impact on both online sales offline sales and allocate budgets across channels to maximize ROI.

Motion and Geospatial Analytics

Extract insights from Customer, Device and Store locations and other spatial data to optimize sales, marketing, distribution, provide bundling, targeted advertising and prioritization of locations.

 

Forecasting

Forecast your sales volume in turbulent or opaque markets, facilitating your sales planning, resource allocation and purchase decision making.

Segmentation

Improve sales process and customer satisfaction through better understanding of customer groups and corresponding differences in customer needs.

 

ROI Analysis

Evaluate business (financial) impact of investments, decisions, or (marketing) actions. Enable resource allocation to highest-return actions.

Customer Journey Analytics

Model and simulate customer progression through product/ service life cycles. Determine value per customer journey stage, drivers for retention and paths to purchase to optimize life time value.

 

Predictive Maintenance

Optimize asset usage and maximize ROI from large Cap equipment through predictive maintenance - minimizing downtime, maximize uptime during peak load periods, minimize repair costs, maximize asset life

Individual Level Targeted Marketing

Design the best possible marketing campaign, product offering and pricing for each individual customer based on their historical behavior and predicted future behavior.

 

Risk Assessment

Assesse risk profiles of customer groups: quantify inherent and residual risks, as well as risk mitigation measures. Application in e.g. risk based pricing in leasing businesses.

Pricing

Determine the optimum combination of customer perceived value and price. Complement with views on competitive and substitute pricing models and positions.

 

Ascription

Leverage and complete existing datasets by filling in missing values. This approach allows prediction of the missing inputs through complex simulations with high levels of accuracy.

Customer Loyalty Modeling

Identify factors that affect customer experience and ultimately engage/disengage customers leading them to loyalty or exit.

 

Content Marketing

Optimize marketing message content and structure so as to maximize impact on target audience and KPIs (opens, reads, clicks, views, etc.) based on historical data and predictive analytics.

Fraud Detection and Prevention

Detect and prevent customer and supplier fraud by identifying data patterns. Sample data, profile customers, identify red flags, and apply predictive customer behavior.