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.
Some of our data science application areas:
Analyzes of key drivers for sales and market share performance. Pricing models research & analytics. Estimation of the size of adjacent industries and new opportunities.
Maps decision makers, influencers and purchase drivers in the purchase funnel. Ranks and prioritize drivers for purchase in complex funnels. This enables sales team to target the right stakeholders with the right messages and propositions.
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
Volume forecasting in turbulent or opaque markets. Provides input to sales planning, resource allocation and purchase decision making.
Evaluates business (financial) impact of investments, decisions, or (marketing) actions. Enable resource allocation to best potential actions.
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
Assesses risk profiles of customer groups. Quantification of inherent risks, risk mitigation measures and residual risks. Application in e.g. risk based pricing in leasing businesses.
Leverage and complete existing datasets by filling in missing values. Through complex simulations this approach allows prediction of the missing inputs with high levels of accuracy.
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
Retention / Churn analytics to improve customer value in Service-based industries (telecoms, insurance companies, banks and non-banking financial institutions).
Monitors and analyzes the current demands on resources (e.g. call centers). Analyses drivers for calls and propensity of occurrence considering customer, product, service life cycles and volumes.
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
Improves sales process and customer satisfaction through better understanding of customer groups and corresponding differences in customer needs.
Modelling and simulating customer progression through product/ service life cycles. Determining value per life cycle stage and drivers for retention and mapping routes to optimize life time value.
Design the best possible marketing campaign, product offering and pricing for each individual customer based on their historical behavior and predicted future behavior
Determining the optimum combination of customer perceived value and price. Complemented with views on competitive and substitute pricing models and positions.
Helps clients identify factors that affect customer experience and ultimately engage/disengage customers causing them to be loyal or abandon company/product/service.
Help detect and prevent customer and supplier fraud by identifying patterns. Sample data, profile customers, identify red flags, and apply predictive customer behavior.