Applied Decision Intelligence

 

We start with the decision and turn first party customer data into calibrated, research based predictive signals translated into decision ready policies for prioritization. Guided by the Pareto principle, we identify the few signals that matter most and turn insight into action for long term retention, profitability, and customer value.

Customer Base Audit

A research-based diagnostic (descriptive or predictive) that maps how customer value is created, sustained, and lost, so you can focus retention, acquisition, and investment where it matters most.

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Predictive CLV Modeling

Explainable, research-driven CLV models that forecast future customer value accurately at the individual level, so you can allocate budgets, set targeting rules, and grow profitably with confidence.

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Customer State Intelligence

A dynamic probabilistic lifecycle model that detects state shifts (active → drifting → dormant) and predicts what's next, so your CRM team can intervene earlier, choose the right action, and avoid one-size-fits-all journeys.

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Micro-Behavioral Value Drivers

We isolate the few repeatable behaviors that reliably drive retention and long-term value, then translate them into CRM triggers and targeting rules that turn insight into action.

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Behavioral Experimentation

We design and guide decision-critical experiments grounded in behavioral science, to quantify incremental profit, retention, and long-term value, so you can scale the right playbook with confidence.

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Behavioral Segmentation

Behavior-based segmentation that turns your customer base into actionable groups, so your CRM team can tailor journeys, policies, and experiences by how customers behave and what they're likely to do next.

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"We partner with organizations where growth depends on long-term customer behavior—across retail, travel, subscriptions, NGOs, and broad B2B portfolios. Deployed into your existing CRM/CDP, we apply research-grade statistical modeling to reveal, predict, and scale the behaviors that drive retention and profitability—earlier than traditional metrics can."

Joel Anderssén, Partner & Lead Data Scientist
Joel Anderssén, Partner & Lead Data Scientist
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