All Insights
805 articles on customer experience, AI readiness and service automation.
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A practical playbook for test governance
Executives face a quality paradox. Teams ship faster, but customer expectations rise faster still. Test governance aligns product, engineering, data, and…
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Audit your experiment backlog: a step-by-step workflow
Leaders run experiments to reduce uncertainty, but backlogs often swell with ambiguous ideas, weak hypotheses, and unclear measures of success. A…
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Audit your personalisation experiences: a step-by-step workflow
Executive teams face a simple truth. Personalisation wins when it serves real customer needs with timely, relevant interactions. Multiple studies link…
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Case Study: bank lifts cross-sell with real-time decisioning (2025)
Executives use real-time decisioning to select the next best action for an individual during a live interaction. Real-time decisioning blends identity…
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Case Study: marketplace accelerates learning with experiments (2025)
The leadership team framed growth as a learning problem. The marketplace served two asymmetric customer groups, and the team saw faster learning as the…
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Common mistakes with p-values and how to avoid them?
P-values estimate how compatible observed data are with a specific statistical model that assumes there is no real effect. A small p-value indicates that…
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Experimentation checklist and guardrail metric templates
High performing CX and service teams treat experimentation as the operating system for decision making. They use controlled experiments to isolate causal…
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How randomised tests work: samples, power and spillovers?
Randomised tests assign units to treatment and control by chance to estimate a causal effect with minimal bias. This simple mechanism creates comparable…
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How to measure causal impact: metrics and methods
Executives run programs to change behaviour. Programs only create value if they cause a change that would not otherwise happen. Causal impact measurement…
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How to measure personalisation impact: metrics and methods?
Executives face pressure to prove that personalisation drives revenue, reduces cost, and improves loyalty. Strong intent does not guarantee effect.…
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How to roll out an experimentation program in your organisation?
Executives face volatile demand, shifting preferences, and finite capital. An experimentation program turns uncertainty into measured learning by running…
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How to roll out real-time decisioning in your organisation?
Real-time decisioning is the practice of using current context, customer data, and predictive signals to select the next best action in the moment of…
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Implementing geo experiments step by step
Leaders face a measurement problem. Cookie-based tests struggle with privacy, identity resolution, and cross-device leakage. Geo experiments solve this by…
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Key principles of causal inference for CX teams
Causal inference explains how actions change outcomes, not just how variables move together. CX leaders need causal answers to decide which journey fixes…
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Meet Customer Science at Genesys Xperience 2025 in Sydney
Genesys Xperience 2025 is just around the corner - and Customer Science is excited to be part of Australia’s premier customer experience event. Join us at…
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Myths and facts about over-personalisation risks
Executives confront a paradox where customers expect relevant experiences while regulators and platforms restrict tracking. Over-personalisation occurs…
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Personalisation checklist and rules catalogue templates
Leaders define personalisation as the dynamic tailoring of experiences, content, and offers to an individual or account using declared, observed, and…
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Rule-based personalisation vs ml-based personalisation: when to use each
Leaders set strategy when they define personalisation as the delivery of content, offers, or service treatments that adapt to the individual based on…
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What is experiment design and why it matters?
Leaders define experiment design as the structured plan that governs how a team runs a test to estimate cause and effect with confidence. The unit is the…
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A practical playbook for next-best-offer eligibility
Eligibility rules decide who can safely and profitably receive an offer. Eligibility is the gate between aspiration and execution. Organisations often…
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Audit your model lifecycle: a step-by-step workflow
Executives face rising expectations, tighter privacy rules, and models that change when data or context shifts. A model lifecycle audit gives leaders a…
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Case Study: insurer reduces churn with uplift modelling (2025)
A regional multiline insurer saw rising customer churn in motor and home lines and a widening gap between acquisition cost and lifetime value. Leadership…
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Common mistakes with feature leakage and how to avoid them?
Leaders set bold goals for predictive CX. Models promise next-best-action, churn risk, and proactive service. Feature leakage breaks those promises.…
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How decisioning works: contexts, policies and arbitration?
Decisioning selects the next best action for a customer at a specific time. It uses data, rules, and models to resolve tradeoffs across revenue, cost…