Your data strategy. Is it documented, AI-aware, and aligned with business outcomes?

We have business goals, but they’re not really connected to a clear data or AI strategy.We use data analytics to manage, monitor and activate our business strategy.We have also defined clear KPIs and performance metrics to evaluate new opportunities.We have a documented AI & data strategy that treats first-party data as a strategic asset and defines how we’ll power agents, personalization, and automation across the customer journey.

Do you revisit and refine your data strategy, or is it “one and done”?

We created a data strategy once, but we rarely revisit it.We revisit our strategy mainly to keep up with regulations and avoid fines.We refine our strategy based on regulation, macro conditions, and major tech shifts like cloud and AI.We continuously iterate our AI & data strategy as new opportunities emerge (e.g. agents, new models, new channels) and use clear feedback loops from experiments to update it.

To what level are you using data insights and predictions to shape your data strategy (not just report on it)?

A little. We use historic customer data to make basic campaign and CX decisions.We analyze customer and warehouse data together to inform audiences, journeys, and investment choices.We use real-time behavioral data, predictive analytics, and machine learning to drive strategy and feed downstream tools.We treat our first-party data and AI models as a continuous decisioning engine – using real-time context, predictions, and next-best-action logic to steer product, marketing, service and risk decisions.