Next it’s time to put the data to work. This capability is made up of two key pieces: what insights you derive from customer data at face value and what machine learning and AI can predict about customer activity based on historical data. Insights should be generated at the customer and segment-level, including brand and channel behaviors, product preferences, revenue sizing and recommended actions.
Because personalization is a business driver, it’s also important to visualize and monitor customer-centric metrics and KPIs that highlight shifts in customer economic indicators, identifying risks and opportunities. Additionally, predictive models can help marketers identify which segments, personas, or audiences have the highest growth opportunity, affinity for specific products, or churn propensity, all of which can be applied to improve campaign ROI and customer lifetime value.