Right now, marketers are campaign-oriented. They focus on measuring the success of campaigns above measuring the customer lifetime value (CLV). This happens for various reasons, one being that campaign metrics are easier to measure than individual customer lifetime value.
Additionally, the way we calculate CLV isn’t always very accurate, as it uses known historical data and bases that data solely upon past transactions for a particular customer or group. This only provides insight into what has already occurred, and as such, sheds very little insight into the potential value of new subscribers and customers.
But not for long. Predictive marketing metrics, such as CLV calculation, will be vastly improved upon. Machine learning and AI will predict individual and group customer lifetime value – this is called Predictive CLV. So goodbye historical CLV and hello predictive CLV.
Predictive CLV measures types of actionable insights that are current, to help marketers make better informed, data-driven decisions that contribute to increased revenue. Insights such as: which group of subscribers and their attributes will be the most profitable over a specific time period, which acquisition budget earns the highest ROI and which customer attributes assist in driving increased lifetime value.
Marketers most value services that enable them to measure, attribute, and optimize their programs.
Measurement/attribution (40%) Program optimization (36%) Algorithm development (33%) Identifying audience trends (31%) Implement new marketing channels (30%) New customer acquisition (28%) New content/templates (27%) Data management/integration (24%) Process management (17%) None (3%)
Machine learning and AI will predict individual and group customer lifetime value — this is called Predictive CLV.
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