Understanding the LTV of our clients is key to establish a strategy to increase the value of our customer base with a long-term vision.
The process involves defining a perimeter (for example, 12 months), a metric (for example, number of purchases of a certain product) and making the prediction of this metric on the perimeter for our customers base (how many purchases of a given product will our customers make in 12 months?).
Once we know the average number of purchases from our customer base, we can group our customers according to different criteria (Dimensions) and compare their LTV. This can allow us, for example, to compare the LTV of the users who have visited our ecommerce for the first time through a Desktop versus those who did it through our App (in this case the Dimension is the device). With this information, we can decide in which device we are more interested in investing taking into account the future profitability of our clients.
In Big Thing Analytics we use prediction models to calculate the LTV of your customer base. We also help you define what dimensions are valuable for the analysis of your business and implement all the strategy, definition and implementation of the LTV within your organization, providing a valuable source of information for the implementation of medium and long-term policies.