Post by anamika371 on Jan 13, 2024 6:09:12 GMT -5
System as a cake. Without the right ingredients, a cake tastes bad. Without the right signals, a health scoring system tells you nothing. Best practice: Challenge your assumptions. Naturally, selecting your predictive signals requires making some assumptions. For example, you assume that there’s a positive correlation between how often a customer uses your product and their overall health score. Although you can’t get around making assumptions, you can reduce the likelihood of them being costly.
How? By staying on top of your data. If you notice that a significant amount of churn Email Marketing List among customers who use your product often, you’ll need to adjust your signal selections. . Assign weights to your signals Something that may not be immediately obvious to you upon selecting your predictive signals is the fact that they’re not equally indicative of the outcome you’re tracking. For example, whereas breadth of features a particular customer uses whenever they log into your product may only be somewhat predictive of churn.
The degree of impact your product has on their business results is probably highly predictive of churn. If you treat signals of different importance as if they’re equally important, your customers’ health scores won’t be accurate. customer-health-scoring-example-score That’s why you need to assign a weight to each signal. If your signals are ingredients, their weights are the measurements. In order for the cake to taste good—in order for your health scoring system to be meaningful—you need to be thoughtful about the measurements.
How? By staying on top of your data. If you notice that a significant amount of churn Email Marketing List among customers who use your product often, you’ll need to adjust your signal selections. . Assign weights to your signals Something that may not be immediately obvious to you upon selecting your predictive signals is the fact that they’re not equally indicative of the outcome you’re tracking. For example, whereas breadth of features a particular customer uses whenever they log into your product may only be somewhat predictive of churn.
The degree of impact your product has on their business results is probably highly predictive of churn. If you treat signals of different importance as if they’re equally important, your customers’ health scores won’t be accurate. customer-health-scoring-example-score That’s why you need to assign a weight to each signal. If your signals are ingredients, their weights are the measurements. In order for the cake to taste good—in order for your health scoring system to be meaningful—you need to be thoughtful about the measurements.
