Retention Analytics for AI Marketing with SAS Customer Intelligence 360
Customer retention refers to the decisions, actions and tactics a brand facilitates to hold onto existing customers. In support of these efforts, applied customer-oriented analytics provide predictive metrics of which consumers might churn. Retention starts with the first customer interaction and continues throughout their entire relationship with your organization.
Though acquisition marketing seems exciting because these campaigns yield faster, more measurable results, it is important to take a long-term view. It is critical to recognize that a brand’s financial metrics related to revenue and net profit will depend significantly on increasing customer lifetime value.
Analytics provides three value propositions to support retention strategies:
- Reduce the cost to acquire customers
- Upsell/cross-sell opportunities
- Facilitate sustainable growth
Retention is interpreted by many as a vague concept, nebulous and evolving in meaning across industries. Wouldn't it be lovely if we relied on our customers to indicate they are leaving us for a competitor?
For some industry applications, like subscription-based services, that's easier to detect. But for most brands, the customer does not inform you that they are departing. As a result, you have to deduce customer retention based on activity within a certain period of time. By identifying unique shorter-term behaviors that can be used as an indicator for retention, optimizing on engagement in customer interactions often derives positive impact.
Figure 1: Strategies for improving customer retention
There isn’t a one-size-fits-all strategy to improve customer retention that can be applied in broad strokes across all industries. This is why SAS delivers features and capabilities for addressing retention, churn and attrition through both automated and customizable mechanisms. A non-exhaustive list of actionable analytical marketing approaches includes up/net-lift, deep survival, look-a-like, two-step and group-by modeling leveraging algorithms such as regressions, anomaly detection, gradient boosting & forests. For non-contractual customer relationships, approaches other than classification modeling are available since the churn event is not explicitly observed.
Within the subject of customer journey activation, let's walk through a presentation and demonstration together in the video below addressing the following questions:
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How does SAS deliver customer churn and attrition analysis?
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What data and algorithms does SAS use?
- How does SAS take action?
The demo video will show how SAS Customer Intelligence 360 performs AI-based targeted marketing exploiting powerful analytical insight derived from SAS Visual Data Mining & Machine Learning.
Learn more about how the SAS Platform can be applied for customer journey activation here.
