The method
Apply this prompt in your LLM platform. Input customer data (purchase history, support interactions, demographics). Refine prompt with specific churn indicators relevant to your industry for targeted and effective prevention strategies.
The prompts
Prompt 1
Analyze the following customer data to predict the likelihood of churn and suggest three actionable retention strategies. Prioritize strategies that address identified risk factors: [Insert Customer Data, including purchase history, support tickets, website activity, and demographic information]. Focus on highlighting specific churn indicators such as decreased purchase frequency, increased complaints, or inactivity. Output the churn probability score along with the rationale and the tailored retention strategies.
Prompt 2
Based on the following customer profile and interaction history, identify potential triggers for churn and propose personalized interventions to prevent it. Consider factors such as customer lifetime value, recent product usage, and sentiment expressed in customer service interactions. [Insert Customer Data]. The interventions should be specific, measurable, achievable, relevant, and time-bound (SMART). Include a cost-benefit analysis of each proposed intervention, considering the potential revenue saved versus the cost of implementation. The output should be concise but detailed.