The method
Use this prompt within a content generation LLM such as ChatGPT, Gemini, or Claude. Input your current segmentation criteria and campaign goals. Adjust based on initial output relevance, and use A/B testing with generated variations for optimal results.
The prompts
Prompt 1
I'm running a marketing automation campaign targeting leads who downloaded our whitepaper on 'AI in Finance.' Our primary goal is to convert these leads into paying customers for our AI-powered financial planning software. Currently, we segment based on job title (e.g., CFO, Financial Analyst) and company size (SMB, Enterprise). Suggest three alternative or refined segmentation strategies that could improve our conversion rates. Consider factors like lead engagement with previous emails, specific pain points related to financial planning that our software addresses, and potential buying signals we can track. For each suggested strategy, explain why it's an improvement over our current approach and how we can implement it within our marketing automation platform.
Prompt 2
Our current marketing automation targets subscribers based on purchase history, specifically whether they've bought product A, B, or C. We want to personalize the experience further, focusing on cross-selling and upselling opportunities. The products are: Product A (Basic CRM), Product B (Advanced Analytics Module), and Product C (Marketing Automation Suite). Generate three prompt variations that enhance segmentation for more effective upselling and cross-selling campaigns. Include how to use behavioral data (website activity, product usage) and demographic data (industry, company size) to tailor messaging. Provide examples of specific email subject lines and content snippets we could use for each segment. The goal is to increase average order value and customer lifetime value.