What Lifecycle Marketing Leaders Need to Know About Model Context Protocols (MCPs)
The AI Protocol That Could 10x Your Email Marketing Workflow
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Explain Model Context Protocols (MCPs) to a Lifecycle Marketer
Model Context Protocols (MCPs) are essentially a new universal language that lets AI systems connect with all your different marketing tools and data sources. Think of how HTTP standardized the web – any browser can retrieve information from any website via a common protocol. Similarly, MCP is an open standard that allows AI (like GPT-4, Claude, etc.) to “talk” to CRMs, email platforms, analytics databases, content repositories, and more through one unified interface. In plain terms, MCPs act as a bridge between AI assistants and the systems where your data lives
Instead of building a custom integration for every single tool (CRM, email service, e-commerce database, etc.), you or your vendors would implement MCP once for each system. After that, any AI agent that understands MCP can securely access that system’s data or functions. Your preferred AI assistant could query customer data, pull content, or even execute an action (like scheduling an email) across different platforms – all while maintaining context (so it remembers who the customer is, what campaign it’s part of, what rules to follow, etc.)
Why It Matters for Lifecycle Marketers
MCPs are very new (Anthropic open-sourced the standard in late 2024), so widespread adoption in marketing is only just beginning.
The true power of MCPs in email marketing is just starting to be tapped. Because MCP is an open protocol, forward-thinking teams are beginning to connect AI assistants to email workflows in more flexible ways. For instance, Zapier (a popular no-code integration platform) recently launched support for MCP, which effectively lets an AI agent access 7,000+ apps through one standard interface. In practical terms, a savvy marketer could use this to have an AI assistant that can pull a list from their CRM, draft an email in their ESP (Email Service Provider), and even trigger the send – all in response to a single natural-language prompt. Zapier notes that with MCP, an AI assistant isn’t limited to just talking; it can take action in your apps. In one example, “your assistant can send emails, schedule meetings, and keep your life running smoothly, all with a single prompt.” This means a lifecycle marketer could say something like, “Find all users who haven’t opened in 90 days and send them a re-engagement email offer,” and an AI agent (using MCP) could handle the data querying and execute the email send, end-to-end.
To be clear, most marketing teams are not quite there yet – these capabilities are in beta or pilot phases. Early adopters are testing the waters: Anthropic mentioned that companies like Block (Square) and Apollo have already integrated MCP into internal systems. Those aren’t email marketing examples per se, but they show enterprises are confident enough in MCP to connect mission-critical data. In the marketing realm, we’re seeing the first signs in tooling: for example, CRM and automation vendors are announcing AI features that could leverage MCP or similar standards soon. Microsoft has added MCP support to its Copilot Studio (for building AI agents in the enterprise) – so a Copilot that helps with customer outreach can now hook into data or actions via MCP with just a few clicks.
Near-Term Opportunities:
While some of the use cases below are already possible today through custom integrations, MCP makes it possible to do all of this seamlessly through a single interface—like Claude or ChatGPT—without stitching tools together manually.
Smarter Segmentation: AI can query multiple systems live to build ultra-targeted audiences and surface engagement insights.
Hyper-Personalized Emails: Instead of pre-filled fields, an AI can fetch live customer behavior, inventory data, and brand-safe content to create emails that feel handcrafted.
Cross-Channel Orchestration: The AI could pick the best channel (email, SMS, push) for each user based on behavior and context.
Automated Optimization: AI agents can monitor performance (e.g., A/B test results) and make real-time adjustments or pause sequences based on support tickets, purchase behavior, etc.
Actionable Steps for Lifecycle Marketers
Audit & Document Your Data + Content: Make sure your customer data is clean, connected, and accessible. Document brand guidelines, compliance rules, campaign logic, and anything else an AI assistant should “know” to operate effectively. This becomes the foundation of your model’s context.
Push Your Vendors: Ask your ESP, CRM, or CDP vendors about MCP or open integration plans. Prefer platforms with Zapier/API access.
Run Small Experiments: Use Claude or GPT-4 with dummy data and tools like Zapier to test automation or report generation.
Establish Guardrails: Set approval flows and security controls as you would for a new team member.
Educate & Brainstorm Internally: Identify repetitive, high-volume, or cross-platform tasks AI could own.
Double Down on Strategy & Creativity: Let AI do the heavy lifting—your value lies in storytelling, journey design, and customer insight.
Stay Informed: Join communities, follow AI/marketing updates, and get into betas early. Here’s the slack community I created just to discuss AI x lifecycle marketing.