When using AI to write emails, one of the biggest challenges is the limitless possibilities it offers. While it can generate countless emails, the challenge is to ensure the emails align with your brand and resonate with your customers.\nHere are five common mistakes marketers make when using AI for email generation and solutions to improve both the quality and performance of these emails:\nTexts Become Too Long\nWrong Tone of Voice\nToo Fancy Language\nToo Generic Texts\nLacks Knowledge on High-Performing Emails\nBest Practices for Prompting AI to Write Effective Emails\nKeep Prompts Short and Specific\nInclude Key Information\n: Specify the text length, core message, and any particular emojis to use.\nBe Concise\n: Ensure every sentence in the prompt is necessary and specific. When working with new customers, we often find that simplifying and shortening the prompt enhances the quality of the email.\nHighlight Essential Details\n: Focus on the main points that need to be communicated, such as the purpose of the email and the desired call to action.\nAvoid Ambiguity\n: Clearly state what you want to avoid misunderstandings that can lead to irrelevant or off-brand content.\nAvoid Giving the AI Rules\nRules Can Confuse AI\n: Similar to how a child fixates on ice cream when told not to eat it, AI can become preoccupied with rules, which can negatively impact the output.\nFocus on Positive Guidance\n: Instead of setting rules, guide the AI with positive examples.\nUse Positive Reinforcement\n: Highlight what the AI does well and encourage it to do more of that rather than focusing on what it shouldn't do.\nSimplify Instructions\n: Provide clear, straightforward guidance without overcomplicating the prompt with too many restrictions.\nProvide Examples\nUse High-Performing Samples\n: Successful customers often include five high-performing subject lines from existing emails as examples. This helps the AI understand what works well.\nInclude Different Scenarios\n: Offer a variety of examples that cover different contexts and tones to give the AI a broader understanding.\nExplain Why Examples Work\n: Add brief notes on why each example is effective, helping the AI to grasp the underlying principles of good email content.\nUpdate Examples Regularly\n: Keep your examples current by periodically updating them with the latest high-performing emails.\nBe Iterative\nStart Simple\n: Begin with a basic prompt and let the AI generate an email.\nEvaluate and Adjust\n: Review the output to identify what works and what doesn’t. Highlight the positive aspects and think of ways to avoid the negatives without setting strict rules.\nIncremental Improvements\n: Make small, gradual changes to the prompt and observe how the AI's output evolves.\nTest Different Approaches\n: Experiment with various prompt structures and strategies to find the most effective method for your specific needs.\nConclusion\nBy following these best practices, you can leverage AI to generate high-quality, effective emails. Keep your prompts clear and concise, avoid setting confusing rules, provide positive examples, and iterate based on feedback to continuously improve the AI’s performance.

How to Prompt a Good Email Using AI

I'm fascinated by the intersection of Machine Learning (ML) and Generative AI (Gen AI).\nThis document is written to share how we at Alvas.ai are working on combining the two technologies, to improve the output of what a Generative AI model can output.\nFirst I’ll define a few key concepts, that are important to be aligned on. \nUnderstanding Machine Learning and Generative AI\nMachine Learning (ML)\n: This technology is excellent at optimizing and identifying patterns to achieve specific goals, like increasing watch time on YouTube, maximizing return on ad spend on Facebook Ads, or improving product recommendations.\nGenerative AI (Gen AI)\n: This type of AI excels at creativity and generating content, which is crucial for marketing where creating engaging content is key.\nCombining Creativity and Optimization\nIn marketing, we need to excel at both creating compelling content and optimizing it to maximize return on investment. Traditionally, marketers have created multiple versions of content and used split testing to find the best-performing variant.\nIntroducing Few Shot Learning\nFew Shot Learning represents a new way to create high-performing marketing content. Here’s how it works:\nZero Shot Learning\n: Typically, you might give an AI a single prompt and ask it to create something new. For example, you might prompt it like; "You're an excellent marketeer, write a promotional email for our new summer collection." The AI will produce a basic email, but the quality could be hit or miss because it only has one chance to get it right.\nFew Shot Learning\n: Instead, you show the AI multiple examples of good content before asking it to create something new. This way, the AI understands the style, tone, and quality you expect.\nHere’s an example of promotional emails for an e-commerce business:\nPrompt\n: "You're an excellent marketeer, write a promotional email for our new summer collection."\nAI Output\n: "Good email we've sent to customers before."\nPrompt\n: "Great, now write another promotional email about our upcoming holiday sale."\nAI Output\n: "Another good email we've sent to customers."\nPrompt\n: "Thanks, now write about our new arrivals in the electronics section."\nAI Output\n: "Good email we've sent to customers."\nPrompt: \n: "Now, please write a similar promotional email about our exclusive deals on home decor."\nBy providing these examples, the AI produces content that aligns with your brand and maintains a consistent tone.\nCombining Few Shot Learning with Machine Learning\nAt Alvas, we use a combination of Machine Learning and Few Shot Learning to generate personalized emails. Here’s our process:\nSegmenting Customers\n: We segment our customer base and identify which customers fit within each segment.\nFinding Good Emails\n: Using Machine Learning, we find emails that have been sent to similar customers and have generated conversions. These are our "Good Emails."\nFew Shot Learning\n: We then use these good emails as prompts for Few Shot Learning.\nFor example:\nPrompt\n: "Write an email to customer A about our winter sale."\nAI Output\n: "Good email that made a conversion."\nPrompt\n: "Write an email to customer B about our new arrivals in fashion."\nAI Output\n: "Good email that made a conversion."\nPrompt\n: "Write an email to customer C about our clearance event."\nAI Output\n: "Good email that made a conversion."\nPrompt\n: "Thanks, now write an email to customer D about our loyalty rewards program."\nBenefits\nUsing Few Shot Learning, we create content faster and better. Every new piece of content is based on data about what works and resonates with our customers, leading to improved results and more efficient marketing efforts.\nFor example, one of our clients, Shaping New Tomorrow, implemented Self Learning in their emails, and saw an uplift of more than 100% in Revenue Per Email. 

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