Token

To understand tokens, imagine you're sending an email to a customer service team. You don't send the entire email as one blob of information—it gets broken into sentences, then words, then letters. AI models work similarly, but they break text into chunks called tokens. A token is typically a word, part of a word, or punctuation mark. For example, the word "marketing" might be one token, while "unbelievable" might be three tokens (un-believ-able). The AI model processes these tokens one at a time to understand and generate text.

Why should you care? Because you're charged by tokens, not by words or requests. If you're using an AI tool like ChatGPT or Claude for customer service, content generation, or data analysis, every input you send and every output you receive consumes tokens. A single customer service query might use 150 tokens. A product description might use 300 tokens. Scale that across thousands of interactions monthly, and token consumption directly affects your AI budget.

Here's a concrete example: You're using an AI tool to personalize email campaigns. You send the AI a customer's purchase history (200 tokens), ask it to write a personalized email (100 tokens input), and it generates a 300-token response. That single interaction costs you 600 tokens. If you're doing this for 10,000 customers, you're consuming 6 million tokens—which could cost hundreds or thousands of dollars depending on the model and pricing tier.

The practical implication: When evaluating AI tools, don't just look at the monthly subscription price. Ask vendors about token limits, overage costs, and token efficiency. Some models are more "token-efficient" than others, meaning they accomplish the same task using fewer tokens. Also, understand that longer prompts (your instructions to the AI) consume more tokens, so how you structure your requests matters financially. This is why prompt engineering—writing efficient instructions—has become a cost-management skill for marketing teams using AI at scale.