8 Practical AI Tips for Content Marketing

16 min read • 3,812 words

Opening LinkedIn nowadays feels like a nightmare. We’re bombarded by a plethora of “This AI agent is about to help you solve all your business problems” or a thousand different variations of “This magical prompt that will skyrocket your CTR”.

It’s overwhelming. The truth is, the world of digital marketing has never felt as difficult to navigate as today.

I’ve had the chance to dive deep into AI early on and wanted to share some practical applications specifically for content marketing that actually work in the real world and don’t just sound good on Linkedin.

Executive Summary

In this article, I share eight practical AI tips for content marketers to navigate the digital marketing landscape more effectively. These tips are not theoretical but actionable strategies I’ve implemented myself, designed to help you maximize the value of your content marketing efforts.

1. Maximize Subject Matter Expert (SME) Interviews: Use AI to transform a single interview with an expert into a content goldmine, cutting SME involvement from hours to just 45 minutes per month.
2. Analyze Content Performance Patterns: Let AI crunch your performance data to reveal patterns you’d never spot manually, uncovering statistical correlations that move the needle.
3. Refresh High-Value Content: Strategically resurrect old content by updating it using AI, keeping the same URL and preserving SEO equity while improving user experience and relevance.
4. Generate FAQs: Turn customer questions scattered across support tickets, chats, and social media into a dynamic FAQ system that answers what customers are really asking.
5. Test Headlines Systematically: Use AI to generate 15 headline variations for a single piece of content, A/B testing them to build a data-driven formula for headlines that work with your specific audience.
6. Ensure Brand Voice Consistency: Use AI to check drafts against your established voice patterns or optimize old content pieces, maintaining brand voice consistency as your content volume scales.
7. Enhance Readability: Use AI as your readability coach to analyze your content using readability metrics and suggest specific simplifications without losing meaning.
8. Mine Customer Reviews: Use AI to turn customer feedback into content that directly addresses pain points, creating high-quality support content that solves customer pain points.

By implementing these AI strategies, you’ll transform raw conversation data into structured, valuable insights that can be readily applied to content creation, research, decision-making, and knowledge development.

Maximize Subject Matter Expert (SME) Interviews

Getting time with your company’s experts is like trying to schedule a meeting with a celebrity. But when you do get those precious 30 minutes, you need to make sure you squeeze every drop of value from them.

Here’s what you can do: Instead of burning your SME’s time, you can grab one solid interview and let AI help you turn it into a content goldmine.

Step-by-Step SME Interview Process

  1. Record a focused 30-minute chat with clear questions
  2. Get it transcribed using AI (check out Sonix and Trint)
  3. Feed the transcript to AI with a specific content extraction prompt
  4. Create one main piece and several derivatives from a single interview

AI Transcript Analysis Prompt

# Advanced Transcript Analysis and Content Extraction System

## Role Definition
Act as an Expert Content Analyst specialized in identifying key themes, extracting meaningful quotes, and generating valuable content ideas from transcripts. Your expertise combines analytical precision with contextual understanding to transform raw transcripts into structured, actionable insights.

## Context and Background
Transcripts contain rich information but often in unstructured formats that make extraction of valuable content difficult. The relationship between effective content extraction and usability of that information is direct and significant. This analysis system applies systematic approaches to identify patterns, highlight important statements, and recognize content opportunities that might otherwise remain buried in lengthy transcripts.

## Key Objectives
- Identify and categorize the central themes and topics discussed in the transcript
- Extract the most impactful, quotable statements that capture key insights
- Generate practical content ideas that build upon the transcript's subject matter
- Present findings in a structured, accessible format that facilitates easy application
- Preserve the authentic voice and intent of the original speakers

## Methodology for Content Extraction

### 1. Initial Analysis (Understanding the Transcript)
- Analyze the transcript to identify:
  * The core subject matter and discussion focus
  * The participant roles and perspectives represented
  * The progression of ideas and conversation flow
  * Any recurring concepts, terms, or references
  * The implicit and explicit knowledge domains covered
- Note contextual elements such as tone, intended audience, and discussion format

### 2. Thematic Extraction
- Identify primary themes by:
  * Recognizing repeated topics or concerns
  * Grouping related ideas under broader conceptual umbrellas
  * Noting points of consensus or disagreement
  * Tracking the evolution of key concepts throughout the transcript
  * Distinguishing between explicit themes (directly stated) and implicit themes (suggested)
- Organize themes hierarchically, from major frameworks to supporting concepts

### 3. Quote Selection
- Select high-value quotes based on these criteria:
  * Clarity and conciseness of expression
  * Substantive content that encapsulates key insights
  * Uniqueness of perspective or articulation
  * Emotional resonance or storytelling impact
  * Utility for illustrating broader themes
  * Attribution to specific speakers when available
- Preserve the original wording while ensuring quotes can stand independently

### 4. Content Idea Generation
- Develop content ideas by:
  * Identifying knowledge gaps the transcript begins to address
  * Recognizing areas where expansion would be valuable
  * Considering different formats that could leverage the transcript content
  * Looking for actionable insights that could be developed further
  * Finding connection points to broader topics or current trends
  * Noting audience needs implied by the discussion

### 5. Contextual Integration
- Connect findings to the broader context by:
  * Relating themes to established knowledge in the field
  * Identifying how quotes reflect larger patterns or principles
  * Suggesting how content ideas could address specific audience needs
  * Considering how the transcript content relates to current developments
  * Noting how diverse perspectives within the transcript create a richer understanding

## Output Format

Your analysis will include these components:

### 1. Executive Summary
A concise overview of the transcript's focus, key participants (if identified), and the most significant themes and insights (150-200 words).

### 2. Thematic Analysis
Structured presentation of 3-7 central themes with:
- Theme name/title
- Brief description of the theme (2-3 sentences)
- Sub-themes or related concepts
- Frequency and significance within the transcript

### 3. Notable Quotes
A collection of 5-10 impactful quotes with:
- The exact quote (properly attributed if speaker is identified)
- Brief context for the quote
- Connection to identified themes
- Reason for selection (insight, clarity, uniqueness, etc.)

### 4. Content Development Opportunities
A set of 5-8 content ideas inspired by the transcript:
- Content idea title/concept
- Format suggestion (article, video, infographic, podcast, etc.)
- Brief description of the proposed content
- Key points to include
- Target audience and potential value

### 5. Analytical Insights
Higher-level observations about:
- Patterns in language, framing, or perspective
- Gaps or unanswered questions from the discussion
- Connections to broader industry trends or challenges
- Potential applications or implications of the discussed ideas

## Additional Parameters
- Maintain objectivity while extracting content, avoiding insertion of personal opinion
- Preserve nuance and complexity rather than oversimplifying positions
- Include contrasting viewpoints when present in the transcript
- Respect the original context and intent of statements
- Balance breadth of coverage with depth of analysis
- Consider both explicit content and subtextual elements
- Ensure all conclusions are directly supported by transcript content

By following this systematic approach to transcript analysis, you will transform raw conversation data into structured, valuable insights that can be readily applied to content creation, research, decision-making, and knowledge development.

Common Questions About SME Interviews

How long should an SME interview last?

30 minutes is the optimal duration. This gives you enough time to extract valuable insights without overwhelming busy experts with long commitments.

How many content pieces can you create from one interview?

Typically 5-8 pieces: one main article, 2-3 social media posts, 1-2 email newsletter sections, and several quotes for future content. This cuts SME involvement from hours down to 45 minutes per month.

What’s the best transcription tool for SME interviews?

Sonix and Trint are excellent options for AI-powered transcription with high accuracy rates and easy export features.

Quick Implementation Checklist

  • Schedule 30-minute SME interview
  • Choose transcription tool (Sonix/Trint)
  • Apply content extraction prompt
  • Create multiple content pieces from transcript

What makes this so powerful is you’re cutting SME involvement from hours down to 45 minutes per month while increasing the authority in your content. Your experts talk once, you publish all month.

The key is using AI for what it’s good at (analysis and organization) while preserving the authentic expertise from the interview. Your readers get the real deal – expert knowledge in a digestible format.

Analyze Content Performance Patterns

We all like to think we know what content works, but deep down, we’re always aware we’re guessing at best.

Instead of shooting in the dark, let AI crunch your performance data and reveal patterns you’d never spot manually. There are always statistical correlations that move the needle, it’s never been easier to find them than now.

Data Analysis Implementation Methods

Method 1: CSV Analysis Approach

  1. Extract raw performance data from Google Analytics/Google Search Console
  2. Structure it in a way AI can process (CSV format works well)
  3. Ask your LLM to optimize data structure for maximum analysis quality
  4. Generate a specific prompt for identifying statistically significant patterns
  5. Remove misleading data points (bounce rates can be deceptive)
  6. Run analysis in new chat with optimized prompt and clean data

Method 2: Python Script Analysis

Ask your LLM to build a Python script that identifies statistical patterns. Claude, Grok, and OpenAI all have artifact functionality that makes this powerful once you master it.

Real-World Results

I had incredible breakthroughs using this methodology. Below, you can see that while I worked at Pipedrive, I ran a Python script to identify patterns in the blog data, and the results were incredibly beneficial.

The insights were more valuable than those provided by very expensive analytics platforms because AI can consider multiple variables simultaneously.

Common Questions About Performance Analysis

What data should I exclude from my analysis?

Bounce rates can be misleading, especially for single-page resources. Focus on engagement metrics, conversion data, and organic traffic patterns instead.

How much data do I need for meaningful analysis?

At minimum, 3 months of data with at least 50 content pieces. More data equals more reliable pattern identification.

Refresh High-Value Content

Sometimes, your best-performing pieces from the past are slowly dying, hemorrhaging traffic month after month.

You can use AI to strategically resurrect your old content by updating it.

Content Resurrection Process

  1. Open your content library and review all pieces published more than a year ago
  2. Identify articles with declining traffic (use tip #2 for this analysis)
  3. Apply the strategic content update prompt below
  4. Update strategically while keeping the same URL

Content Resurrection Prompt

# Content Resurrection Specialist: Strategic Content Update Protocol

## Role Establishment
Act as a Content Resurrection Specialist with expertise in SEO optimization, content strategy, and digital publishing trends. You are skilled at identifying outdated elements in content while preserving its core value and search equity.

## Context Framework
You are helping a content marketer who needs to revitalize underperforming but previously successful content pieces that have experienced traffic decline. This requires analyzing the existing content structure, identifying outdated information, and strategically refreshing the material while maintaining URL structure and core keyword targeting.

## Task Definition
Your task is to transform an aging content piece into a refreshed, relevant, and engaging version that can recapture its previous traffic performance while requiring significantly less effort than creating entirely new content.

## Process Instructions
Follow this process:
1. First, perform a comprehensive analysis of the existing content by identifying:
   - Outdated statistics, research, or time-sensitive references
   - Missing subtopics that have become relevant since publication
   - Sections with weak engagement or high bounce rates (if data available)
   - Keywords that may have evolved or changed in search intent
   - Content structure issues that don't match current best practices

2. Then, develop an update strategy that focuses on:
   - Preserving the URL structure and core topic focus
   - Maintaining successful elements from the original content
   - Integrating current industry data, statistics, and examples
   - Expanding coverage of subtopics that have grown in importance
   - Improving content structure for better user experience

3. Next, execute the content refresh by:
   - Rewriting the introduction to reflect current relevance and search intent
   - Updating factual information, statistics, and references with current data
   - Adding new sections addressing emerging subtopics or questions
   - Enhancing visual elements with updated graphics or embedding options
   - Strengthening calls-to-action based on current conversion best practices

4. Finally, prepare implementation recommendations that:
   - Preserve SEO equity through proper redirects if necessary
   - Update metadata to align with current search trends
   - Suggest internal linking improvements to boost content visibility
   - Outline a promotion strategy to re-introduce the content to your audience

## Output Specifications
Present your response as a comprehensive content update plan with:
- An executive summary highlighting key improvement opportunities
- A side-by-side comparison showing specific sections to preserve, modify, or add
- A detailed refresh implementation guide with specific content recommendations
- A post-update promotion strategy to maximize the refreshed content's visibility

## Additional Parameters
Additional requirements:
- Maintain the successful elements of the original content that drove its initial performance
- Ensure all updated information is accurate and properly sourced
- Focus on strategic improvements that will have the highest impact on performance
- Provide specific examples of how to modernize the content's tone and approach
- Include recommendations for fresh visuals, media embeds, or interactive elements where appropriate
- Suggest ways to better address current user search intent for the topic

Important Content Refresh Guidelines

Using this approach, I was able to resurrect some old pieces of content, but unless you want to get a manual penalty for using too much AI from Google 1, make sure to read through the content pieces and edit as a final step before you hit publish.

This approach is perfect for resource-constrained teams as you get maximum value from old content.

Common Questions About Content Refresh

How old should content be before refreshing?

Content published more than a year ago is typically a good candidate, especially if you notice declining traffic trends.

Should I change the URL when refreshing content?

Keep the same URL to preserve SEO equity and existing backlinks. Only change URLs if absolutely necessary and implement proper redirects.

Generate FAQs

Your customers are often telling us what content they need through their questions, but the problem is that this goldmine of data is often scattered across support tickets, chats, and social media.

AI can turn this chaos into a dynamic FAQ system that answers what customers are really asking.

FAQ Generation Process

  1. Gather real customer questions from all your support channels
  2. Use AI to cluster them by topic and identify the most frequent ones
  3. Create clear, conversational answers using the customer’s exact terminology
  4. Update monthly as new questions emerge

Why This Approach Works

What makes this powerful is that you’re addressing actual customer pain points in their own language. This creates content that naturally ranks for long-tail queries your customers are actually searching for.

Common Questions About FAQ Generation

How many customer questions do I need to start?

Start with at least 50-100 customer questions from various sources to identify meaningful patterns and clusters.

How often should I update my FAQ content?

Monthly updates work well for most businesses. This keeps content fresh and addresses new customer concerns as they emerge.

Test Headlines Systematically

Most of us write headlines based on gut feeling or what sounds clever in the moment. But in reality, headlines are probably the single biggest factor in whether anyone reads your content.

Setting up a systematic testing approach with AI is ridiculously simple:

Systematic Headlines Testing Process

  1. Use AI to generate 15 headline variations for a single piece of content
  2. Choose only 5 headlines you like the most
  3. A/B test them against each other
  4. Track which ones get the highest click-through rates

Headlines Generation Prompt

# Headline Variation Generator for Content Marketing

## Role Establishment
Act as a Senior Copywriting Specialist with expertise in headline optimization, digital marketing psychology, and content engagement metrics. You understand the principles of effective headlines across different platforms and content types.

## Context Framework
You are helping a content marketer who needs to generate multiple high-quality headline variations for a single piece of content to test for engagement, click-through rates, and audience resonance. These headlines will be critical for maximizing the content's visibility and impact across various distribution channels.

## Task Definition
Your task is to create 15 distinct, compelling headline variations that capture the essence of the content while optimizing for both audience engagement and potential search visibility.

## Process Instructions
Follow this process:
1. First, analyze the content description/summary by identifying the core topic, key value propositions, target audience, and primary keywords
2. Then, develop 5 headline variations using factual/informative approaches that clearly communicate the content's value
3. Next, create 5 headline variations using emotional triggers (curiosity, surprise, fear of missing out, etc.) to drive engagement
4. Then, craft 5 headline variations using different structural formats (questions, numbers, how-to, etc.) for variety
5. Finally, review all headlines to ensure they are concise (typically under 70 characters), compelling, and aligned with the content

## Output Specifications
Present your response as a structured list with:
- A brief introduction explaining your approach to the headline variations
- Three clearly labeled categories of headlines (Informative, Emotional, and Structural)
- All 15 headlines numbered consecutively across categories
- A brief explanation after each headline noting its specific technique or appeal

## Additional Parameters
Additional requirements:
- Maintain a professional yet conversational tone throughout the response
- Ensure each headline is unique and avoids redundancy in approach or wording
- Exclude clickbait tactics that over-promise or mislead
- Include SEO-friendly elements where appropriate without keyword stuffing
- If the content topic wasn't provided, request specific details about the content before proceeding

Building Your Headlines Formula

The beauty of this system is that you’re building a data-driven formula for headlines that work with your specific audience. Over time, you’ll develop headline templates that consistently outperform, taking the guesswork out of this critical element.

Common Questions About Headlines Testing

How many headlines should I test simultaneously?

Start with 3-5 headlines to get statistically meaningful results. Testing too many variations simultaneously can dilute your data.

What’s the minimum sample size for reliable headlines testing?

Aim for at least 100 clicks per headline variation to draw meaningful conclusions about performance differences.

Ensure Brand Voice Consistency

AI can be your brand voice guardian, ensuring consistency across all content:

Brand Voice Analysis Process

  1. Use the brand voice analysis prompt to identify style patterns in multiple content pieces
  2. Create a detailed voice guide based on the feedback you receive
  3. Use AI to check drafts against your established voice patterns
  4. Optimize old content pieces for voice consistency

Brand Voice Analysis Prompt

# Brand Voice Analysis and Consistency Evaluation

## Role Establishment
Act as a Brand Communications Analyst with expertise in voice analysis, linguistics, and content strategy. Your analytical skills allow you to identify patterns, recognize stylistic elements, and articulate the nuanced characteristics that define a brand's unique communication style.

## Context Framework
You are helping a marketing team who needs to evaluate and maintain consistency in their brand's voice across multiple content pieces and channels. This requires a systematic approach to identify the distinctive elements of the existing brand voice, detect any inconsistencies, and provide actionable guidance for maintaining a cohesive brand identity through language.

## Task Definition
Your task is to analyze multiple content samples from the same brand to extract, define, and articulate the core elements of their brand voice, identify any inconsistencies or deviations, and provide recommendations for maintaining voice consistency in future content creation.

## Process Instructions
Follow this process:
1. First, examine each content sample individually, identifying key linguistic patterns, stylistic choices, tone indicators, and vocabulary preferences by noting specific examples from each piece
2. Then, synthesize these observations to define the core elements of the brand voice, including tone, personality traits, vocabulary preferences, and stylistic conventions
3. Next, identify any inconsistencies or deviations across the content samples, pinpointing where and how the voice shifts or breaks from the established patterns
4. Finally, create a brand voice guide that clearly articulates the identified voice characteristics and provides practical recommendations for maintaining consistency

## Output Specifications
Present your analysis as a structured report with:
- An executive summary that briefly describes the overall brand voice and highlights key consistency findings
- A detailed brand voice profile with specific dimensions (tone, personality traits, stylistic elements, vocabulary preferences) supported by examples from the provided content
- A consistency analysis section that identifies patterns and highlights deviations across content pieces
- A practical recommendations section with specific guidance for maintaining and strengthening brand voice consistency

## Additional Parameters
Additional requirements:
- Support all observations with specific examples quoted directly from the provided content
- Use objective, analytical language when describing voice characteristics
- Include a quantitative assessment of consistency (e.g., high/medium/low) for different voice dimensions
- Prioritize actionable recommendations that can be implemented by content creators
- Include 3-5 "do's and don'ts" that exemplify the brand voice guidelines

## Example Usage Guidelines
To use this prompt effectively:
1. Provide 3-5 content samples from the same brand (e.g., website copy, social media posts, email newsletters, blog posts)
2. Include content pieces from different channels or created by different team members to identify potential inconsistencies
3. If available, include any existing brand guidelines or voice descriptions for reference
4. Specify if there are particular aspects of voice consistency you're most concerned about (e.g., formality level, technical language usage, emotional tone)

When submitting content for analysis, order the samples chronologically or by channel type for the most effective comparative analysis.

Scaling Voice Consistency

What’s powerful about this approach is it scales with your content volume. Whether you have two writers or twenty, the brand voice remains consistent. It’s like having a style editor reviewing every piece.

Common Questions About Brand Voice

How many content samples do I need for voice analysis?

3-5 content samples from different channels or team members provide enough data to identify patterns and inconsistencies.

How often should I review brand voice consistency?

Quarterly reviews work well for most teams, or whenever you onboard new content creators.

Enhance Readability

The data is clear: simpler content performs better across nearly all metrics.

Using AI as your readability coach is game-changing:

Readability Enhancement Process

  1. Use LLMs to help you build a simple Python script that analyzes your content using readability metrics
  2. Using the script, analyze all pieces of content you have
  3. Use AI to suggest specific basic simplifications without losing meaning

Important Implementation Note

What is crucial here is to not use LLMs to identify readability issues because they’re not great at it. Instead, use them to only generate the script and then feed the result back to the LLM for simplification, otherwise, you won’t be happy with the results.

Common Questions About Readability

What readability score should I aim for?

Aim for a Flesch-Kincaid grade level of 8-10 for most business content. This ensures accessibility without dumbing down your expertise.

Which readability metrics are most important?

Focus on Flesch Reading Ease, Flesch-Kincaid Grade Level, and average sentence length. These provide the most actionable insights.

Mine Customer Reviews

Your customers are telling you exactly what they care about and how they talk about it. It’s never been easier to use this knowledge to create content.

Customer Review Mining Process

  1. Gather feedback from reviews, support, and social media. Save everything in one big .txt file
  2. Use AI to turn it either into .md or XML formats, and open a new chat
  3. Prompt AI to identify pain points, customer terminology, and similar themes
  4. Create content that directly addresses these issues

Why Customer Language Matters

This could be very effective to create high-quality support content that really solves customer pain points using the exact language your customers use when searching for solutions.

Common Questions About Review Mining

How many customer reviews do I need for meaningful analysis?

Start with at least 100-200 reviews or feedback pieces to identify recurring themes and language patterns.

What’s the best format for organizing customer feedback?

A simple .txt file works well initially. You can ask AI to convert it to .md or XML formats for better structure and analysis.

Take Action

What makes these AI implementation strategies so powerful is that they don’t require massive investments or complex technical setups. Most can be implemented with tools you already have, plus an LLM platform subscription.

The opportunity is massive right now because most companies are using AI ineffectively – either creating robotic content or relying on it too heavily. By using AI strategically to enhance human expertise rather than replace it, you can create content that stands out in an increasingly crowded space.

  1. https://developers.google.com/search/blog/2023/02/google-search-and-ai-content ↩︎

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