Boost Marketing Intelligence with Amazon Bedrock and LLMs for Smarter Content, Sentiment Analysis, and Campaign Evaluation

Boost Marketing Intelligence with Amazon Bedrock and LLMs for Smarter Content, Sentiment Analysis, and Campaign Evaluation

Marketing campaigns drive business growth by attracting new customers and keeping existing ones engaged. But launching a campaign is just the start. To get the most value, marketers need clear insights into how their efforts perform and where to improve.

This article outlines a practical approach using generative AI and large language models (LLMs) via Amazon Bedrock. This managed service offers access to leading foundation models through a simple API. By combining social media sentiment analysis, AI-generated content, and campaign effectiveness evaluation, marketers can make informed decisions that enhance campaign results.

Key Marketing Challenges

• Measuring public sentiment about brands and campaigns accurately
• Creating engaging, targeted content for multiple channels
• Predicting campaign success before launch
• Optimizing marketing spend without sacrificing impact

This solution addresses these challenges by integrating sentiment analysis, content creation, and campaign evaluation into a seamless workflow powered by LLMs.

How the Solution Works

Social media data flows in through real-time ingestion pipelines. Amazon Bedrock’s LLMs then:

• Analyze the sentiment of social media posts
• Generate customized marketing content based on those insights
• Evaluate the potential effectiveness of campaigns

Processed data is stored for reporting and visualization, giving marketers clear metrics and trends. Models like Anthropic’s Claude 3.5 Sonnet, Amazon Nova Pro, and Meta Llama 3.2 3B Instruct power content creation that fits business needs and audience preferences.

Getting Started

You’ll need an AWS account with the right permissions to implement this solution.

Step 1: Collect Social Media Data

• Identify hashtags and keywords relevant to your brand and campaign
• Connect to social media APIs (such as Bluesky)
• Set up data storage and real-time streaming pipelines

Step 2: Perform Sentiment Analysis

Use an LLM to classify social media posts as positive, negative, or neutral, along with explanations for each classification. This insight reveals how your audience feels about your brand and campaign in real time.

Here’s a simple Python example using the AWS SDK to prompt an LLM for sentiment analysis:

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def analyze_sentiment(text, model_id={selected_model}):
    prompt = f"""You are an expert AI sentiment analyst with advanced natural language processing capabilities. Your task is to perform a sentiment analysis on a given social media post, providing a classification of positive, negative, or neutral, and detailed rationale.

Inputs:
Post: "{text}"

Instructions:
1. Carefully read and analyze the provided post content.
2. Consider the tone, word choice, emotional indicators, context, and any sarcasm.
3. Classify sentiment as Positive, Negative, or Neutral.
4. Explain your reasoning with specific references to the post.

Provide your response in this format:
Sentiment: [Positive/Negative/Neutral]
Explanation: [Detailed explanation]

Remember to be objective and base your analysis solely on the post content.
"""
    body = json.dumps({"prompt": prompt, "max_tokens_to_sample": 500, "temperature": 0.5, "top_p": 1})
    response = bedrock.invoke_model(modelId=model_id, body=body)
    return json.loads(response['body'].read())

Step 3: Generate Content and Evaluate Campaign Effectiveness

Feed campaign details like target audience, messaging, and channels into an LLM to create relevant social media posts, ad copy, or emails. Then, use another LLM to assess how well the content aligns with campaign goals and audience sentiment.

Example of generating marketing content:

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def generate_marketing_content(product, target_audience, key_message, tone, platform, char_limit, model_id={selected_model}):
    prompt = f"""You are an expert AI social media copywriter. Create a compelling post based on:

Product: {product}
Target Audience: {target_audience}
Key Message: {key_message}
Tone: {tone}
Platform: {platform}
Character Limit: {char_limit}

Follow platform best practices, include hashtags or emojis if appropriate, and add a call-to-action.

Format:
Generated Post: [Your post]

Be creative, concise, and impactful.
"""
    body = json.dumps({"prompt": prompt, "max_tokens_to_sample": 300, "temperature": 0.7, "top_p": 0.9})
    response = bedrock.invoke_model(modelId=model_id, body=body)
    return json.loads(response['body'].read())

Example of analyzing campaign effectiveness:

import boto3
import json

bedrock = boto3.client('bedrock-runtime')

def analyze_campaign_effectiveness(campaign_objectives, sentiment_summary, marketing_content, model_id={selected_model}):
    prompt = f"""You are an expert AI marketing analyst. Evaluate this marketing campaign based on:

Campaign Objectives: {campaign_objectives}
Positive Sentiments: {sentiment_summary['praises']}
Negative Sentiments: {sentiment_summary['flaws']}
Marketing Content: {marketing_content}

Instructions:
1. Compare content with objectives.
2. Consider positive and negative sentiments.
3. Provide an Effectiveness Score (1-10).
4. Give detailed analysis with strengths, improvements, and recommendations.

Format:
1. Effectiveness Score: [Score]/10
2. Detailed explanation: [Your analysis]

Be objective and specific.
"""
    body = json.dumps({"prompt": prompt, "max_tokens_to_sample": 800, "temperature": 0.3, "top_p": 1})
    response = bedrock.invoke_model(modelId=model_id, body=body)
    return json.loads(response['body'].read())

How Evaluation Works

• Text vectorization: Converts campaign objectives, sentiment summaries, and generated content into numerical vectors.
• Similarity calculation: Measures alignment between content and campaign goals using cosine similarity or transformer-based models.
• Component scoring: Scores content alignment with objectives and sentiment incorporation.
• Weighted scoring: Combines scores to produce an overall effectiveness rating.
• Interpretation: Offers a clear explanation with actionable recommendations.

Example output might look like this:

• Effectiveness Score: 8/10
• Strengths: Engaging content, clear benefits, strong initial interest.
• Improvements: Adjust tone for brand fit, reinforce call-to-action, balance messaging focus.

Benefits of This AI Approach

• Cost savings: Avoid spending on ineffective campaigns.
• Valuable insights: Generate data that can guide internal decisions or be shared externally.
• Targeted marketing: Align content closely with audience preferences.
• Faster decisions: Use AI to quickly evaluate and adapt strategies.
• Better ROI: Optimize campaigns to drive higher revenue and engagement.

Things to Keep in Mind

• Data quality: Input data must be accurate and diverse for best results.
• Model tuning: Customize prompts and models to fit your brand voice and industry.
• Ethics: Respect privacy and avoid bias when analyzing public data.
• Integration: Plan for how AI insights fit into existing marketing workflows.
• Prompt refinement: Experiment with prompt phrasing to get useful AI outputs.

Looking Ahead

Generative AI continues to grow beyond text. Image and video generation models, like those from Stability AI and Amazon Nova, are becoming available through services like Amazon Bedrock. These tools will let marketers create personalized, eye-catching visuals alongside tailored messages.

Success depends on combining AI tools with strong prompt skills and data analysis to keep refining campaigns. Marketers who build these capabilities will stay ahead and create more impactful campaigns.