Elevate marketing intelligence with Amazon Bedrock and LLMs for content creation, sentiment analysis, and campaign performance evaluation – Amazon Web Services
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In the media and entertainment industry, understanding and predicting the effectiveness of marketing campaigns is crucial for success. Marketing campaigns are the driving force behind successful businesses, playing a pivotal role in attracting new customers, retaining existing ones, and ultimately boosting revenue. However, launching a campaign isn’t enough; to maximize their impact and help achieve a favorable return on investment, it’s important to understand how these initiatives perform.
This post explores an innovative end-to-end solution and approach that uses the power of generative AI and large language models (LLMs) to transform marketing intelligence. We use Amazon Bedrock, a fully managed service that provides access to leading foundation models (FMs) through a unified API, to demonstrate how to build and deploy this marketing intelligence solution. By combining sentiment analysis from social media data with AI-driven content generation and campaign effectiveness prediction, businesses can make data-driven decisions that optimize their marketing efforts and drive better results.
Marketing teams in the media and entertainment sector face several challenges:
To address these challenges, we explore a solution that harnesses the power of generative AI and LLMs. Our solution integrates sentiment analysis, content generation, and campaign effectiveness prediction into a unified architecture, allowing for more informed marketing decisions.
The following diagram illustrates the logical data flow for our solution by using sentiment analysis and content generation to enhance marketing strategies.
In this pattern, social media data flows through a streamlined data ingestion and processing pipeline for real-time handling. At its core, the system uses Amazon Bedrock LLMs to perform three key AI functions:
The processed data is stored in databases or data warehouses, then made available for reporting through interactive dashboards and generated detailed performance reports, enabling businesses to visualize trends and extract meaningful insights about their social media performance using customizable metrics and KPIs. This pattern creates a comprehensive solution that transforms raw social media data into actionable business intelligence (BI) through advanced AI capabilities. By integrating LLMs such as Anthropic’s Claude 3.5 Sonnet, Amazon Nova Pro, and Meta Llama 3.2 3B Instruct Amazon Bedrock, the system provides tailored marketing content that adds business value.
The following is a breakdown of each step in this solution.
This solution requires you to have an AWS account with the appropriate permissions.
The first step involves collecting social media data that is relevant to your marketing campaign, for example from platforms such as Bluesky:
The next step involves conducting sentiment analysis on social media data. Here’s how it works:
The following code is an example using the AWS SDK for Python (Boto3) that prompts the LLM for sentiment analysis:
This analysis provides valuable insights into public perception, providing marketers the information they need to understand how their brand or campaign is resonating with the audience in real time.
The following output examples were obtained using Amazon Bedrock:
The next step focuses on using AI for content creation and campaign effectiveness prediction:
The following is an example that prompts a selected LLM for content generation:
The following output examples were obtained using Amazon Bedrock:
The following is an example of code that prompts the selected LLM for campaign effectiveness analysis:
Let’s examine a step-by-step process for evaluating how effectively the generated marketing content aligns with campaign goals using audience feedback to enhance impact and drive better results.
The following diagram shows the logical flow of the application, which is executed in multiple steps, both within the application itself and through services like Amazon Bedrock.
The LLM takes several key inputs (shown in the preceding figure):
The process involves the following underlying key steps (shown in the preceding figure):
The following is example output for the marketing campaign evaluation:
The campaign effectiveness analysis uses advanced natural language processing (NLP) and machine learning (ML) models to evaluate how well the generated marketing content aligns with the campaign objectives while incorporating positive sentiments and avoiding negative ones. By combining these steps, marketers can create data-driven content that is more likely to resonate with their audience and achieve campaign goals.
This AI-powered approach to marketing intelligence provides several key advantages:
Though the potential of this approach is significant, there are several challenges to consider:
To avoid incurring ongoing charges, clean up your resources when you’re done with this solution.
The integration of generative AI and large LLMs into marketing intelligence marks a transformative advancement for the media and entertainment industry. By combining real-time sentiment analysis with AI-driven content creation and campaign effectiveness prediction, companies can make data-driven decisions, reduce costs, and enhance the impact of their marketing efforts.
Looking ahead, the evolution of generative AI—including image generation models like Stability AI’s offerings on Amazon Bedrock and Amazon Nova’s creative content generation capabilities—will further expand possibilities for personalized and visually compelling campaigns. These advancements empower marketers to generate high-quality images, videos, and text that align closely with campaign objectives, offering more engaging experiences for target audiences.
Success in this new landscape requires not only adoption of AI tools but also developing the ability to craft effective prompts, analyze AI-driven insights, and continuously optimize both content and strategy. Those who use these cutting-edge technologies will be well-positioned to thrive in the rapidly evolving digital marketing environment.
Arghya Banerjee is a Sr. Solutions Architect at AWS in the San Francisco Bay Area, focused on helping customers adopt and use the AWS Cloud. He is focused on big data, data lakes, streaming and batch analytics services, and generative AI technologies.
Dhara Vaishnav is Solution Architecture leader at AWS and provides technical advisory to enterprise customers to use cutting-edge technologies in generative AI, data, and analytics. She provides mentorship to solution architects to design scalable, secure, and cost-effective architectures that align with industry best practices and customers’ long-term goals.
Mayank Agrawal is a Senior Customer Solutions Manager at AWS in San Francisco, dedicated to maximizing enterprise cloud success through strategic transformation. With over 20 years in tech and a computer science background, he transforms businesses through strategic cloud adoption. His expertise in HR systems, digital transformation, and previous leadership at Accenture helps organizations across healthcare and professional services modernize their technology landscape.
Namita Mathew is a Solutions Architect at AWS, where she works with enterprise ISV customers to build and innovate in the cloud. She is passionate about generative AI and IoT technologies and how to solve emerging business challenges.
Wesley Petry is a Solutions Architect based in the NYC area, specialized in serverless and edge computing. He is passionate about building and collaborating with customers to create innovative AWS-powered solutions that showcase the art of the possible. He frequently shares his expertise at trade shows and conferences, demonstrating solutions and inspiring others across industries.
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