Understanding generative AI and its applications in marketing
Generative AI (Gen AI) is a transformative technology that uses machine learning models to create content autonomously. It can generate text, images, and even music that mimics human creativity. This capability is revolutionizing various industries by improving productivity and enabling the creation of highly personalized content. Marketers are benefiting from generative AI's ability to produce tailored content that resonates with specific target audiences.
Applications of generative AI in marketing
• Personalization:
Generative AI can create highly customized content, ensuring that marketing materials are tailored to individual preferences and needs, which boosts engagement and conversion rates.
• Efficiency:
Gen AI significantly reduces the time and effort required to produce content, allowing marketers to focus on strategy and other high-value tasks.
• Scalability:
Generative AI enables the production of large volumes of content quickly, making it easier for companies to maintain a consistent and dynamic presence across multiple channels.
About our client
Our client is a Design-and-Make platform provider that empowers people to design and create anything from buildings and cars to products and entertainment media.
Serving a wide range of industries, they faced challenges in creating targeted and personalized marketing content clusters for specific accounts. These challenges included time constraints, lack of resources, inconsistent brand voice, and difficulty in identifying relevant content. Therefore, they sought an AI-based solution to create personalized content clusters based on industry type and account.
Challenges being faced
• Personalized content challenges:
Unable to deliver personalized content to individual customers or specific target segments because it was time-consuming and resource intensive.
• Engagement and conversion issues:
Failing to resonate with specific audience segments, leading to lower engagement and missed conversion opportunities due to using generic content.
Implementing Generative AI as the solution
Phases of the approach
The implementation approach can be broken down into three phases (as summarized in the diagram below):
• Phase 1:
Input Content Classification, where data is categorized and tagged for relevance.
• Phase 2:
User Query Classification; the system interprets user inputs to understand specific content requests.
• Phase 3:
Content Retrieval and Generation involves generating tailored content based on the classified data and user queries.
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Figure 1: Phased approach for the solution
implementation
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Considerations in this approach
• Choose the right
LLM for classification accuracy.
• Consider
pre-training on domain-specific data for improved performance.
• Incorporate
techniques like NER for precise keyword extraction.
• Fine-tune the LLM
with labeled data for improved performance.
• Implement a
robust cross-questioning mechanism to capture essential information.
• Select an
appropriate vectorization technique for accurate retrieval.
• Optimize the
hybrid search strategy for retrieving relevant documents.
• Include
re-ranking methods for more accurate retrieving of relevant documents.
Considerations in this approach
• Choose the right
LLM for classification accuracy.
• Consider
pre-training on domain-specific data for improved performance.
• Incorporate
techniques like NER for precise keyword extraction.
• Fine-tune the LLM
with labeled data for improved performance.
• Implement a
robust cross-questioning mechanism to capture essential information.
• Select an
appropriate vectorization technique for accurate retrieval.
• Optimize the
hybrid search strategy for retrieving relevant documents.
• Include
re-ranking methods for more accurate retrieving of relevant documents.
Diving into the details
To implement the updated generative AI solution, we first onboarded various data types, including text, images,
PDFs, webpages, emails, and more. These data types were mapped with relevant keywords and tags. Next, a chatbot was
developed to handle user inputs and generate specific content for email campaigns and landing pages tailored to
individual accounts.
With this AI-driven chatbot, marketers can quickly create personalized, account-specific content clusters. This
improves the efficiency and effectiveness of their campaigns. This approach also ensures that the generated content
aligns perfectly with the needs of targeted accounts, improving engagement and conversion rates.
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| Figure 3: Detailed solution flow diagram
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About the solution architecture
The process begins with input documents being processed through text extraction and pre-processing
steps, including text chunking, categorization, and vector embeddings. These processed document chunks
are stored in a Weaviate Vector Database. User queries are then processed to extract entities &
tasks and pre-processed to check for required entities. A retrieval engine fetches relevant documents
using a hybrid ranking approach. The final processed input object is then used to generate a relevant
email response using Azure OpenAI services.
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Figure 4: Architecture diagram |
Benefits of this approach
• Optimal chunking size with overlapping improves
efficiency.
• The JSON output provides a structured format
for subsequent information retrieval.
• The cross-questioning mechanism ensures the
completeness of critical data for accurate content generation.
• The hybrid search and filtering approach
ensures retrieved content is semantically relevant.
• This search and filtering method also makes
sure the content retrieved matches the user's specific needs.
Benefits of this approach
• Optimal chunking size with overlapping improves
efficiency.
• The JSON output provides a structured format
for subsequent information retrieval.
• The cross-questioning mechanism ensures the
completeness of critical data for accurate content generation.
• The hybrid search and filtering approach
ensures retrieved content is semantically relevant.
• This search and filtering method also makes
sure the content retrieved matches the user's specific needs.
Benefits of the Gen AI solution
• Streamlined workflows:
Gen AI can seamlessly integrate into content creation processes, saving time and resources by automating routine tasks.
• Improved creativity:
The solution unlocks new avenues for generating personalized and engaging content. This offers fresh perspectives and ideas that might not be obvious to human creators.
• Effective marketing campaigns:
This efficiency leads to more effective and targeted marketing campaigns. Doing so ensures that the right content reaches the right audience at the right time.
• Efficiency and speed:
Organizations report a marked increase in the speed of content creation. Quantitative results show substantial improvements in production times.
• Competitive advantage:
By breaking free from traditional methods, Gen AI facilitates the exploration of innovative formats and approaches, providing an edge in the market.
Interested in improving your marketing operation with Gen AI? Contact Sales@MAQSoftware.com today to see how we can elevate your business and improve your customer relationships.