Claude powered Realtime, Hyper-personalized Marketing Collateral Generation - Solarplexus

Hyper-personalized, real-time marketing collateral generation​

Business Problem

  • SolarPlexus, a Swedish, SaaS marketing solutions provider, wanted to solve the problem that most marketers face. While resorting to mass marketing techniques to increase the outreach radius, they suffer from low conversions & impact. The reason is lack of personalization & value proposition for the user persona of such techniques.
  • The client wanted to build a product that helped the marketers bring in hyper-personalization to the world of mass marketing, ensuring double digit conversion & 60% higher ROI on marketing campaigns. 

About Solarplexus

SolarPlexus is a Sweden based SaaS Marketing solutions provider. Their product helps customers 10X their Marketing outreach conversion by building real-time, hyper-personalized marketing content, leveraging underlying user persona & intelligence.

Solution

With the advent of Generative AI & the capabilities to generate content on the fly, customized to the prompts & data we can generate, GoML’s GenAI consulting team worked with the customer to build a product vision, which allows their clients to build hyper-personalized communication for end customers, leveraging user persona real time, resulting 10X increased conversion. The solution followed a 3-step approach.

Claude powered Realtime, Hyper-personalized Marketing Collateral Generation - Solarplexus
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Architecture

Data Extraction:
  • Upload and Store Documents: Users uploads brand guidelines, tone of voice and picture bank which are stored in SuperBase bucket, ensuring centralized data access.
  • Structured Data Extraction: Utilizing RAG based approach to fetch the details from the documents uploaded by creating vector embeddings and storing vectors in FAISS Vector DB to extract structured data from unstructured documents.
  • Similarity Search: Perform similarity search using stored vector embeddings to identify key document elements like color, font, and tone.
  • Information Storage: Extracted data is stored in a dedicated SuperBase table for easy access and reference.  
Segmentation:
  •  User Data Upload: Users upload Excel files containing target audience information, which is stored in SuperBase table for analysis.
  • Clustering Analysis: Utilize clustering algorithms to segment user data based on demographics and behavior.  
  •  Data Storage: Store segmented data and corresponding cluster information within SuperBase tables.  
  •  Cluster Naming: Name clusters based on specific segmentation criteria for easy identification and analysis.  
  •  Segment Identification: Utilize clustering results to divide target audience into distinct segments for personalized marketing.  
  •  Accuracy Assurance: Ensure accuracy and efficiency in segmenting user data to facilitate effective marketing strategies.  
      
Asset Creation:  
  • Asset Data Collection: Gather user-provided data such as brand logos and extracted information for asset generation.  
  • DALL·E Utilization: Utilize DALL·E to generate marketing assets based on collected data and user preferences.  
  •  Segment Customization: Customize marketing assets for each segment using segment-specific information and user-provided images.  
  • Brand Element Integration: Incorporate brand elements like color and font into the asset generation process for consistency.  
    Process Consistency: Maintain consistency in asset generation process across segments to ensure brand coherence.  
  •  Personalized Asset Delivery: Deliver personalized marketing assets tailored to each segment’s characteristics for maximum impact and engagement.
Outcomes

0%

Increased conversion
of email outreach​

0%

Decreased effort in marketing
collateral design & creation

0

Overall increased brand effectiveness, with custom marketing collateral generation​

Technology Stack​