GenAI Based Creative Intelligence Accelerator: 6-Wk Implementation

Currently enterprise are looking at GenAI from a functional standpoint. The core concept is to leverage data in order to augment processes, analytics and content to support an organizations objectives. GenAI is being extensively used to generate new revenue streams, deliver differentiated customer experience and modernize internal processes.

Creatives play an extremely important role in marketing for boosting audience engagement. As large CPG companies work with different content partners, there is no single point of truth to track all creatives for performance, quality, reusability, etc. Furthermore, it is important to understand the real impact or effectiveness of a creative across campaigns, channels/platforms, time, etc. The following factors are to be considered:

1. Tackling concerns around brand compliance for different content partners
2. Quickly checking the performance of one creative and moving to a different version if it is not working well
3. Speeding up the content creation process

Sigmoid has built a GenAI driven Creative Intelligence Accelerator that solves all the above challenges. It helps in the identification of the best and worst performing creatives, along with the necessary drivers of success or failure. Helping content creators come up with stock ideas, images, content and templates, it speeds up the creative generation time for new marketing activities. The solution also comes with a data driven system that enables selection and optimization of the creatives over time as well.

Benefits ensured:

1. Multiple creatives can be generated and tried in parallel
2. 3x efficiency achieved by designers
3. Optimization of non-performing ones, resulting in considerable dollar savings

The following Azure workloads have been used in developing the above mentioned solution:

1. Azure Machine Learning to train deep learning models on large datasets, manage model versions, and deploy them
2. Azure OpenAI to harness powerful NLP models like GPT, enabling it to generate creative content, automate idea generation, and enhance creative processes with human-like text generation
3. Azure Data Lake Storage, serving as the underlying storage layer
4. Azure Databricks for large-scale data processing and analytics, supporting the ingestion, transformation, and analysis of vast datasets that feed into the creative intelligence models.
5. Azure Kubernetes Service to deploy and manage containerized applications, ensuring that the infrastructure scales according to demand