How does a composable CDP use large language models (LLM)?

Data collection is often considered just as precious as gold in the business world, and companies spend substantial resources to get it. But the challenge often lies in leveraging that data and providing access to the teams that need it to drive business outcomes like revenue and customer loyalty.  

Traditionally, customer data platforms (CDPs) have been the go-to choice for businesses wishing to unify first-party data and activate it for audience segmentation. This is because CDPs offer standard identity resolution modeling features to help businesses unify data from different sources into a single customer profile and then activate that data across various marketing destinations. 

But in the last few years, many organizations have realized that keeping a traditional CDP or packaged CDP isn’t the best approach, especially if they’re also storing data in an existing cloud data warehouse. Among other challenges, these traditional CDPs pose inherent data accuracy and security issues, as they require copying data from various sources. 

This revelation has paved the way for composable CDPs, which sit on top of a centralized cloud data warehouse, allowing companies to quickly activate their data from a single source of truth without needing to copy or store data in a separate tool.

Another primary advantage of composable customer data platforms is their ability to make use of a variety of large language models (LLM), including in-house models. This allows for quicker campaign analysis and more personalized, effective marketing campaigns. Because the composable CDP sits on top of the cloud data warehouse, you can use the LLM model that works best with your data cloud. 

Let’s take a closer look at how LLMs can be used alongside composable CDPs.

Large language models explained

A large language model is a type of generative artificial intelligence that uses deep learning algorithms to process and understand vast amounts of text data. This enables it to generate human-like text, translate languages, answer questions, and perform other natural language processing tasks with high levels of accuracy. Essentially, LLMs are the foundation/structure on which generative AI tools are built. 

LLMs have become increasingly common, with a significant number of organizations deploying generative AI products powered by LLMs, such as OpenAI, Google, and Meta. 

Examples of popular LLMs include:

• GPT-4

• Gemini

• LLaMA 2

• Claude 3

The ‘large’ aspect of LLMs refers to the vast amount of training data they process along with the large number of parameters they contain, which is necessary for precisely capturing the nuances of the human language. 

Before LLMs can optimize tasks with effective results, they must first undergo a fine-tuning process. This process typically includes the following steps:

• Establish a purpose - The specific use case or goal of the LLM will impact which data sources it uses to learn language. The purpose and use case can be fine-tuned and include additional elements as the LLM evolves.

• Pre-training - Because an LLM needs a robust dataset for training, marketing teams and other users must gather and clean the data to ensure it is standardized for consumption.

• Tokenization - This step entails breaking the text within the dataset into smaller units to help the LLM understand sentences, paragraphs, and documents by first learning words and subwords.

• Select an infrastructure - For training to be effective, the LLM requires a powerful computer or cloud-based server. These computational resources are imperative to LLM training.

• Initial training - Data teams can create parameters for the LLM training process and include batch sizes or learning rates for more effective results.

• Ongoing training and fine-tuning - LLM training is a continuous process and regular integration of new data models with the LLM is necessary to assess its output. Existing parameters, including data governance, can be adjusted or new ones can be included to improve results.

Composable CDPs and LLMs

How do composable CDPs and LLMs work together? Composable CDPs use the AI model’s ability to generate campaign recommendations and insights directly from the customer data stored within the cloud data warehouse in real time. This allows for highly customized marketing strategies based on complex data patterns and customer profiles. 

For example, you use a composable CDP to create a customer segment from your data cloud and launch a Google Ad campaign to that segment. As the campaign runs, the composable CDP re-ingests data from that campaign back into your data cloud. From there the LLM can quickly analyze the campaign results — likely faster than a human team — and provide recommendations for improving results for future campaigns.

Key factors of how CDPs use LLMs include:

• Data activation and access - LLMs can directly access vast amounts of customer data to generate relevant insights. 

• Customer segmentation - By analyzing customer data with LLMs, composable CDPs can identify complex customer segments for targeted marketing campaigns.

• Predictive analytics - Using data in the cloud data warehouse, LLMs can be used to predict future customer behavior based on past interactions and demographics, enabling proactive engagement strategies. 

• Natural language processing (NLP): LLMs can process requests in natural language. For example, asking it to find a specific customer segment in the data warehouse, or to provide a certain type of analysis on campaign results.

Choosing the right model

While they might perform similar tasks, all LLMs are not created equally. The primary difference lies in the specific datasets they are trained on, which can determine their ability to handle specific contexts and messages. 

The LLM you choose may also depend on the cloud data warehouse where you store your customer data.  For example, GrowthLoop uses several LLMs — all based on the cloud partner our customer chooses. BigQuery users can take advantage of the Gemini model, and Snowflake users can rely on the Cortex model.

If your team has built an in-house model, a composable CDP can tap into that, as well, allowing you to leverage your team’s expertise and unique data set to launch smarter, more successful campaigns. 

Discover more at GrowthLoop

GrowthLoop is a composable CDP with a deep understanding of the struggles marketers face when attempting to access and activate customer data. Instead of resorting to hiring more people to write SQL queries or other inefficient, expensive, and time-consuming approaches, GrowthLoop empowers companies to activate customer data directly for marketing, sales, and customer service uses.

GrowthLooop’s product, The Loop, applies AI to the data cloud and enables businesses to understand how specific marketing channels and audiences impact their business outcomes. The Loop ingests campaign data back into the cloud and uses AI to reveal campaign insights and future recommendations, allowing businesses to strategize for maximum ROI. 

Contact us today to learn more about our SaaS products and to start collaborating with GrowthLoop’s AI for more effective results and optimized workflows.