genAI and google cloud ML to get actionable insight

To operate a thriving business, it is important to have a deep understanding of your customers’ needs and extract valuable insights from their feedback. However, the journey of extracting actionable information from customer feedback is a formidable task. Examining and categorizing feedback can help you discover your customers’ core pain points with your products, but can become increasingly challenging and time-consuming as the volume of feedback multiplies.

Several new generative AI and ML capabilities in Google Cloud can help you build a scalable solution to this problem, enabling you to glean insights from your customer feedback and identify the product issues that are top-of-mind, even when that feedback is unstructured.

In this blog post, we’ll walk through an example of building such a solution for transforming raw customer feedback into actionable intelligence.

Our solution will segment (or cluster) a large dataset of customer feedback and summarize the narratives associated with each logical segment. We’ll demonstrate this solution using sample data from the CFPB Consumer Complaint Database, which is available as a BigQuery Public Dataset. This dataset is a collection of complaints about consumer financial products and services, and represents a varied collection of feedback in an unstructured form.

The core capabilities of Google Cloud that we will use to build this solution are:

1. text-bison foundation model: a large language model that has been trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer all kinds of questions. It is part of Generative AI on Vertex AI.

2. textembedding-gecko model: a NLP technique that converts textual data into numerical vectors that can be processed by machine learning algorithms, especially large models. These vector representations are designed to capture the semantic meaning and context of the words they represent. It is also part of Generative AI on Vertex AI.

3. BigQuery ML K-means model: a clustering model for data segmentation. K-means is an unsupervised learning technique, so model training doesn’t require labels or to split data for training or evaluation.

We’ll be using BigQuery DataFrames to perform these ML and generative AI operations. BigQuery DataFrames is an open-source Python client that simplifies the interaction with BigQuery and Google Cloud by compiling popular Python APIs into scalable BigQuery SQL queries and API calls.

With BigQuery DataFrames, data scientists can move from data exploration to a production application by deploying their Python code as BigQuery programmable objects, while integrating with data engineering pipelines, BigQuery ML, Vertex AI, LLM models, and Google Cloud services. We’ll showcase the ML use cases here, and you can also check out more supported ML capabilities.

Building a feedback segmentation and summarization solution

If you want to follow along, you can make a copy of the notebook, Use BigQuery DataFrames to cluster and characterize complaints, which allows you to run this solution in Colab using your own Google Cloud project.

Load and prepare the data

In order to use BigQuery DataFrames, you’ll need to import its pandas library and set the Google Cloud project and location for the BigQuery session that it will use.

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