Customer Feedback Analysis (Prioritization)

bhavyekhetan · 11-27-2023 12:07 AM

Hello Everyone

I am working as a Fractional Head of Growth at a FinTech. I want to understand how I can analyze customer data. Currently, we use Zendesk for ticketing. This flows to Snowflake + Looker for insights.I need a better way to link feedback to impact metrics like CSAT, churn, expansion, referrals, etc. aka how important is this piece of feedback.

Can I do this with BigQuery? We can't shift from Snowflake. If not BigQuery, how else can I do this? Also happy to build something custom in-house if needed.

ms4446

Yes, you can effectively use Google Cloud's BigQuery to analyze customer data and link feedback to key impact metrics like CSAT, churn, expansion, and referrals. BigQuery is a robust data warehouse capable of handling large volumes of data, and it can be integrated with your existing Snowflake and Looker setup to provide deeper insights and improve customer experience.

Steps to Use BigQuery for Customer Data Analysis:

Data Integration:
- From Zendesk to Snowflake: Continue leveraging your existing pipeline to move data from Zendesk to Snowflake.
- From Snowflake to BigQuery: Implement a data transfer process from Snowflake to BigQuery, if necessary. This can be achieved through Google Cloud Dataflow or other ETL tools, ensuring that BigQuery complements Snowflake in your data strategy.
Data Transformation and Cleaning:
- Perform necessary data transformations and cleaning either within Snowflake or after the data is loaded into BigQuery. This includes deduplication, error correction, and formatting.
Data Modeling:
- Create efficient data models in BigQuery to facilitate easy querying and analysis. This step is crucial for organizing your data for insightful analytics.
Data Analysis:
- Utilize BigQuery's SQL capabilities for querying and analyzing data. For predictive insights, leverage BigQuery ML to build models that can, for example, predict customer churn or analyze sentiment.
Visualization:
- Integrate BigQuery with Looker or other visualization tools like Google Data Studio to create comprehensive dashboards and reports for better data interpretation and decision-making.

Specific Applications in BigQuery:

Feedback Sentiment and CSAT Analysis: Analyze the correlation between customer feedback sentiment and CSAT scores to identify areas for service improvement.
Churn Risk Identification: Use BigQuery to pinpoint customers at risk of churn by examining their feedback, usage patterns, and other relevant data.
Feedback's Impact on Product Development: Assess how customer feedback influences product development priorities and feature enhancements.

Building a Custom Solution:

If a more tailored solution is needed, consider developing a custom data pipeline using Google Cloud products. This approach offers greater control over data handling and allows for customization to meet specific analytical needs.

Additional Considerations:

Data Governance and Security: Ensure adherence to data governance standards and maintain high levels of security, especially when handling sensitive customer data.
Change Management and Training: Be prepared for potential changes in processes and ensure your team is adequately trained to use BigQuery and other integrated tools effectively.
Cost and Resource Management: Keep an eye on the costs associated with using BigQuery and other Google Cloud services, optimizing resource usage for cost-effectiveness.

Helpful Resources:

By integrating BigQuery with your existing Snowflake and Looker setup, you can harness its powerful analytics capabilities to gain deeper insights into customer feedback and its impact on key business metrics. Whether you choose to enhance your current data infrastructure or build a custom solution, BigQuery offers the flexibility and power needed for advanced data analysis.