Harness the Power of BigQuery for Storing and Analyzing Contact Center Data

Introduction

Contact Center Data Deluge

Modern Contact center is a rapid data stream. Each action like phone calls, emails, chats and social media generates a treasure trove of information. This data holds the key to understand customer needs, improve operations and overall better improvements in customer experience. Still organizations find it a challenge to efficiently capture, store and analyze this data deluge.

Contact Center - The Power of BigQuery

Google Cloud BigQuery is a fully managed, serverless data warehouse that offers game-changers for the storage and analysis of the vast amount of data. How BiqQuery can transform Contact Center Data Management and Analytics.

Why BigQuery is perfect for Contact Center?

BigQuery is a powerful platform that was specially built to address the challenge of contact center data management and analysis. Accommodates huge chunks of data and scales accordingly without difficulty.

Real-Time Processing: Generates insights instantly for decision-making based on continuous stream-of-data analysis. Pay-as-you use that will only cost money as used.

Advanced Analytics Capabilities:BigQuery’s powerful SQL-based querying, along with the machine learning, enable sophisticated analysis:

Agent performance optimization:Monitor agent performance metrices (handle time, customer satisfaction, first-call resolution) for potential areas for enhancement.

Integration with Other Google cloud Services:BigQuery is integrated easily with other Google Cloud services, such as Google Cloud AI Platform, allowing you to build and deploy machine learning models directly on your contact center data.

How to store Contact Center Data in BigQuery?

Storing contact center data in BigQuery has different steps in which structured procedures allow ingesting data, properly organize it and make it readily accessible for analysis.

There is so much diversity in data formats: call logs, transcripts from chats, agent performance metrics, customer satisfaction scores, etc. Follow the steps as set forth below to store that kind of data in BigQuery.

Step 1 Identify the Data Sources

Understand all Data sources in your contact center. These may include

• Call Management System:Call duration, first call resolution time, call recording and transfer calls agent hand off customer interactions.
• Chat and Messaging platforms:Customer chat transcripts and agent side responses.
• Workforce Management Tools:Agent Schedules, In/Out times and productivity metrices.

Each data source may use different formats like CSV and JSON, for which BigQuery supports.

Step 2: Data Preparation for BigQuery

Ensure data from different sources follows a standardized schema to make querying and analysis easier. Define the field names, data types, and relationships across the datasets. Eliminates duplicates, null or irrelevant data before ingestion.

Example:Removing incomplete call records or false customer entries.

1. Schema Design -Sample Data Structure call logs Schema
2. Call ID:Unique Identifier of the call (String)
3. Timestamp:Date and Time of the call log (Timestamp)
4. Call Duration:Call in seconds (Integer)
5. Agent ID:Identifier of the agent handling the call (Sting)
6. Customer ID:This is unique identifier for customer (String)
7. Resolution Status:Status of the call (example: “Resolved”, “Escalated”)

Step 3: Optimize Storage in BigQuery

Store related datasets in separate tables below the unified dataset. Example: dataset.contact_center_data.call_logs

Step 4: Secure the Data

BigQuery provides the strongest security features to protect contact center data

1. Access Control:Use IAM role to restrict access to datasets or tables.
2. Encryption:All data is encrypted at rest and when in transit.
3. Compliance:Ensure that your data storage is compliant with industry standards such as GDPR, HIPAA, or ISO 27001.

Step 5: Test and Validate

Run test queries to ensure data integrity and schema alignment. Validate that ingestion pipelines handle edge cases, such as missing or malformed data.

Example: Loading call log Data into BigQuery

Enable API’s

Make sure the following APIs are enabled for your Google Cloud project:

1. Dialogflow CX API
2. BigQuery API
3. Cloud Logging API

Create a BigQuery Dataset

Go to BigQuery in the Google Cloud Console.

Create a Table in BigQuery for Storing Logs

Once you have a BigQuery dataset, you will need a table to store your Dialogflow CX logs. While Cloud Logging exports logs to BigQuery, you can define the schema of the table where the logs will be stored.

Here’s how to create a table in BigQuery for your logs:

1. Go toBigQuery Google Cloud Console.

2. Navigate to the dataset you created earlier.

3. Click on Create Table.

4. In the Create Table page:

1. Source: Select Google Cloud Logging if you're streaming logs directly
2. File Format: If uploading, choose JSON or CSV depending on your log export format.
3. Schema: Define the schema manually or use an auto-detected schema based on the logs.

A sample schema for logging interaction data might look like this:

Once the schema is defined, click Create Table.

Configure Dialogflow CX to Export Logs to BigQuery

Create a Sink in Logging Router

1. From the main navigation menu, select "Logging"->"Logs Router".
2. Alternatively, you can use the search bar at the top to search for "Logs Router"or"Logging".
3. Click on the "Create Sink" button on the Logs Router page.

4. Define Sink Details

   Sink Name: Enter a name for the sink. This is how you will identify the sink later.

   Sink Description (optional): Add an optional description for the sink.

5. Set the Sink Destination
   

   Choose where you want to send the logs. The available destinations are

   • Cloud Storage: For storing logs in buckets.
   • BigQuery: For exporting logs to BigQuery datasets.
   • Cloud Pub/Sub: For publishing logs to a Pub/Subtopic.

BigQuery Data Storing Architecture

BigQuery has an extremely scalable and efficient architecture for data storage and analysis in contact centers. Below is an explanation of the main components and architecture used to store such data: telephony systems, chat platforms, email and ticketing systems and social media feedback systems.

Key features of BigQuery for Contact Center Data

BigQuery is built to deal with huge amounts of data. In contact centers, big volumes of call recordings, transcripts, and customer interactions generate vast amounts of data. The service enables the rapid analysis of complex data, which enables quick insights into customer behavior, agent performance and operational trends.

Easy-to-Use: It is accessible to analysts who are familiar with database querying using standard SQL.

Cost-Effective: Pay-per-query ensures that you pay only for the resources consumed by analysis.

Conclusion

BigQuery gives contact centers the power to tap into their data for operational efficiency and better customer experience. Centralizing data storage, enabling advanced analytics, and integrating with a suite of Google Cloud tools, BigQuery can transform how contact centers operate, ensuring they stay competitive and responsive in an increasingly data-driven world. With CloudSens you can unlock the full potential of BigQuery to build smarter, more responsive and future-ready contact center operations.