Introduction
In today's data-driven world, businesses rely on business intelligence (BI) systems to gather insights that fuel better decision-making. When integrated with powerful tools like Ender Turing—a Speech Analytics and Auto Quality platform designed for contact centers and support teams—you can unlock a deeper understanding of customer interactions, identify product, sales, and service improvements, and streamline performance evaluations. You can also update customer information in the CRM system to provide better service in the future.
Key Use Cases for Integrating Ender Turing with BI, DWH, and CRM Platforms
Deep Customization of Dashboards and Reports
Integrating Ender Turing with your BI system allows you to create highly customized dashboards and reports that are deeply customized for your specific business process. You can track specific KPIs such as scheduled made by locations and rejections on products on specific campaign dates, add additional business logic and data, and tailor dashboards and reports to your organization's needs. By blending Ender Turing data with other business metrics, you can gain a comprehensive view of your operations through a prism of customer conversations and make informed decisions based on real-time insights.
Customer Data Enrichment in CRM
With Ender Turing’s speech analytics capabilities, you can enrich customer data within your CRM by adding insights from customer interactions, such as sentiment scores, keyword mentions, and call outcomes. These insights provide a more detailed profile of each customer, enabling personalized engagement, targeted marketing, and better customer support. Integrating this enriched data into your BI system ensures decision-makers have a 360-degree view of customer behavior.
Advanced Visualization
Ender Turing’s data, when integrated with a BI system, enables advanced visualizations that go beyond basic charts and tables. You can create dynamic visualizations such as heatmaps, trend lines, and others to monitor customer interaction insights and performance metrics over time. These visualizations help stakeholders quickly identify patterns, outliers, and opportunities for improvement in customer service operations.
3rd Party Systems Enrichment
By integrating Ender Turing’s insights into third-party systems such as product development or scheduling platforms, you can ensure that key teams across your organization can access relevant customer feedback and interaction data. For example, product teams can use call data to refine product features, while scheduling systems can optimize service/product availability based on real customer requests. This enrichment helps align business operations with customer needs and drives strategic improvements across departments.
Incorporating Customer Conversation Data into Your Company Data Lake
Another robust use case is integrating Ender Turing's conversation data into your company’s data lake. By incorporating call transcripts, topics and patterns data, summaries, quality metrics, and others into your centralized data repository, you create a robust foundation for advanced analytics. This data can be leveraged to build predictive models, identify long-term customer trends, and improve strategic decision-making. A unified data lake with customer conversations enables businesses to unlock new insights by correlating interaction data with other key business metrics, driving innovation and enhancing the customer experience.
Integration Options
API Integration
Ender Turing offers REST APIs that allow seamless integration with BI, DWH, and CRM platforms, enabling businesses to analyze, visualize, and report on interaction data, quality evaluations, and customer sentiments. This guide will explore effectively integrating Ender Turing into your BI, DWH, and CRM platforms to boost your organization's data capabilities.
Thanks to the API first approach, which Ender Turing incorporates into the development lifecycle, any data visible to the end user in Ender Turing can be fetched through API.
Below is the step-by-step breakdown. You can create a middleware script (pipeline) or configure your BI system to pull this data from Ender Turing’s API periodically. You can also use already implemented middleware pipelines as an example and inspiration.
Step 1: Set Up Ender Turing API Access
Obtain API Credentials: If you are an Administrator, follow this guide to create an API user. If you are not an Administrator, contact your administrator or Ender Turing’s support team to obtain your API credentials.
Permissions: Follow this guide to understand the default API user role permissions. If you need additional permissions, you can create a new role or add permissions by navigating System-> Roles & Permissions.
Step 2: Identify Key Data to Extract
Ender Turing’s API allows you to retrieve several types of data:
Analitycs data: Extract topics, tags, summaries, and other analytical metadata on the conversation level.
Quality Scores: Retrieve auto-evaluations and quality metrics, including compliance with scripts, greetings, and other KPIs.
Transcripts: Fetch call transcripts, which can be analyzed for keywords, call duration, and customer-agent dialogues.
Performance and operational metrics: Retrieve durations, load, efficiency, and other metrics.
Step 3: Data Transformation
Once the data is extracted from Ender Turing’s API, it’s often in JSON format. You’ll need to:
Parse JSON Data: Transform it into a structured format like CSV, Excel, or SQL-compatible tables.
Map Fields to Your BI Platform: Ensure relevant data fields (like sentiment, quality scores, etc.) align with your BI system's data schema.
You can automate this transformation process using tools like Python, ETL (Extract, Transform, Load) scripts, or connectors built into your BI platform.
Step 4: Load Data to BI, DWH, or CRM Platform
Choose where you will Load data and obtain credentials.
Load data to BI, DWH, or CRM Platform
Use BI native Connectors: Most BI and CRM platforms offer native connectors for REST APIs, which you can configure to import data from Ender Turing. For example, Power BI’s Web connector or Tableau’s JSON connector can be used to load data.
APIs: If no connector is available, you can always use APIs from the BI and CRM platforms.
Middleware DB: Some customers prefer to load data to the middleware DWH database instead of the direct BI system.
DB Synchronization (dedicated and on-premises deployment models only)
For on-premises deployments with a dedicated Ender Turing instance, the customer can copy database content to warehouse databases (DWH).
Pros: There is no need to implement fetch/sync pipelines.
Cons: You will need to rely on Ender Turing data structures
Contact Ender Turing support if you would like to exploit this integration option.
Data Fetch Flow (and data structures)
After logging in to the Ender Turing API (authentication API endpoint), you will usually start by searching for specific sessions (last day, week, sessions with updated scores, etc.).
Flow:
Search conversations
Session - conversation of any type (call, chat, email, etc).
Filter—Filters are essential to find specific sessions (conversations) across many. More details on Filters can be found here. You will mostly use date range filters like:
filters=date_range,2024-10-08,2024-10-21||. If your business needs a more specific filter, you can always ask them to show the applied filter from the web interface and copy it to the house in API.Usually, you will start by searching for sessions using the Read Sessions Endpoint with a filter to find specific sessions (conversations). As a result, you will get a list of sessions and metadata for each.
Fetch Additional meta
If the metadata obtained from the previous step is not enough, you can always get the most detailed metadata for the session using this endpoint.
Fetch Scores
Fetch Summaries
Fetch Transcripts
Fetch dictionaries
In all previous steps, you will be returned with some data as an external identification, for example
agent_id. It would help if you also fetched dictionaries to transform this into a readable format. Users, Agents, Teams, Categories, Scorecards.
If you need additional support or examples, don't hesitate to get in touch with the Ender Turing support team.
Best Practices
Ensure Data Accuracy: As data already pulled into the BI system can be changed afterward, like Quality Evaluations that may be made days (and sometimes even weeks) after the conversation finishes, it is essential to implement not only regular new data fetches but also fetch delta changes for historical data already in BI.
Optimize Data Fetch Schedules: Adjust data synchronization based on your operational needs, ensuring your BI dashboards offer near-real-time insights. We recommend synchronization daily between 4 and 6 AM for the previous day.
API Documentation, Python SDK, and Pipelines
API Documentation
API Documentation can be found here: https://app.enderturing.com/docs and https://app.enderturing.com/redoc (in Redoc format).
Ender Turing Python SDK
If you are using Python as the main library to develop integration, you can rely on Ender Turing Python SDK: https://pypi.org/project/enderturing
Examples: Implemented Synchronization Pipelines
Beginner minimalistic fetch script can be found here:
Advanced ETL (Extract Transform Load) pipeline for fetch from Ender Turing and Load to Mysql can be found here: https://github.com/evios/ETL-Ender-Turing/tree/main/ET-ETL-DWH-PY312
Conclusion
Integrating Ender Turing with your BI system directly brings customer interaction data into your business analytics processes. By leveraging its APIs, your organization can monitor call quality and uncover insights that lead to more informed decision-making. With the proper setup, this integration can help streamline operations, improve customer satisfaction, and drive business growth.
Feel free to contact Ender Turing's support or check the API documentation to get started on this transformative journey for your contact center analytics!