CallMiner Ingestion APIs

By Mingren Xiang, Allen Shapiro, Ronald Mueller

The Key To Unlock Hidden Insights From Your Customer Interactions

We live in an era of big data. Data is fast becoming one of the biggest and most valuable assets of a company. Having an efficient data flow both internally and externally is very important for an organization. Application Programming Interfaces (APIs) are generally the most effective way for data communication across different platforms.

CallMiner understands how important your data is. That’s why the Eureka platform, the best-in-class speech and text analytic tool comes with a publicly available API package at no additional cost. There are two collections of APIs in the package. The Data APIs export data out of the platform and the Ingestion APIs take in data to the platform for analysis.

This paper will give you an overview of the Ingestion APIs. We will also demo the use cases for uploading various media types into the system such as call recordings, emails, chats, and surveys.

Background

CallMiner’s leading speech analytic platform lives entirely on APIs as every use case in the system can be achieved by calling one or a set of APIs. One of CallMiner’s goals moving forward is to expand the usage of their APIs. As a dedicated technology partner of CallMiner, Macrosoft is a great choice for you to consider when it comes to API implementation.  We are working closely with CallMiner to develop custom API-based applications. We are also working with Call Miner to provide top-class documentation on Call Miner’s APIs and how to utilize them in applications, both from the point of view of a developer and business user.

To help customers understand and appreciate the power of CallMiner’s APIs, we are excited to present this three-part series on CallMiner APIs, of which this is the second paper in the series. Check out the first paper (https://www.macrosoftinc.com/introduction-to-callminers-apis/) to get a basic overview of the Call Miner API suites.

This paper is part two of our API series covering Ingestion APIs. Utilizing Ingestion APIs is the most efficient and organized way to upload all of your customer interaction data to the Call Miner platform for analytics. Yes, that’s right. These ingestion APIs can take in 100% of your interaction data – from call center recordings to emails, chats, texts, and surveys. You can use the Ingestion APIs to upload all of them to the system in an automated and robust fashion.

What is CallMiner’s Ingestion API?

The CallMiner Media Ingestion API is a standardized interface into the CallMiner Eureka Analyze platform. It provides a highly available, scalable way to add and update the media and metadata for all your audio and text-based contacts into your Eureka system.

Once a customer engages with CallMiner, they will be given access to the API library. A security token will be provided to you to securely upload your data to your instance of the platform. In addition, the token can grant you access to a Swagger User Interface that has documentation around every single API route. The picture below shows an overview of this Swagger UI.   

Customers can choose to upload their data here in the UI without writing any code

Ingestion API use cases

CallMiner allows for three methods for data uploading:

  1. SFTP (SSH File Transfer Protocol)
  2. Data Extractor on the customer’s server
  3. Ingestion APIs.

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Ingestion APIs are without a doubt the best way to upload your data because APIs make it possible to upload 100% of your customer interaction data without the excessive configuration work that is needed in the other two methods.

Here is a list of use cases for the Ingestion APIs

  • Call recordings uploading

The above snips show the mapping of the Ingestion API Post request for audio recordings. CallMiner supports all mainstream audio types including WAV, MP3, and WMA. Customers can easily set up different audio types by changing the “MediaType” fields

  • Set up the hierarchy of your organization

With the power of the Ingestion API, you can upload the hierarchy metadata with the call recordings in an automated fashion. Ingestion APIs organize your data while transferring the data to the Eureka system. For example, you can implement any team change by changing the metadata values.

  • Text based interaction uploading

As the above example shows, Ingestion APIs can take in Chat data. The system also supports the use of emojis.

Another common text interaction with the customer would be Emails. Ingestion APIs can also take in emails efficiently, as the example below shows

As these use cases show, Ingestion APIs can take in all of your customer interaction data in an automatic and organized fashion.

So all your data is ready for analysis. We believe that utilizing the ingestion API is truly the key to unlocking all the hidden insights in your data!

Engage with Macrosoft to get the best usage of Ingestion API

The Call Miner ingestion APIs are powerful, agreed, but for a new customer of Call Miner there will definitely be a significant learning curve. If a client does not have expert developers conversant in APIs it will be doubly hard to implement these ingestion APIs from Call Miner.

Macrosoft’s dedicated CallMiner team is here to help you. Our developers are available to quickly and easily get you the best usage out of CallMiner’s Ingestion APIs. Our developers are trained and conversant in all the ins and outs of best practices for utilizing the Call Miner Ingestion APIs to build scalable and best-practice custom apps. Macrosoft has 25+ years of experience in delivering top-quality custom technical solutions. Our team will take care of your organization’s API integration with the CallMiner platform – all the way through, from uploading data, daily maintenance, to building custom API apps. Let us take the heavyweight for API development while your organization thrives with all the insights available through the CallMiner analytic platform!

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By Mingren Xiang, Allen Shapiro, Ronald Mueller | January 6th, 2021 | CallMiner

About the Author

Mingren Xiang

Mingren Xiang

Mingren is a Data Science professional at Macrosoft. He is Macrosoft's technical lead in voice and conversational analytics using the CallMiner suite of utilities. The practice includes both partnering with CallMiner to deliver speech analytic solutions and developing customized NLP applications. Mingren has a Master of Science from the University of Wisconsin-Milwaukee.

Aside from leading the speech analytics practice at Macrosoft. Mingren's research work focus on Deep Learning applications for medical image processing. He presented the Master thesis on training a CNN (Convolutional Neural Network ) based Encoder-Decoder model to reconstruct CT scans using only one X-ray image. Such a task remains to be one of the hardest challenges in the computer graphic community

Coming from a strong computer science background, Mingren is also sufficient in multiple programming language such as Java, Python, C/C++, various JavaScript libraries and SQL scripts. His specialty in software development is to utilize API to create functional backend services using web development framework like Java Spring and Django in Python.

Allen Shapiro, Director – CCM Practice

Allen Shapiro

Allen brings more than 25 years of diverse experience in Marketing and Vendor Management to Macrosoft Inc. As the Managing Director of our Customer Communications Management (CCM) practice, Allen leads the Onshore and Off-shore CCM development teams. Additionally, Allen oversees pre-sales activities and is responsible for managing the relationship with our CCM software provider Quadient.

Dr. Ronald Mueller CEO of Macrosoft

Ronald Mueller

Ron is CEO and Founder of Macrosoft, Inc. He heads up all company strategic activities and directs day-to-day work of the Leadership Team at Macrosoft. As Macrosoft’s Chief Scientist, Ron defines and structures Macrosoft’s path forward. Ron's focus on new technologies and products, such as Cloud, Big Data, and AI/ML/WFP. Ron has a Ph.D. in Theoretical Physics from New York University and worked in physics for over a decade at Yale University, The Fusion Energy Institute in Princeton, New Jersey, and at Argonne National Laboratory.

Ron also worked at Bell Laboratories in Murray Hill, New Jersey., where he managed a group on Big Data. Ron's work focused around the early work on neural networks. Ron has a career-long passion in ultra-large-scale data processing and analysis including predictive analytics, data mining, machine learning and deep learning.

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