More On Topic Modeling, A Form Of AI For Customer Support

By Loops · 23 June, 2023

Last week on the TheLoops blog, we introduced topic modeling–an impactful way to leverage AI and machine learning for Customer Support.

As a recap, topic modeling helps CX (support, success, product and engineering) move away from manual, delayed analysis of customer interactions and instead, understand the prevalent themes and issues customers are experiencing in real-time.

In other words, the algorithm takes large volumes of customer interactions, understands their meaning then provides visualizations and analysis on how they’re grouped. This helps Support agents and managers correlate topics to volumes instantly.

In response to our blog, here’s one of the recurring questions we received: is topic modeling new like Chat-GPT? Or has this always been around?

A History On Topic Modeling and Machine Learning

To answer the question, topic modeling has been around for quite some time. It started to find more prominence in 2011.

That said, 10-15 years ago, the Internet was less developed. As a result, the topic modeling of those days utilized a premature language model and the results were not easy to consume or act on. They also had bad accuracy.

Recent advancements and refinement to an algorithm called BERT–an open source machine learning framework for natural language processing (NLP) that was pre-trained on Wikipedia–have improved the accuracy and performance of topic modeling. Given the results, you can now take actions on the outputs.

For all the history buffs out there, here’s a timeline of topic modeling.

There’s one key distinction to point out as well between topic modeling and a method of AI called clustering. Clustering algorithms group similar items together, whereas topic modeling algorithms identify relationships between items and uncover the hidden structure of a dataset, understanding their underlying patterns. Refer to our Topic Modeling post to see this visualized. 

In closing, topic modeling is used in a variety of CX use cases including text classification, content recommendations, and sentiment analysis–all valuable insights that provide CX teams with a real-time understanding of specific customer issues and pattern analysis. 

By leveraging these topics, you can:

  • Ensure that the right agents handle complex or specific types of cases
  • Quickly uncover product adoption issues or friction and
  • Proactively manage customer cases based on sentiment

Learn more about TheLoops Agent Assist capabilities here. 

With topic modeling deployed, you’ll put the days of reactive support behind you and quickly see your retention, employees productivity and customer revenue go up and to the right.

Stay tuned for our next “AI Summer School” blog. We also cover AI use cases and real-world examples for Support Leaders in our monthly Fireside Chat Series.

Recent Articles

Learn How Gainsight Reclaimed 120 Manager Hours Using AI-Driven Support Operations
The buzz surrounding AI for CX is taking on a new tone. Leaders no longer...

By Loops 17 July, 2024

Go Beyond The Hype—5 Practical Steps for AI Integration From A Customer Support Leader
One of the biggest challenges Support leaders face is deciding where and how to begin...

By Loops 05 July, 2024

Reinventing Support with AI: How Bloomreach Aligned Their Support and Success Teams With TheLoops 
This is a high-level recap of TheLoops Fireside Chat series, “From Early Adoption To...

By Loops 03 July, 2024