How to reduce churn rate using NLP

Your product has a high churn rate.

Topic Modeling can help you discover what aspects of your product people are talking about.

It analyzes text data to determine clusters of common words.

Topic modeling is a way of understanding a text by identifying the key topics mentioned multiple times.

It's useful for identifying key areas of a text that you may want to improve.

I'm going to share some techniques:

1/ Latent Dirichlet Allocation (LDA)

It works by assuming that each document is made up of a mixture of topics.

They are then assigned a probability.

Documents are sorted according to their most probable one.

2/ Hierarchical Latent Dirichlet Allocation (HLDA)

It is similar to LDA, but it takes into account the hierarchical structure of a document.

This can be useful for documents that are made up of multiple sections, such as a research paper.

3/ Latent Semantic Analysis (LSA)

It is a technique that is used to find the most important words in a text.

It does this by analyzing the relationships between words.

This can be useful for identifying words that are related to a particular topic.

4/ Probabilistic Latent Semantic Analysis (PLSA)

It is a more recent technique that builds on LSA.

It uses a probabilistic model to find the relationships between words.

This allows it to identify words that are related to more than one topic.

5/ Deep Learning and Transformers architecture

BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF.

It create dense clusters allowing for easily interpretable topics.

So, keeping important words in the topic descriptions.

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