How to analyze sentiment of Zoho Desk CSAT Survey Comments with generative AI

Sentiment Analysis

Zoho Desk

As a customer support team, you want to ensure that your customers are happy with your service. One way to measure this is by using CSAT surveys, which provide feedback on the customer's satisfaction level. However, analyzing the sentiments of the comments can be time-consuming and tedious. In this article, we will show you how to use generative AI to automatically analyze the sentiment of Zoho Desk CSAT survey comments.

What is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique that uses machine learning algorithms to identify and extract emotions expressed in a given piece of text. The algorithms are trained on a labeled dataset of text samples, where each sample is labeled with its corresponding sentiment (positive, negative, or neutral). The model learns to recognize patterns and features in the text that are associated with different emotions and uses these patterns to predict the sentiment of new, unseen text.

Sentiment analysis can be used in various applications, such as customer feedback analysis, social media monitoring, and market research. It is a powerful tool for organizations that want to understand how people feel about their products or services, track public opinion on different issues, and extract valuable insights from large amounts of text data.

Example Use Cases

Some use cases for performing sentiment analysis on Zoho Desk CSAT survey comments include:

Identify common trends in customer feedback to improve products or services
Detect common issues and quickly address them to improve the customer experience
Identify areas for improvement in customer support teams
Improve customer satisfaction and overall company reputation

Teams that might find these use cases helpful include: customer support, customer success, product, marketing, and operations.

Accessing your Data and Confirming your Sentiment Scale

To analyze the sentiment of Zoho Desk CSAT survey comments, you need to extract the comments data. You can do this by exporting the data in CSV format or querying a list of comments from your data warehouse or BI tool.

Next, you need to confirm the sentiment scale you will use for assessing customer sentiment. Typically, sentiment is measured on a scale of -1 (most negative) to 1 (most positive). You can also assign sentiment ratings, such as:

Very Positive
Positive
Neutral
Negative
Very Negative

Once you have your data and sentiment scale, you can use generative AI to automatically assess the sentiment of your Zoho Desk CSAT survey comments. This will help you improve the quality and consistency of your customer support, reduce churn, and improve the efficiency of your support team.

To use generative AI for sentiment analysis, you can use tools like IBM Watson, Google Cloud Natural Language API, or AWS Comprehend. These tools provide a user-friendly interface to train models, analyze sentiments, and visualize results.

Conclusion

By using generative AI for sentiment analysis, you can quickly and accurately assess the sentiment of Zoho Desk CSAT survey comments, identify common trends and issues, and improve the customer experience. This will help you retain customers, improve your company reputation, and increase revenue.

Using AirOps to perform Keyword Identification

With AirOps, you can easily extract relevant keywords and phrases from your text-based data using the Keyword Identifier data app. Here's how:

Select "Keyword Identifier" from the Data Apps page. The input required for Keyword Identifier is the "text_field" which is the input text data.
Decide where you want the analysis to be performed and stored. The Keyword Identifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_KEYWORD_IDENTIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_KEYWORD_IDENTIFIER(text_field) as result
FROM
your_table
```
Execute the keyword extraction analysis by running the SQL query. The output will contain an array of keywords and phrases extracted from the input text data.

Example Input:

"Hello, I am having trouble with my account. I cannot seem to log in and I have tried resetting my password multiple times."

Example Output:
```
"keywords": ["trouble", "account", "log in", "resetting", "password", "multiple times"],"summary": "A customer is having trouble logging into their account and has tried resetting their password multiple times."
```

Using AirOps to perform Sentiment Analysis

With AirOps, you can easily perform sentiment analysis on any text data such as reviews, support tickets, or sales calls using Sentiment Analyzer. Here’s how:

Select "Sentiment Analyzer" from the Data Apps page. The only input for Sentiment Analyzer is some text to analyze.
Decide where you want the analysis to be performed and stored. The Sentiment Analyzer data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_SENTIMENT_ANALYZER.

Here is an example SQL query:
```
SELECT
AIROPS_SENTIMENT_ANALYZER(text_field) as result
FROM
your_table
```
Execute the sentiment analysis by running the SQL query. The output will contain a sentiment score and sentiment summary, as well as a list of positive and negative keywords extracted from the input text data.
Input:
"I'm sorry to say that I had a terrible experience with your product. The customer service was unresponsive and the product didn't work as advertised."
Output:
```
"positive_keywords": [],"negative_keywords": ["terrible experience", "customer service", "unresponsive", "product", "didn't work", "advertised"],"score": -0.8,"sentiment": "Very Negative"
```

Using AirOps to perform Text Classification

With AirOps, you can easily perform classification using generative AI. Here’s how:

Select "Text Classifier'' from the Data Apps page. Below are the possible inputs for Text Classifier.text_field: The input text data.categories (optional): Categories can be specified as a comma-separated list. Leave empty for automatic determination.multi_category: Set to “true” if the text can belong to multiple categories, or “false” if it can only belong to one category.
Decide where you want the analysis to be performed and stored. The Text Classifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_CLASSIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_CLASSIFIER(text_field, categories, multi_category) as result
FROM
your_table
```
Execute the classification analysis by running the SQL query. The output will contain a list of keywords extracted from the input text data that are relevant to the identified categories and a list of categories that the input text data belongs to based on the provided categories or automatic determination.

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