How to analyze sentiment of Intercom CSAT Survey Comments with generative AI

Sentiment Analysis

Intercom

As a customer-focused company, understanding how your customers feel about your products or services is crucial. Customer satisfaction (CSAT) surveys are a great way to collect feedback, but analyzing the comments can be time-consuming and subjective. In this article, we’ll show you how to use generative AI to automatically perform sentiment analysis on Intercom CSAT survey comments, giving you a deeper understanding of your customers' satisfaction level.

What is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique that can accurately identify the emotions and opinions expressed in a given piece of text. The algorithms are trained on a labeled dataset of text samples and can predict the sentiment of new, unseen text by recognizing patterns and features in the text associated with different emotions. Sentiment analysis can be used in various applications, such as customer feedback analysis, social media monitoring, and market research.

Example Use Cases

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

Quickly identify common themes and issues that customers are experiencing
Measure the overall sentiment of customer satisfaction and identify areas of improvement
Detect negative sentiment and prioritize follow-up actions
Improve customer retention and loyalty by addressing customer concerns

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

Accessing your Data and Confirming your Sentiment Scale

To analyze the sentiment of Intercom CSAT survey comments, you need to first extract the data. Intercom provides an API to export your CSAT survey data in CSV format. Alternatively, you can use a data warehouse or BI tool to extract the data. Once you have the data, 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). Here is an example of a sentiment rating scale:

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 Intercom CSAT survey comments.

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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