How to extract keywords from Medallia NPS Survey Comments using generative AI

Keyword Identification

Medallia

How to Extract Keywords from Medallia NPS Survey Comments using Generative AI

As a business, understanding your customer's experiences and feedback is essential. Medallia NPS surveys provide valuable insights into customer sentiment, but with thousands of comments to sift through, it can be overwhelming. This is where generative AI comes in handy. In this post, we’ll show you how to use generative AI to automatically extract keywords from Medallia NPS survey comments.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. It is used to extract key information and themes from text data, such as customer feedback, reviews, and survey comments.

By analyzing large amounts of text data, keyword extraction can help identify patterns and themes that are important to customers. These insights can be used to improve customer experience, product development, and marketing strategy.

Example Use Cases

Use cases for extracting keywords from Medallia NPS survey comments include:

Identifying customer pain points and areas for improvement
Measuring customer satisfaction and sentiment
Identifying customer trends and preferences
Improving customer experience and loyalty
Developing effective marketing strategies

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

Accessing and Preparing Data for Keyword Extraction

To extract keywords from Medallia NPS survey comments, you will need to export the data in CSV format. You can do this by going to the "Data Export" section of the Medallia platform and selecting the appropriate survey and date range.

Once you have your data, you can upload it to a generative AI tool that specializes in keyword extraction.

Before running the analysis, it can be helpful to identify common keywords and themes that you may want to extract from your survey comments. This can help ensure that the analysis is focused on the most relevant information. Once you have your preliminary keywords identified, you can use generative AI to automatically extract and categorize keywords based on frequency and relevance.

Step-by-Step Instructions

Export Medallia NPS survey comments in CSV format
Upload CSV file to a generative AI tool that specializes in keyword extraction
Identify preliminary keywords and themes to focus analysis
Run keyword extraction analysis
Review and refine extracted keywords

By using generative AI to extract keywords from Medallia NPS survey comments, you can quickly identify important insights and improve customer experience, loyalty, and retention.

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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