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

Keyword Identification

Wootric

If you're looking to improve your customer experience, you need to listen to your customers. NPS surveys are a great way to do that, but analyzing the comments can be time-consuming and overwhelming. That's where generative AI comes in. In this post, we'll show you how to use generative AI to automatically extract keywords from Wootric NPS survey comments and gain valuable insights into customer sentiment and preferences.

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 can be used to extract key insights and themes from customer feedback, social media comments, and other forms of customer communication. Keyword extraction can be performed manually, but it is time-consuming and may not be scalable for large datasets. That's why many companies are turning to automated keyword extraction using machine learning algorithms.

Example Use Cases

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

Identifying common pain points and areas for improvement
Tracking changes in customer sentiment over time
Improving customer retention and loyalty
Identifying potential brand advocates and influencers
Improving product development and innovation

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

Finding your input data and identifying preliminary keywords

You can extract the Wootric NPS survey comments using the Wootric API, export it in CSV format, or query a list of comments from your data warehouse or BI tool. Once you have your data, you can use generative AI to automatically extract keywords and gain insights into customer sentiment and preferences. It can also be helpful (but not necessary) to identify common keywords that you may want to extract from your NPS survey comments. Generative AI tools can suggest additional keywords you may not have been aware to look for.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords and gain insights into customer sentiment and preferences. This will help you improve your product, marketing, and customer experience strategies.

Overall, keyword extraction using generative AI can help you gain valuable insights into customer sentiment and preferences, which can help you improve your product, marketing, and customer experience strategies. By automating the process of keyword extraction, you can save time and resources while gaining a deeper understanding of your customers.

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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