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Why NylasTrack Trends and Increase Revenue with Sentiment Analysis
Understand trends in customers’ happiness levels and prevent churn at scale.
Understand trends in customers’ happiness levels and prevent churn at scale.
Managing inboxes filled with competing priorities wastes time that should be spent on more strategic initiatives. Customer-facing roles like customer success often have the most challenging time managing their inboxes, as customer tickets pile up faster than there are hours in the day to respond. In fact, the average inbox has 200 emails in it at any given time, and most business professionals only have time to respond to only 25% of their emails.
As a result, high-priority emails are often missed, resulting in poor user experience, increased churn, and lower retention metrics. If you’re building a customer success application or any customer-centric platform, you need a solution that enables your customers to quickly pinpoint the highest priority emails and proactively prioritize their work. We are excited to launch a powerful new feature to help improve customer satisfaction and engagement: Sentiment Analysis.
The Nylas Sentiment Analysis beta (part of our Neural API) uses natural language processing (NLP) to label a body of text with either a positive, negative, or neutral score that can be used to evaluate data and guide business decisions. Eliminate the tedious act of prioritizing emails while leveraging Nylas’ machine learning models to quickly and easily aggregate sentiment in real-time – freeing up time to focus on business-critical projects.
People spend 20% of their time on tasks that could be automated, and prioritizing emails is one such task. Without leveraging email sentiment, teams manually sort through and prioritize their inboxes. Time spent sorting and prioritizing is time that should be spent driving your business and working towards initiatives. On the other hand, building a tool to aggregate sentiment can take more than six months to complete due to the complexity of emails. The various email formats, nested responses, legal text, and more make it difficult to extract away the necessary text to aggregate sentiment while also training and maintaining your in-house models.
Sentiment Analysis is a feature of the Neural API and uses advanced machine learning algorithms to detect and aggregate sentiment in bodies of text. With a PUT request to the api.nylas.com/neural/sentiment endpoint, you can prioritize your inbox based on urgency and need. To remove the unnecessary nested responses, legal text, and any other information that can throw off sentiment scores, Nylas passes bodies of text through the Clean Conversations endpoint to extract only the necessary body of text.
The sentiment endpoint calculates the sentiment_score and returns sentiment that is either Positive, Negative, or Neutral. The sentiment_score ranges from -1 (negative) to 1 (positive) and corresponds to the text’s overall emotional leaning.
Here is an example of a Positive score below:
{
"account_id": null,
"model": "sentiment-v0",
"sentiment": "POSITIVE",
"sentiment_score": 0.6000000238418579,
"text": "Hi, thank you so much for reaching out! The meeting went great, and I am looking forward to learning more next week."
}
Learn more about the Sentiment Analysis Beta in our docs here.
Sentiment Analysis gives you the tools needed to engage people at the right time and increase overall productivity, efficiency, and happiness. Here are a few examples below of how you can use the sentiment endpoint to improve your business.
Customer Success: CSMs work to improve customer satisfaction and reduce churn. Applications can leverage the sentiment endpoint to pass customers emails through advanced algorithms to aggregate sentiment and flag potential churn risks. Your end users can proactively engage customers and drive higher NPS. You can leverage the sentiment scores to build triggers that automatically alert CSMs when customers have low scores.
Sales/CRM: Sales reps email prospects constantly throughout the day. A CRM application can use the sentiment endpoint to label which conversations are going well and which need manager help. Sales managers can quickly scan through open opportunities to see which conversations are positive and which are negative and might not close. Sentiment Analysis gives teams more visibility into ongoing conversations and can help proactively prioritize workload.
Leveraging Sentiment Analysis enables your users to quickly see trends in customer sentiment, proactively prioritize messages based on urgency and need, and better prevent and forecast churn. With this new endpoint, not only do you have the tools to see sentiment, but you also can create triggers based on the sentiment score. In addition to other features of the Neural API such as OCR, Sentiment Analysis enables you to automate previously manual business tasks and focus on your business goals.
Speak to a platform specialist to learn more about the Sentiment Analysis beta and other features of the Nylas Neural API.
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Dominic is a Product Marketing Manager at Nylas. In his spare time, he loves to hike and go to the beach with his dog.
