Dealing with thousands of service requests? Discover the perks of AI-enabled customer sentiment analysis
Any business that deals with large volumes of customer communications needs to have a means of both categorizing the data and understanding the emotional sentiment of it. In this piece, we’re going to explain what customer sentiment analysis is, why it’s important and how EnateAI automatically performs sentiment analysis for operations teams, saving valuable time and money.
What is customer sentiment analysis?
Sentiment analysis is a method of intelligently analyzing correspondence to determine how satisfied, dissatisfied or neutral a customer is. Technically, this is achieved by applying the science of text analysis or Natural Language Understanding (NLU) to look for patterns within language to determine how positive or negative customer correspondence is.
Sentiment analysis can be applied to many forms of customer correspondence. For example, emails, reviews, survey feedback or even social media posts. For this article though, we’re going to focus on customer sentiment analysis specifically in operations which usually takes the form of emails or documents.
What’s the difference between sentiment analysis and opinion mining?
Opinion mining is built on top of Sentiment Analysis and attempts to analyze data more deeply to determine why the sentiment is either positive, negative or neutral has been identified. Opinion mining often looks at an extended data set and can be extremely beneficial in identifying trends or triggers that may lead to a negative experience or allow business to act with foresight when they see a pattern emerging that is likely to lead to a negative or dissatisfactory experience.
While opinion mining may deliver a more rich and sophisticated result, often businesses want to act quickly on sentiment expressed in a message to direct a workflow. In this case, sentiment analysis is the best way to proceed.
Why is customer sentiment analysis important?
By understanding the emotions and attitudes locked within incoming customer correspondence, service teams can react more intelligently to customers or queries. This has the cascade effect of improving customer experience within a platform or workflow like Enate.
Customer sentiment analysis operations examples
Customer service call centers often have to process thousands of emails each day, all of which relate to ongoing work or new requests. Although it’s possible to triage and prioritize emails based on the type of work, this approach ignores the sentiment or attitudes that exist within the communication.
Customer satisfaction is paramount to retaining customers as such call centers often want to react more swiftly to messages that have a negative sentiment to hold onto and delight their staff.
The problems with implementing customer sentiment analysis in operations
Traditionally, Sentiment Analysis is built on-top of Natural Language Understanding (NLU) and although a general model could be deployed, customer specific adaptation is usually required to deliver value. This requires a complex, highly expensive and lengthy MLOps third-party process, with results often hovering at around 70% accuracy.
Until now, the alternative has been for staff to manually check the sentiment of emails, but this of course takes a lot of time when you are dealing with thousands of emails every day.
How Enate helps businesses automatically perform customer sentiment analysis in operations
EnateAI essentially jumps 3 steps ahead with an advanced sentiment analysis tool which automatically checks emails for sentiment. It’s integrated within Enate’s platform which means that you don’t have to pay for complex MLOps, and you can react to sentiment analysis within your existing workflows.
Employees that use EnateAI can immediately start triaging emails based on sentiment, with an estimated accuracy of 90%. Leveraging and operationalizing large language models means that Enate is able to more accurately determine sentiment across a wider range of inbound data with resilience to quality. It also removes the need to manually read through each email and check for sentiment, saving businesses valuable time.
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Sentiment analysis is just one feature of EnateAI. Our brand-new product has been engineered to maximize efficiency in operations and has the capability to automatically categorize emails, extract data, understand foreign languages and ask/answer specific questions such as ‘Is this email just a thank you’?
AI in Operations
Operational Soup is a term we use when work is being carried out, but businesses have little idea how much, by whom or exactly how it is processed.
Start orchestration in departments with strong use-cases to deliver value quickly. Often, good examples can be found in back/middle office process areas that have high variation and complexity such as finance or HR operations. Recent intelligence sourced through process mining suggests 80%+ of the work performed in a shared services organization is not performed in the ERP systems, but rather in Excel or Outlook. This is where Orchestration thrives.
Having orchestration implemented across our departments can be likened to having x-ray vision into your operations.
Global Head of Operations at TMF
Almost half of banking and investment CIOs (49%) and insurance CIOs (44%) indicated that they will increase their automation investments in 2021.
Source: Gartner, 2021