How SuccessKPI’s Implementation of Sentiment Analysis is Revolutionizing Customer Experiences

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In today’s marketplace, businesses encounter considerable challenges due to the absence of thorough sentiment analysis in call center interactions. This shortfall hinders the ability to truly comprehend and address customer emotions and concerns in real-time, which is essential for upholding high service quality standards.

Without robust sentiment analysis, businesses risk misreading customer feedback, leading to lost opportunities to resolve issues and enhance satisfaction. This gap also obstructs the accurate measurement of overall customer satisfaction, a vital element in developing strategies to improve customer experiences.

In this article, we will delve into how SuccessKPI utilizes natural language understanding (NLU) and machine learning (ML) across extensive datasets to predict sentiment. This methodology empowers clients to grasp their customers’ feelings towards the products or services they use.

SuccessKPI, recognized as an AWS Specialization Partner and AWS Marketplace Seller with Machine Learning Competency, supports various sentiment types: positive, neutral, negative, or mixed. Sentiment can be identified across multiple formats including voice, emails, support chat logs, social media interactions, and customer reviews.

By employing artificial intelligence (AI)-based sentiment analysis tools, SuccessKPI helps businesses eliminate the personal biases that human reviewers might introduce. Consequently, companies achieve consistent and objective evaluations when analyzing customer feedback and dialogues.

How is Sentiment Determined?

The initial phase of sentiment detection is tokenization. When a conversation is inputted into the sentiment analysis engine, it dissects the dialogue into customer and sentiment channels. Each channel is then further divided into multiple turns, which consist of phrases or sentences.

The second phase involves preparing each turn by eliminating non-essential words, often referred to as “stop” words, that don’t contribute meaningful value to the phrases or sentences.

The third phase encompasses stemming and lemmatization. Each word typically has multiple forms but shares a common root. For instance, the word “run” serves as the base for “running,” “ran,” and “runs.” This might look something like this:

running => run
ran => run
runs => run

Lemmatization parallels stemming but factors in the context of the phrase, allowing differentiation between words with varying meanings depending on their part of speech. For example, if we lemmatize the sentence “I am running and I usually use to run,” it would transform to:

I => I
am => be
running => run
and => and
I => I
usually => usually
use => use
to => to
run => run

ML techniques and sentiment classification algorithms, such as neural networks and deep learning, train machines to recognize emotional sentiment from text. This process involves developing a sentiment analysis model and repeatedly training it on known data to accurately predict sentiment in unknown data. The analysis considers spoken phrases, contextual nuances, vocal tone, and volume as key factors influencing sentiment.

How Are Sentiment Scores Interpreted?

Sentiment within conversations is documented by assigning labels (negative, positive, neutral) along with confidence scores. A typical sentiment output appears as follows:

{
  "SentimentScore": {
      "Mixed": 0.030585512690246105,
      "Positive": 0.94992071056365967,
      "Neutral": 0.0141543131828308,
      "Negative": 0.00893945890665054
  },
  "Sentiment": "POSITIVE",
  "LanguageCode": "en"
}

In this instance, machine learning predicts less than a 1% chance of negative sentiment and about 95% confidence in positive sentiment. The SuccessKPI algorithm selects the most accurate prediction, which is positive in this case.

What Are Common Types of Sentiment Analysis?

• Sentiment by channel: SuccessKPI currently supports sentiment analysis segmented by channel (customer or agent) along with the confidence score of that sentiment classification.
• Sentiment by time: SuccessKPI can identify sentiment at the speaker-sentence level, enabling clients to understand how sentiment fluctuates throughout the conversation.
• Sentiment by quarter: This feature allows the analysis of sentiment across four segments of a conversation, facilitating quality assurance sampling. For example, it can highlight calls where a customer initially expressed positive sentiment but later ended on a negative note, indicating dissatisfaction.
• Sentiment by entity: SuccessKPI captures all entities mentioned in a conversation without linking them to sentiment. This capability enables clients to discern sentiment related to specific products or services. For instance, in the feedback “the shoes are amazing, but the stores are dirty,” SuccessKPI can identify the sentiment around “shoes” as positive and “store” as negative.

Examples of SuccessKPI Sentiment Dashboards

These dashboards provide crucial insights across diverse business dimensions, including sentiment analysis for various business units and locations. They also reveal sentiment trends across different service offerings, which assists in identifying and mitigating potential risks. Furthermore, the dashboards offer geographical performance analysis to identify regions at risk, allowing for strategic business pivots and informed decision-making.

What Are the Challenges with Sentiment Analysis?

Despite advances in natural language technology, machines still struggle with the subtleties of human communication. Even when excluding body language, verbal interactions contain numerous nuances that can be challenging to interpret.

Sarcasm is one such scenario where machines often falter. For example, if a customer remarks, “Yeah, great. It took three weeks for my order to arrive,” without contextual understanding, a machine may inaccurately label the sentiment as positive due to the word “great.”

Negation also poses challenges for machines. Consider a customer saying, “Oh yeah, it will take less than a week for you to go live with another vendor.” A machine might interpret this as positive when the intent was to convey a longer time frame.

Multipolarity in speech further complicates sentiment analysis. For instance, in the phrase “The shoes were great, but the store was dirty,” two distinct sentiments are expressed, which requires careful interpretation.

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