Customer surveys are undoubtedly valuable to your business. They produce rich, valuable insights into loyalty and satisfaction, provide actionable feedback to improve your product or service, and help build a relationship with your customer base. That being said, surveys come with their own set of challenges, including accurate text analysis. In this article, we’ll take a look at one powerful tool that can help you accurately analyze your customer feedback: state-of-the-art sentiment analysis.

The Challenge: Understanding Your Feedback from Open-Ended Surveys

Let’s rewind for a minute and walk through what a typical customer survey might look like for your business. If you’re looking to gain insight into customer satisfaction or loyalty, you might seek to gather a measurable metric accompanied by open-ended feedback– such as NPS (net promoter score) or CSAT (customer satisfaction).

If you choose to send a net promoter score survey, for example, your survey will ask customers, How likely are you to recommend Brand X to a friend, coworker, or family member? Please provide a rating on a scale of 0-10, 0 being extremely unlikely and 10 being extremely likely.

This question will be followed up by an opportunity for respondents to explain their rating: Please explain your rating.

Then, you’ll place your respondents into three categories: Promoters (rating of 9 or 10), Passives (7 or 8), and Detractors (6 or below). After calculating your score
(% of promoters - % of detractors = NPS),
you’re now faced with the task of evaluating feedback: the open-ended text that expresses the thoughts, opinions, and advice of your customers.

Here’s where the challenge begins–accurately analyzing what your customers had to say, and producing actionable insights.

Option 1: Use a human

Of course, you could rely on a human being to perform text analysis. In theory, a human could accurately run through hundreds (or thousands) of open-ended responses to identify common themes and evaluate various sentiments.

One man is staring at the screen of one of two laptops representing the investigating of data for sentiment analysis.

The glaring issue with this approach is, of course, the fact that this takes an enormous amount of time (and money) to achieve. It’s slow, tedious, and meticulous work to pick through each individual response. And depending on the size of your customer base, this could take days or even weeks to accomplish successfully.

The second issue with human analysis is that humans are susceptible to fatigue and inconsistency. For example, a human coder might evaluate the phrase “I don’t care for product X” as extremely negative in one instance, but a few hours later, decide that this same phrase only connotes mild negativity.

In any case, human analysis isn’t ideal.

Option 2: Use a conventional algorithm

Your second option is to use conventional algorithms to evaluate open-ended feedback. In theory, an algorithm can perform the work of analysis for you and applies consistent intelligence to produce consistent results.

Conventional algorithms, however, often lack the type of intelligence required to evaluate the following:

  • Negations or words that carry different meanings depending on the context. Word-dictionary sentiment analysis cannot, for example, decipher the difference between the literal and figurative use of the word “hot” (The bread is always hot vs. The hottest place to be on a Friday night.)
  • Irony or sarcasm (such as “We only waited for 2 hours for our food to arrive…“)
  • Spelling mistakes

That being the case, a conventional algorithm may produce some basic feedback for you…but don’t count on it being accurate. 😕

The Solution: Use State-of-the-Art Sentiment Analysis

Unlike a conventional algorithm, state-of-the-art sentiment analysis relies on a machine-based learning system to evaluate open-ended text.

The difference lies in the system’s ability to accurately analyze the commentary of the survey-taker (i.e. The quality of the product is great, but customer service is terrible).

The right kind of sentiment analysis empowers you to accurately capture both positive and negative sentiments around various topics. In short, it allows you to really understand “what was good” and “what was bad”.

How to Choose the Right Kind of Sentiment Analysis

A sentiment analysis that takes an aspect approach gives you the richest type of insight. How so? An aspect approach understands sub-themes – not just overall sentiment.

An answer-level approach assesses the single mood of a response. But aspect-level sentiment analysis can assess multiple sentiments–or themes–within a single response.

Not only that, but a user can define different sub-themes he or she would like to identify in the feedback by creating polarized codes.

Some aspects of your product or service, such as billing, may simply be positive or negative. If a customer doesn’t appreciate your pricing, the sentiment won’t be very complex.

But other aspects of your business may carry multiple connotations. Customer Service, for example, may produce a range of emotions in your customers. Sub-themes such as “competent/incompetent,” “friendly/unfriendly,” and “knowledgeable/lack of knowledge” can be understood by sentiment analysis that takes an aspect approach, but may not be picked up by sentiment analysis that picks up on the overall mood.

Furthermore, customers might mention specific brand names in their feedback. If a customer is comparing you to a competitor, you’ll want to be able to identify the theme. Are you being compared positively or negatively? Are they drawing similarities, or wishing you offered a comparable service or product?

In any case, state-of-the-art sentiment analysis can help you quickly understand the emotions of your client base and ultimately, give you the accurate, actionable insight you need to improve your business and generate more enthusiastic customers.

Caplena: Your Answer for Detailed, Specific Analysis

Caplena is an A.I.-powered software that can offer you the kind of detailed, accurate insight you need from any type of open-ended feedback. Caplena’s state-of-the-art sentiment analysis is founded on a deep, transformer-based neural network that has been trained on big data and fine-tuned on domain-specific data. It operates on individual sub-words (“word-pieces”) to accurately evaluate a text. In other words, it takes the powerful aspect approach described above.

Caplena can help you:

  • Save significant time and money on manual coding
  • Gain high-value insights into any kind of open-ended feedback (not just keywords)
  • Streamline your feedback process
  • Boost your overall efficiency
  • Finally, tell the story of your feedback: What are customers saying about your business, and why?

To give Caplena a try for free, click here.

Related Posts:

A Stoic Breakdown: Sentiment Analysis

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3 steps: Analyse survey responses in Caplena

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