How Sentiment Analysis Can Improve Customer Experiences

How Sentiment Analysis Can Improve Customer Experiences

When you feel good about the brand behind a product, you're more likely to buy it. If you know how your customers are feeling, you can course correct easily to improve customer success and sentiment.

Of course, for a brand, it's not always clear how consumers feel about a product. It's easy when it's really good — like the Instant Pot everyone bought last year — or really bad, like how people felt about the much-maligned 2019 Peloton Christmas ad. But in between success and failure is a vast gray area of uncertainty.

That's where sentiment analysis can make a difference. It enables companies of all shapes and sizes to pinpoint major trends in how customers feel about them, and why. Adding a business analytics tool to the mix gives you the ability to make informed decisions about effective next steps based on insights gleaned from real-time customer data. Aggregating and analyzing that data is now easier thanks to artificial intelligence, which can automate the process of monitoring and assessing customer experiences based on everything from customer reviews to social media chatter.

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Understanding Your Customers

Sentiment analysis is data-driven process that will help you track customer experiences and identify potential problems that could impact sales. An uptick in customer complaints about long hold times, for example, could provide insight into the need for improved staffing at call center.

Sentiment analysis, sometimes called opinion mining, is an automated process for analyzing data related to customer experiences with your brand, services, or products. It enables companies to better analyze massive amounts of data from customers in order to generate actionable insights based on what people are actually saying about their experiences.

Because it can sort-out unstructured data — such as random tweets or social media posts — sentiment analysis is a game changer for measuring the aggregated pulse of your customer base. Combined with more structured, quantitative data, sentiment analysis can help companies proactively resolve problems that ultimately result in better customer experiences.

Today, companies use sentiment analysis to study data from microblogging platforms, forums, review sites and social media posts. With the help of AI they can identify patterns or anomalies that can lead to product improvements, customer service changes, or more targeted marketing campaigns.

Some common insights gleaned from a sentiment analysis include:

Satisfaction: Are your customers happy with your product?

Retention: Will they come back for more?

Loyalty: Will they come back even in the face of competition?

Engagement: How often will they come back? Will they tell their friends?

How Sentiment Analysis Works

Sentiment analysis is a branch of natural language process (NLP) technology. This is a fancy way of saying that it analyzes the written language to decode emotion and tone, and then establishes attitude or opinion based on those findings.

It works by mapping words into three common categories of emotions: positive, neutral and negative.

There are four basic steps to a sentiment analysis:

  1. Define your classes: Positive, neutral, or negative
  2. Pick your dataset: Twitter, Yelp, Reddit, or other sites?
  3. Classify your sentiment: Is gnarly a bad word? If you're a restaurant, yes. If you're a surf shop, maybe not.
  4. Analyze

You can choose to analyze your data in three different ways:

Rule-based approach: You tell the computer what's positive and negative, and then let it analyze based on your instructions.

Automatic: You let the computer use complex machine learning techniques to recognize patterns on its own, after you show it examples of each class. Like how Netflix tailors recommendations for you after you've continuously shown them examples of what you like (by watching content) on their platform.

Hybrid: A combination of rule based and automatic.

Regardless of the approach, the algorithm will scan the text for words it thinks belongs in each class; good, bad or neutral. A score will then be applied based on the findings. Usually +1 for positive sentiment, -1 for negative sentiment, and 0 for neutral. The higher or lower the score, the stronger the emotion.

The only caveat is sarcasm. Algorithms still aren't great at understanding the non-truth in a sentence like, “Great, my package still hasn't arrived." But it's getting better. It can capture nuances, such as the fact that "hell" might actually be a positive indicator if used in the phrase "hell of a good time." This illustrates the difference between polarity, the emotion in a sentence, and subjectivity, its context. The polarity for the word "hell" in this example could be good or bad, depending on its subjectivity, whether or not its used in the phrase "hell of a good time."

Easy-to-Use Tools

If you're lucky enough to have a data scientist on your team, you can work with him or her to develop a script to help you make data-driven decisions. But you don't need a data wizard on call. Anyone can use sentiment analysis software tools to get the job done. Machine learning capabilities are now readily accessible to analysts and non-technical users.

Here are three tools designed to make sentiment analysis accessible to companies without data scientists on hand:

1. Gamalon

You can receive automated sentiment analysis in real-time, as well as rely on an automated chat bot that speaks naturally with consumers.

2. Squirro

This platform enables you to build dashboards based on your specific industry, and automates reporting that's sent to you in real-time.

3. Brandwatch

Claiming to have access to a dataset with more than 1.2 trillion online posts to analyze, Brandwatch helps brands achieve market fit with social listening techniques.

Optimizing Outcomes

A sentiment analysis tool alone isn't enough to enable you to make decisions on how to course correct. You also need to know why sentiment is tipping towards one side of the scale or another.

Using the results from sentiment analysis with an AI-based business analytics platform, such as Ki, will enable you to automatically generate plans and strategies in response to your sentiment analysis.

By extracting emotional themes from your sentiment analysis, You'll be able to course correct in the following ways:

  • Optimize your product line
  • Optimize your marketing campaigns
  • Identify urgent issues
  • Automate processes
  • Get an edge on the competition

Ultimately, you can improve negative sentiment by streamlining customer service workflows, or amplifying positive sentiment by making a feature that customers really like more visible or accessible. Gleaning insights from both negative and positive customer sentiment scenarios will ensure that you understand what your customers need and want. That will enable you to deliver better customer experiences that will ultimately lead to repeat sales that boost your bottom line.

To learn more about leveraging sentiment analysis and AI, request a demo and see how your data can be turned into action.

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