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Sentiment analysis: Why it's necessary and how it improves CX

Sentiment analysis tools are essential to detect and understand customer feelings. Companies that use these tools to understand how customers feel can use it to improve CX.

Sentiment analysis tools generate insights into how companies can enhance the customer experience and improve customer service.

Sentiment analysis is an application of natural language processing (NLP) that reveals the emotional states in human speech or text -- in this case, the speech and text that customers generate. Businesses can use machine-learning-based sentiment analysis software to examine this speech and text for positive or negative sentiment about the brand. With this information, companies have an opportunity to respond meaningfully -- and with greater empathy. The aim is to improve the customer relationship and enhance customer loyalty.

Customer interactions with organizations aren't the only source of this expressive text. Companies can also obtain customer sentiment by mining social media. Social media monitoring produces significant amounts of data for NLP analysis. Social media sentiment can be just as important in crafting empathy for the customer as direct interaction.

When the organization determines how to detect positive and negative sentiment in customer expressions, it can improve its interactions with the customer. By exploring historical data on customer interaction and experience, the company can predict future customer actions and behaviors, and work toward making those actions and behaviors positive.

Why sentiment analysis is necessary

Sentiment analysis can improve customer loyalty and retention through better service outcomes and customer experience.

For example, a customer makes a support request through email or chat. The NLP machine learning model generates an algorithm that performs sentiment analysis of the text from the customer's email or chat session. The algorithm detects the customer's emotional state. In this scenario, the customer feels agitation. Business rules related to this emotional state set the customer service agent up for the appropriate response. In this case, immediate upgrade of the support request to highest priority and prompts for a customer service representative to make immediate direct contact. Finally, the service representative's awareness of the customer's emotional state results in a more empathetic response than a standard one, leading to a satisfying resolution of the issue and improvement in the customer relationship.

This scenario, simple though it may seem, shows how effectively sentiment analysis can improve customer outcomes. It's an example of augmented intelligence, where the NLP assists human performance. In this case, the customer service representative partners with machine learning software in pursuit of a more empathetic exchange with another person.

This scenario is just one of many; and sentiment analysis isn't just a tool that businesses apply to customer interactions. It's also a resource for brand management in social media.

Customer sentiment can improve customer loyalty and retention by improving service outcomes and enhancing the customer experience.

The importance of customer sentiment extends to what positive or negative sentiment the customer expresses, not just directly to the organization, but to other customers as well. People commonly share their feelings about a brand's products or services, whether they are positive or negative, on social media. If a customer likes or dislikes a product or service that a brand offers, they may post a comment about it -- and those comments can add up. Such posts amount to a snapshot of customer experience that is, in many ways, more accurate than what a customer survey can obtain.

This is where social media monitoring comes in. By mining the comments that customers post about the brand, the sentiment analytics tool can surface social media sentiments for natural language processing, yielding insights. This activity can result in more focused, empathetic responses to customers.

Sentiment analysis tool options

There are numerous steps to incorporate sentiment analysis for business success, but the most essential is selecting the right software.

Which sentiment analysis software is best for any particular organization depends on how the company will use it. A company that focuses exclusively on doing sentiment analysis to bolster the empathetic responses of customer service personnel might choose one tool, while a company interested in social media monitoring and mining that data for customer sentiment might choose a different one. Another business might be interested in combining this sentiment data to guide future product development, and would choose a different sentiment analysis tool.

Here are five sentiment analysis tools that demonstrate how different options are better suited for particular application scenarios. These tools are not in a ranked order. 

  1. Talkwalker. This tool is sensitive to emotional nuance; it can detect sarcasm, for instance. It also can detect trends in external data from a given market, making it possible to compare brand performance against other brands.
  2. Clarabridge. Clarabridge integrates a customer's sentiment results with other contextual data, including surveys, online reviews and emails. This feature helps generate predictive insights into customer behavior.
  3. MeaningCloud. This tool gathers sentiment data and integrates new data with old data. Use this analysis feature to observe long-term sentiment trends and behavioral shift over time in a customer base.
  4. Aylien. This tool's focus is the text found on news websites. Rather than being customer-specific in its sourcing, it surfaces brand mentions in articles, reviews and press releases. Use it to gain insights into brand performance in the marketplace.
  5. Mention. Mention scans for mentions of the brand on social media and news websites, as well as in search results. It categorizes the results as positive or negative. Mention also has a competitor comparison feature. Consider Mention if social media sentiment is the organization's only real sentiment analysis interest.

The social-media-friendly tools integrate with Facebook and Twitter; but some, such as Aylien, MeaningCloud and the multilingual Rosette Text Analytics, feature APIs that enable companies to pull data from a wide range of sources.

How to use sentiment analysis for customer feedback

A necessary first step for companies is to have the sentiment analysis tools in place and a clear direction for how they aim to use them.

Sentiment analysis tools show the organization what it needs to watch for in customer text, including interactions or social media. This is more than a matter of scanning for positive and negative keywords. Patterns of speech emerge in individual customers over time, and surface within like-minded groups -- such as online consumer forums where people gather to discuss products or services.

The organization can choose certain keywords that sentiment analysis software detects, such as:

  • It's great or it's terrible;
  • It's simple or it's difficult; and
  • It's economical or it's expensive.

However, they can also include word groups, such as:

  • I didn't like it;
  • It doesn't work; and
  • It's easy to use.

Sentiment analysis software may also detect emotional descriptors, such as generous, irritating, attractive, annoyed, charming, creative, innovative, confusing, lovely, rewarding, broken, thorough, wonderful, atrocious, clumsy and dangerous. These are just a few examples in a list of words and terms that can run into the thousands.

Sentiment analysis software notifies customer service agents -- and software -- when it detects words on an organization's list. Sometimes, a rule-based system detects the words or phrases, and uses its rules to prioritize the customer message and prompt the agent to modify their response accordingly. The customer's feedback determines the agent's course of action.

Businesses can use the sentiment in customer feedback for strategic planning, product design, marketing campaigns and internal process improvement.

Sentiment analysis use cases

When considering sentiment analysis tools, it's helpful to think through the use cases.

Social media monitoring and brand management. Companies can scan social media for mentions and collect positive and negative sentiment about the brand and its offerings.

Customer service response. Bolstering customer service empathy by detecting the emotional tone of the customer can be the basis for an entire procedural overhaul of how customer service does its job.

Product analysis. When a company puts out a new product or service, it's their responsibility to closely monitor how customers react to it. Companies can deploy surveys to assess customer reactions and monitor questions or complaints that the service desk receives.

Companies should also monitor social media during product launch to see what kind of first impression the new offering is making. Social media sentiment is often more candid -- and therefore more useful -- than survey responses.

When harvesting social media data, companies should observe what comparisons customers make between the new product or service and its competitors to measure feature-by-feature what makes it better than its peers. This data can become part of the product's evolutionary lifecycle.

CRM enhancement. Companies can use customer sentiment to alert service representatives when the customer is upset and enable them to reprioritize the issue and respond with empathy, as described in the customer service use case.

Customer service platforms integrate with the customer relationship management (CRM) system. This integration enables a customer service agent to have the following information at their fingertips when the sentiment analysis tool flags an issue as high priority. 

  • the customer's preferred channel of contact -- especially if it's a different channel than the one they used to make the complaint;
  • the customer's journey stage, such as whether they are a new or old customer; and
  • the customer's frequency of issues in the past.

This information can help the agent decide how to respond -- not just with heightened empathy -- but with greater and more focused effectiveness.

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