How to gather and evaluate customer sentiment

What is customer sentiment?

Customer sentiment is a measure of how customers feel and think about a brand based on their thoughts, opinions and attitudes. Positive customer sentiment contributes significantly to higher customer loyalty and continuous positive purchase behavior.

Sentiment can be both quantitative -- like customer satisfaction surveys -- and qualitative, like comments on social media. Customer sentiment goes beyond keywords or customer purchasing behavior. It's a complex set of attitudes, feelings and thoughts that organizations can find on social media, derive from user behavior and solicit from direct interactions with the customer.

Knowing how customers feel about a brand, for better or worse, is invaluable information for businesses.

Customer sentiment can be positive or negative and customers may or may not express it in their communications during or after an interaction with a company or product. It commonly includes emotions such as anger, frustration and happiness. Additionally, sentiment can be a major driver of customer intent -- whether they will make a purchase -- and urgency. However, the biggest opportunity it presents is for organizations to capture negative sentiment and act upon it.

For example, "I think the new website design really works" is positive, while "The new website design doesn't work for me" is slightly different, yet is negative. It's useful feedback that identifies opportunity for improvement.

Tools to gather customer sentiment

The first step for gathering sentiment is knowing where and how to find it. There are both proactive ways and reactive ways to gather customer sentiment.

Proactively gathering customer sentiment

To proactively gather customer sentiment, an organization must reach out to the customer to collect feedback. It can do this in person or virtually, such as through surveys.

Surveys

Survey tools enable organizations to ask customers directly how they feel about a particular transaction or their products or services. A customer's direct emotional expression of satisfaction with a purchase provides valuable sentiment data.

Various types of survey techniques include the following:

Point of sale survey. Asking a question at a point of sale -- for example, a comment card at a restaurant -- captures the immediate effect of a customer interaction.

Customer satisfaction survey. A CSAT survey may come immediately following a customer interaction or later.

The CSAT is a Likert scale measure -- like a 0-10 rating -- of, "How satisfied were you with your experience?" It gauges customers' receptions to product changes, a service's effectiveness, a campaign's success or another event where customer response matters. The score is simple to calculate, and businesses can add it to any CRM analytical process.

CSAT is most valuable to gather general trends of key items. It also enables organizations to dive deeper into free-form comments and whether respondents are open to follow-up conversations.

Net promoter score. The NPS is another type of survey that identifies the likelihood that a respondent will recommend a company's products or services. This score is a strong measure of sentiment, as it reduces emotion into data for analytics.

NPS is frequently a back-end measure of customer loyalty taken from surveys, recommendation requests, customer interviews and service follow-ups. Like CSAT, it's a simple calculation that's easy to incorporate into any customer data collection process.

NPS is generally based on a Likert scale broken into three categories: Promoters, who are very satisfied and loyal customers; Passives, who may be loyal but are open to the competition; and Detractors, who are unhappy customers who prefer other brands. NPS takes the percentage of promoters and subtracts the percentage of detractors.

Mystery shopping. Organizations may choose to create an in-house program or hire an outside agency as mystery shoppers. This means they act as customers and go through all the steps a normal customer performs, up to and including purchasing and using the product. The mystery shoppers then complete documentation, such as surveys.

Mystery shopping surveys may also include NPS ratings. However unconventional this technique might seem, it leads the pack in terms of the resulting data quality. Information from mystery shoppers has high authenticity, broad utility and can help generate unanticipated insights.

In-person customer feedback

Asking customers directly is another effective method of gathering customer sentiment. The biggest risk with this method is the challenge of getting valid sample sizes.

The main ways to get in-person customer feedback are the following:

• Customer focus groups. Gathering several customers in a structured focus group session helps gather feedback and dig deeper into specific comments made during the session. However, customers may raise issues that are not widespread but affect a specific individual.
• User interviews. While more labor intensive and time-consuming, individual user interviews enable an organization to focus on specific issues and dig deeply to fully understand the details. Often, a follow-up to a negative survey result is an opportune time for a user interview.

Reactively gathering customer sentiment

Customers express their feelings about an organization in various ways, including both solicited and unsolicited. In the past, customer service was the main way to capture this type of information. Now, with social media, there is a never-ending opportunity to capture it.

Social media. Capturing feedback on social media can be rewarding, yet also complex. Posts and conversations between customers on major social media channels, such as Facebook, Twitter, Instagram, etc., offer a fly-on-the-wall perspective on which products and services customers want and what they do or don't like. However, this methodology can snowball, as many customers can hop on a trend and add their input. Social media feedback may not provide the level of depth needed, but can help organizations better understand an issue.

Likes. Less revealing than social media mentions and discussions, likes within apps such as Twitter and Instagram present products to customers and ask for binary feedback, such as do you like/love this? Despite being a thin measure, it's easily managed and offers a simple ratio.

Online reviews. Product reviews are likes on steroids. They both reflect a thumbs-up for the brand and provide the why of the thumbs-up -- or, conversely, the thumbs-down. This method offers valuable detail and is increasingly easy to harvest. Consumers routinely turn to online review sites such as Yelp, Angie's List and Google+ to see what others think. Even if a business can't work with unstructured data, star ratings are more useful than like counts. An average 4.2 star rating is worth more than knowing 62% of the people who visited the page liked the product.

Employee feedback. As a result of daily interactions with customers and working to resolve their issues, agents who respond to customer service inquiries have a wealth of knowledge of where pain points exist.

Customer service interactions. Most negative feedback comes from service interactions across various channels -- such as phone, email and chat. Customer issues and dissatisfaction flood this point of contact daily. Historically, data collection from here is generally very poor, with the only actionable analytics traditionally being the, "How satisfied were you?" question at the end of an interaction.

But there's more valuable data in this exchange. How intense was the customer's dissatisfaction? What was the customer's tone of voice? What is the customer's history of contacts and complaints? All this information has great value, especially when aggregated with data from other customers.

To gather this data, major contact center and CRM platforms bundle contact center and social media listening software into their systems or use standalone products. Software can monitor all communication channels for keywords and phrases, and the most important analytics are built in. Parsing through this data can be as simple or as complex as the tools allow, answering questions from "Is the product launch going well?" to "What don't they like about the interface?"

Organizations that are serious about sentiment analysis should obtain a tool suite that brings natural language processing and sophisticated data science together, going beyond keywords and brand mentions to extract mood, intentions and other subtleties.

Sentiment analysis tools scan a body of text for emotional expression, then score the polarity of that expression. Did it lean positive? Negative? Neutral? These tools can go beyond scoring negative versus positive sentiment by detecting patterns and sometimes pinpointing specific emotions.

At its most advanced, customer sentiment analysis can break text down to the level of identifying aspects of the product or service referenced and score each one. For example, "I love the product," followed by "The instructions that came with it are really complicated" is a positive and a negative in a single review, but both pieces of information are valuable.

Behaviors representing customer sentiment

Actual customer behavior provides another channel for collecting customer sentiment, and it often follows points of the customer journey.

Web analytics. Analysis of customers' site visits should include the journey they take and specific decisions they make on their journeys. Items to note include changes in volumes of site visits, browsing times and abandoned carts.

Purchase behavior. Analysis of customers' purchase behavior may show how customer sentiment drives what they purchase and how often.

Customer churn. Analysis of customers who have left an organization -- for example, lost cable subscribers -- can also represent customer sentiment, although it is often negative.

Each method of capturing customer sentiment has pros and cons, so organizations should use several of them and look for common points in the feedback. Regardless of the tool used to capture sentiment, organizations should step back and think about what customers say before acting too quickly.

Examples include the following:

• When reviewing a group of media posts, was there a unique outside influence that may have driven customer sentiment to be positive or negative?
• When holding a focus group, did a participant have a challenging experience with a customer earlier in the day that may have biased their comments?

Act on customer sentiment

All this data plays a role in supporting business decisions, such as product development and service policy. But it's not enough to have the numbers and see how they trend. The data itself raises important questions, so businesses should analyze it and take appropriate action.

When a customer's rating or answer changes, businesses must understand why. What is the timing? Is there an upgrade or new release of a popular product or service? Did a competitor launch new features that are drawing customers away?

A customer's positive rating going negative affects the overall numbers of a product's popularity, but it's also a cue to reach out to the customer for more data to address problems and determine what the business can do differently.

Additionally, organizations that use sentiment as the basis for action must view it as a whole. While they should constantly monitor for important cues, they should also keep an analytical eye on the general trend of sentiment where the brand is concerned.

Monitoring whether the same customer praises a brand repeatedly in recommendations or on social media is also important, as this person may be a potential brand ambassador -- a customer who can be incentivized to promote the brand to others.

Businesses can gather customer sentiment across numerous listening posts. When properly studied and interpreted, it can show the why of underlying customer behavior. This behavioral cue can signal to an organization what to do next to improve the customer experience.

Editor's note: This article was originally written by Scott Robinson and expanded by Scott Sachs.