How to Use Sentiment Analysis to Improve Your Organization’s Satisfaction Scores

Whether it’s a rating, ranking, score, review, or number of stars/tomatoes/thumbs up, consumers have ample opportunities to report their experiences with businesses, service providers, and organizations in the form of survey responses and other user-generated content. The challenge for organizations remains processing this high volume of data, finding positive and negative sentiment, identifying opportunities for improvement, and tracking the success of improvement efforts.

What Sentiment Scores Can’t Tell You

Numbers without context cannot inform real improvements. If an organization receives a sentiment score of 72, the leadership team may recognize that the score is good or bad but have no information about what is influencing it either way. Worse still, a simple number does not reveal what to do to raise it.

A rational approach to improving is to look at a sentiment score and ask, “What is contributing to that number? What’s keeping us from earning a higher one?”

Analyzing the user-generated content and text-based survey results that accompany numbers, scores, and ratings provides that context and helps guide efforts towards the most significant changes to make. However, manual analysis of text is extremely time-consuming and nearly impossible to do consistently. Without a systematic and efficient process in place, organizations will remain largely in the dark about how to improve their constituents’ experiences.

When Ascribe launched automated text analytics programs CX Snapshot and CX Inspector with X-Score™, they decreased the time it takes to review comments from weeks to hours. These programs perform text sentiment analysis using natural language processing (NLP) and report findings using visuals (charts and diagrams) to highlight trends, trouble spots, and strengths.

X-Score is a patent-pending approach to customer measurement that provides a customer satisfaction score derived from people’s authentic, open-ended comments about their experience. X-Score highlights key topics driving satisfaction and dissatisfaction, helping identify the actions needed to improve customer satisfaction quickly and easily. In this way, companies can cut the number of questions asked in half and reduce the size of the data set without compromising on the quality of data they receive.

Consumer Glass Company Gains Sentiment Data within Hours with Automated Text Analysis

A major consumer glass company serves more than 4 million customers each year across the United States. They received 500,000 survey responses, which produced massive data sets to review. They needed to interpret the verbatim comments and connect them with other data, like Net Promoter Scores® (NPS). This would enable them to understand exactly how to delight every customer every time.

The company tried a standard text analytics software, but its results were inconsistent. The system took days or weeks to model the data and could not connect the sentiment analysis with their NPS data.

Within a week of obtaining Ascribe’s natural language processing tool CX Inspector with X-Score, the software was delivering exactly the kind of analysis needed. CX Inspector now reviews customer feedback comments from surveys, social media, and call center transcriptions, and the company has a strategic way to use the data and trust it to inform sound business decisions. Their customer and quality analytics manager says,

“NPS is a score, and you don’t know what’s driving that score. But when you can see how it aligns with sentiment, then that informs you as to what might be moving that NPS needle. In every instance where I compared CX Inspector output with NPS, it was spot-on directionally, so it gives us a lot of confidence in it – and that also helps to validate the NPS.

“Without a text analytical method like CX Inspector, it is extremely difficult to analyze this amount of data. Ascribe provides a way to apply a consistent approach. This really allows us to add a customer listening perspective to our decision making.”

How Sentiment Analysis Informs Your Organization’s Improvement Efforts

Reviewing the verbatim comments of individuals who have experienced a product or service provides essential data for teams looking for trouble spots, trends, and successes. This analysis of text reveals specific areas of service that are suffering even if other areas are doing well.

For example, text sentiment analysis can find consistently satisfied customers in nine of the interactions they have with an organization, and also highlight that tenth interaction that’s consistently unsatisfactory.

Having this detail empowers organizations to focus their efforts on the handful of areas contributing most to the unhappiness of the people involved (whether they’re employees, customers, patients, etc.). By implementing change and then checking current against previous scores and sentiment, organizations can also measure their progress.

Regional Bank Experiences 60% Decrease in Processing Time

Recently a regional bank approached Ascribe in search of a more efficient and effective tool for finding positive and negative sentiment from customer feedback and analyzing it for specific changes that would enhance their customers’ experiences. They had been consuming thousands of man-hours reading, categorizing, and analyzing comments. Yet they still struggled to identify the most important themes and sentiment, and the manual process was inconsistent and unable to show trends over time.

In minutes, Ascribe’s CX Snapshot processed a year’s worth of customer feedback gathered from all channels. It identified trends the team had been unable to unlock using their manual processes, and it quickly identified steps to take to improve the customer experience. The repeatable methodology ensured consistent analysis from month to month, making it possible to track trends over time.

A senior data scientist on the bank’s staff says,

“This new approach is repeatable, powerful, and it expedites our ability to act on the voice of the customer.”

How Asking Better Questions Improves Insights 

Another way to improve the efficiency and effectiveness of surveys is to ask questions in a better way, which reduces the number of questions needed. This works because it captures better data, as the consumer is not being forced to respond to leading questions. For example, asking, “What did you not like?” could lead consumers to provide a negative answer even though their overall experience was very positive. “Tell us about your experience” is more neutral and allows for consumers to give either a positive or negative response.

Asking fewer and better questions also reduces interview fatigue, thereby increasing the number of completed surveys and improving the survey-taking experience. It recognizes that people who choose to fill out a questionnaire usually have something very specific to share. Rather than making people work to fit their comments into question responses, “Tell us about your experience” invites consumers to immediately provide their feedback.

Ascribe’s text analytics tools help researchers in both of these areas. Often, all the feedback needed can come from this single open-ended question: “Tell us how we’re doing.”

Recommendations

Sentiment analysis is an essential component of an organization’s improvement efforts. A robust and strategic approach will automate surveying and the processing of user-generated content like reviews, social media posts, and other verbatim comments. Using machine learning, natural language processing, and systems that visualize the positive and negative themes that emerge can empower organizations to make decisions based on data-driven insights. Today, this kind of automated analysis can review massive data sets within hours and inform organizations of the specific steps to take to improve the satisfaction of their constituents. Ascribe’s suite of software solutions does just this, simplifying and shortening analysis time so organizations can focus on what needs to be improved, and then see better results faster.

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