If you’re like me, you’ve misinterpreted more than your fair share of text messages because you couldn’t determine the intended emotion behind the words. Was the sender being serious? Sarcastic? Flippant? Unless you know the person—and their use of emoticons—figuring out what they really mean can be dicey.
Contact center analytics can run into the same type of issue when analyzing a customer call. It’s not enough to analyze and understand what customers say—the software also needs to understand what the customer means.
Most organizations don’t do a great job of holistically ascertaining customer sentiment. And if they rely solely upon common tools such as surveys and focus groups, they’re only targeting a minute portion of the population and will never be able to truly understand customer satisfaction levels.
The good news is that the tide is turning. With the rising importance of voice-of-the-customer (VoC) data, contact centers are in a unique position to decipher both meaning and context from customer interactions. Thus, sentiment analysis is a powerful tool that call centers managers and customer experience leaders can use to learn more about their customers.
Sentiment Analysis not only tells you your contact center’s overall sentiment score, it also augments additional contact center workforce optimization (WFO) suite KPIs to correlate sentiment with metrics like call duration, hold time, silence, evaluation score and Net Promoter Score (NPS) to allow you to identify trends that until now may have gone unnoticed.
Here’s a short look at how Sentiment Analysis works in the call center:
Sentiment analysis creates a scorecard that brings together both the workforce optimization (WFO) and the Workforce Engagement Management (WEM) picture. All the utterances in a call are analyzed to give each call a sentiment score of positive, negative or neutral. By looking at this dashboard, call center managers can spot trends in call sentiment and can often identify issues before other KPIs like sales or NPS drop.
When your contact center leverages Sentiment Analysis along with your call recording software, you no longer need to manually monitor calls or study interaction transcripts to find out how customers feel about your business.
Here are five of the top benefits of Sentiment Analysis:
1. Capture agent effort often overlooked in typical performance metrics.
KPIs like call duration don’t always tell you how effective your agents are. For example, a longer call can sometimes mean that an agent is adept at handling complex issues. You can use Sentiment Analysis to identify the agents consistently involved with calls that have positive sentiment so you don’t miss out on rewarding—and learning from—your top agents.
2. Send QM evaluators down the right path.
Your evaluators don’t have time to listen to every call to monitor for quality. Sentiment Analytics is able to help identify the agents involved with calls that had negative sentiment—which gives your evaluators a better idea of where to start their reviews.
3. Supplement post-call surveys to amplify Voice of Customer.
Don’t rely on the small percentage of customers who respond to survey requests to learn how your customers feel about your brand. Instead, you can supplement your survey and focus groups results with Sentiment Analysis data so you understand the impact of every interaction.
4. Test effectiveness of marketing campaigns.
Marketers can use sentiment analysis to discover how customers view their most recent ad campaigns, hone in on the most effective marketing messages, find out how customers view their brand or understand how customer sentiment varies by product line.
5. Quickly identify root causes.
By pulling sentiment data into your everyday contact center KPI reports, you can identify correlations that might not be obvious. For example, you can view a line chart showing your rate of customer retention alongside the number of calls with negative sentiment. Then listen to only those calls that are both negative in sentiment and correlate to a decrease in retention to find out why customers are attriting.
Let’s look at a real-world example. A furniture retailer in North America turned to Calabrio Sentiment Analysis to better understand how their customers’ satisfaction varied by product. They evaluated the sentiment of all 49 of their product lines and found that the product with the most positive sentiment score was a line of coffee tables. This was a surprise to them because, to date, the sales numbers for those tables had been only average.
They learned from Sentiment Analysis, however, that customers who owned this particular coffee table were overwhelmingly positive during their interactions with the contact center. The retailer dug into the interactions to find out why—and they learned that customers loved how well the table held up over time and that the table’s finish “never chipped.”
The retailer used this insight to revamp the way they positioned the coffee table in their stores and on their website. Now when customers walked into a store or viewed the company’s home page, the “never chipping” coffee table was front and center—along with new messaging that highlighted the strength of the table’s finish.
And the retailer’s sales went up! Prior to their analysis of customer satisfaction by product line, sales of the table had been flat and relatively average compared to other lines of products. But after they found out how much (and exactly why) customers loved the table, they could change their website and store layouts to better highlight the product’s strengths. The result was an increase in sales to the tune of $400,000.
Traditional processes and legacy tools for understanding customer sentiment are tedious and unreliable, often preventing businesses from seeing a complete customer picture—the opposite of what you want to achieve.
Instead, you want quick, accurate and meaningful customer engagement metrics and automatically updating customer sentiment metrics that you can access via dashboards and customizable reports.
Here are six questions you should ask when evaluating a contact center sentiment analysis solution:
1. Is this solution specifically attuned to sentiment expressed within contact centers?
Nearly every call into a contact center occurs because the customer has an issue they need solving—but that doesn’t mean that every interaction will have a negative sentiment. The right solution will recognize that contact center conversations are unique.
2. Can it detect negation?
Most contact center analytics solutions can’t tell the difference in sentiment between the phrases “That was a terrible response” and “I did not love that response.” The right solution will take a holistic approach that doesn’t base sentiment analysis around a single word or phrase and so can accurately detect the negative sentiment of both statements.
3. Are manual efforts required?
You’re more likely to take advantage of contact center sentiment analysis if it’s easy to use and access results. You’ll want to look for a fully automated solution that delivers sentiment scores directly to your dashboard or report without the need to spend valuable time on manual examination.
4. Does it enable targeted Quality Management?
The best solution will deliver the names of top-performing agents, teams, and groups in terms of sentiment so you can easily enable sharing of best practices—and it will serve as a leading indicator in terms of identifying agents who need manual quality evaluations.
5. Does it allow you to correlate sentiment scores with other contact center KPIs?
The right solution will not only tell you the overall sentiment score for your contact center, but it will also allow you to easily slice and dice sentiment data by key metrics like call duration, average hold time, product line or retention rate—and to marry sentiment info with customer and agent effort scores.
6. Is it accurate?
Look for a solution that leverages machine learning and best-in-class speech analytics engines to deliver sentiment analysis accuracy rates that statistical benchmarking reveals to be more accurate than tools that include IBM Watson.
You’ll also want to look for a solution that makes it easy to analyze and report on the results. Top sentiment analysis solutions give you the ability to:
Sentiment analysis can help companies speedily identify unhappy consumers; gain essential insight into customer perceptions of its brand, product, operations and agent performance, receive automated, straightforward and accurate analysis of customer attitudes, and promptly identify root causes of concern and mitigate problems before they undermine the bottom line. But these achievements only will occur if you select the right sentiment analysis technology.
Want to learn more about sentiment analysis? watch the on-demand webinar: Three Ways to Boost Your Bottom Line with Contact Center Analytics.