Using Contact Center Intelligence to Detect Customer Disappointment

It's no secret that customer disappointment can have a severe impact on your bottom line. Research shows that it can be up to 25 times more expensive to invest in new customers than to retain existing ones (Invesp). After just one bad experience, around 80% of consumers say they would instead do business with a competitor (Zendesk).

Customer expectation is at an all-time high, and contact center intelligence is more important than ever before if you want to meet those expectations. Today, we will explore how you can use Contact Center  Conversation Intelligence to not only get a sense of when your customers are disappointed, but also find the root cause of their negative feelings and find it early, giving you the highest chances of mitigating a bad situation.

How Customer Disappointment Impacts Your Bottom Line

There are many factors that can lead to customer disappointment. Maybe they had an issue with a product or service and didn't feel like their concerns were taken seriously. Or maybe they felt like they were being treated as just another number instead of an actual person. 

When customers are disappointed, it's not just about the individual transaction or interaction. It's about the cumulative effect of all those disappointments over time. And that's bad news for companies because it means they are losing out on business from unhappy customers who are likely to take their business elsewhere. In fact, according to some estimates, as many as 78% of customers have backed out of making a purchase due to a poor customer experience (Glance). 

On the flipside, promoters are much more likely to recommend your company to others, which brings in new business, and they also have a customer lifetime value that's 600-1,400% higher than that of detractors (Bain & Company). In other words, happy customers are worth their weight in gold.

As you can see, customer disappointment can have a serious effect on your bottom line. But what exactly causes customers to become disappointed? And how can you prevent it from happening in the first place?

One way to get to the root of the problem is by using contact center intelligence. This technology can help you track customer interactions and identify patterns that may be indicative of dissatisfaction. By taking advantage of this data, you can make changes to your processes or policies to help improve the customer experience and prevent future disappointment.

How Contact Centers Would Traditionally Try To Detect Customer Disappointment

In the past, call monitoring was the primary way that contact centers tried to detect customer disappointment. This involved recording calls and then listening back to them to identify any signs of dissatisfaction. However, due to the amount of time and effort involved in manually monitoring calls, most contact centers are only able to listen to a tiny fraction (2-5%) of their calls. This means it is impossible to get the full picture as they have to rely on luck to pick the right calls, making this a highly ineffective way to track customer sentiment.

Another way to try and detect customer disappointment is by analyzing call data. This can include things like call volume, call duration, and abandoned calls. If you see a sudden spike in any of these metrics, it could be an indication that customers are not happy with your product or service. However, call data alone can't give you a complete picture of customer sentiment.

Many companies are trying to use NPS scores and surveys to detect negative customer sentiment. You can either call customers after they've interacted with your company or send out a survey via email. Surveys allow you to get feedback from customers about their specific experiences, but they have a very low response rate and are delayed in their response time. In addition, survey results represent an aggregated view of your customer base, making it impossible to follow up with disappointed individuals to make things right.

Tracking Customer Sentiment Outside Of Contact Centers

Customer support tickets are another great source of information about customer disappointment. If you see a sudden increase in the number of tickets, it could be an indication that something is wrong. You can also look for specific keywords in tickets that suggest dissatisfaction. For example, if you start seeing a lot of tickets with the word "frustrated," it's likely that customers are not happy with your product or service. 

Finally, you can also monitor social media to detect customer disappointment. Look for sudden decreases in engagement, negative sentiment, or an increase in complaints. These are all signs that something might be wrong and that customers are unhappy.

How To Use AI To Detect Negative Customer Sentiment

Businesses are always looking for new and innovative ways to improve customer sentiment. One way to do this is through the use of conversational analytics and artificial intelligence.

Conversational analytics is the process of analyzing customer interactions in order to extract valuable insights. This can be done through the use of natural language processing (NLP) and machine learning algorithms. NLP is a type of AI that helps computers understand human language. It can be used to analyze customer interactions to identify negative sentiments. Once negative sentiment is identified, businesses can take steps to address the issue.

While there are many ways companies can utilize Conversational Analytics powered by machine learning — AI in general and NLP specifically — I want to highlight two very efficient ways today:

1. Detection Of Customer Disappointment Through Keyword Extraction & Topics

Contact centers can use AI-driven Voice Analytics to detect customer disappointment very accurately, in almost real-time, and on a customer-by-customer level.

This is done by automatically extracting a list of predefined keywords from call recording transcripts and aggregating them in topics. These topics can range anywhere from "shipping problems" and "returns" to "refund requests" and "angry customers." For example, you could look for the following keywords to identify the feeling of broken trust which can leave a customer with a sense of disappointment after the call:

As a supervisor, you can then review the calls that signal negative feelings to quickly identify any problems or need for follow-up. This allows contact centers to quickly identify areas where customers are unhappy and take steps to address the issues before they lead to order cancellations or even escalate on social media.

2. Sentiment Analysis

Another effective way to gauge customer disappointment is by determining your customer sentiment. This adds another layer of analysis on top of the keyword extraction and topic aggregation mentioned above.

Essentially, a Conversation Intelligence solution would analyze the keywords found in the call transcript and calculate a customer sentiment score. This score can be expressed as a number on a scale of -100 to 100 or as an emoji/smiley face.

Conclusion

Whatever the reason, it's essential to try to understand why your customers are unhappy so you can take steps to prevent it from happening again in the future. While there are manual ways (e.g., surveys, call monitoring, and analyzing call data), they are highly ineffective, take a long time, and are not actionable as they are delayed and aggregated. 

With AI-driven Conversation Intelligence platforms, such as MiaRec, you are now able to identify calls that require follow-up automatically, but you're also able to determine a customer sentiment score that gives you immediate insights into how urgent it is to follow up. This will not only help you minimize returns, order cancellations, and other negative consequences, but will also turn negative experiences into positive ones and increase customer loyalty, sales, and customer lifetime value.