Real-time analytics can curb customer unhappiness

Most business leaders are aware of the incredible power of contact centre analytics when it comes to strategic planning and resource optimisation. But one area where the industry is lagging behind is advanced analytics, particularly real-time analytics. A McKinsey report suggests that just 37% of organisations feel they are using advanced analytics to create value – this means they are missing out on a significant cost advantage and opportunities to improve customer satisfaction.

Understanding the Reasons Behind Customer Dissatisfaction and Unhappiness

To apply real-time analytics for handling unhappy customers, one must first look at the root cause of dissatisfaction and unhappiness.

Most of your regular callers are likely to be loyal customers, and one outlier experience is unlikely to influence their loyalty to the point of churn. Real dissatisfaction or unhappiness is a cumulative result of several poor experiences, and therefore, should be nipped at the bud.

Customer unhappiness can usually be traced back to:

An inability to convey the root cause of the problem to the agent
An underlying frustration with the product quality
Having to repeat information, long waiting times, and other operational inefficiencies
Atypical behaviour on part of the agent, such as compliance violations or abusive language
Lack of first call resolution
Lack of acknowledgement of negative customer sentiment

Unhappy customers can prove very costly for a business. Research suggests that a happy customer will tell three people about their experience, while an unhappy customer will complain to ten people. 13% of dissatisfied customers will tell 20+ people adversely impacting your brand reputation.

In extreme cases, unhappy customers can snowball into customer rage, costing US businesses around $494 billion in revenues.

How Real-Time Analytics Can Curb Customer Unhappiness

Real-time analytics is used to monitor customer interactions as they happen and flag any signals of unhappiness or dissatisfaction. Not only does the data feed into a historical analysis engine to draw patterns, but it also unlocks insights and recommendations in real-time.

Here are some of the ways real-time analytics can help contact centres handle unhappy customers more effectively:

Detect and acknowledge frustration – As soon as customers start negative words/phrases like “don’t know” or “but,” it will use speech analytics to detect negative emotions in real-time. Agents can alter their approach and tone of voice to address potential unhappiness
Anticipate issues before having the customer explain – Real-time analytics can help you fetch purchase details as soon as the customer mentions the product name. If there is any glaring error that could be causing customer unhappiness (e.g., shipping delays), agents can raise the issue pre-emptively
Have a conflict resolution expert intervene – Real-time analytics can automatically request for agent help if it detects a prolonged, negatively worded conversation. A supervisor experienced in conflict resolution can be alerted to step in at the right time
Switch scripts – Sometimes, switching to a different script (like treating an unhappy caller like a VIP customer) can create a sense of reassurance and confidence. Real-time analytics will signal when to switch scripts