Enhancing the customer complaints system with AI tools

The number of customer complaints has reached its all-time highest in the UK. According to UKCSI calculations, British firms spend around £9.3billion every month to handle complaints.

Customer satisfaction is becoming increasingly important in today’s data-driven world. Providing a positive customer experience will build customer relationships and ultimately translate into increased sales. Contrarily, when complaints from dissatisfied customers are not resolved in a timely manner, customers will naturally move away from the brand. 61% of customers will do business with a competitor after just one bad experience. Even if they stay with the company, complaining to their friends or on various social media platforms would undoubtedly negatively affect the company’s reputation. In serious cases, complaints could escalate into professional liability lawsuits when not handled well. This is why it is essential to handle customer complaints promptly and preemptively. 

 

A traditional Customer Relationship Management (CRM) system is sometimes integrated into a company’s customer services system and adopted as a tool to handle customer complaints. Its main function is to centralise and organise information about customers to easier manage customer relationships. Since it is not primarily designed to handle customer complaints, there are limitations in deploying a CRM system in this context.

Lack of prevention focus

CRM systems are built on pre-existing data; they analyse what is already there, organise such data, and provide feedback to the company. Unlike other data, such as those used for sales and marketing, complaints can be resolved without pre-existing data. The quicker a complaint is dealt with, the better it is as the less likely escalation will occur. Other than reacting to a customer complaint, an effective complaint handling system should also be proactive. This is because only 1 in 25 unsatisfied customers complains directly to the company. The overwhelming 91% of those customers who don’t complain will find an alternative service or product. To retain customers better, companies need to find dissatisfied customers proactively. 

 

Existing CRM systems lack the functionality of prevention. However, by deploying advanced Natural Language Processing (NLP) technology such as Deriskly’s, complaints can be handled proactively and preventatively. Deriskly can extract and categorise available data from diverse sources, including emails, documents, social media, and websites – in real-time. This real-time analysis can act as a risk assessment to flag up the early stages of a customer complaint, even when it is not made directly to the company. For example, if a disgruntled customer discusses their disappointment in the product or service on an online forum, Deriskly can instantly detect and analyse this feedback. 

Deriskly’s preventative risk assessment tool is useful in:

  • streamlining the complaints handling system
  • notifying the appropriate department in the company to follow up with the customer 
  • providing the company with an opportunity to resolve complaints proactively

Being responsive to customers’ feedback sets up a positive cycle for the company to improve their products and services. 

 

Categorising complaints

Complaints may be multi-dimensional. A CRM system helps categorise complaints by providing a simple ticketing system with a basic dropdown list. However, reducing multifaceted issues into one simple category may not fully encapsulate the attributes and urgency of the complaint. Instead of improving the efficiency of the complaints handling system, it may lead to situations where customers have to repeat their issues to different company representatives. 

According to a Bloomberg report, customer frustration primarily comes from repeating their issues to different representatives. 81% of customers who chose to switch service providers have said that the company could have done something to prevent such a switch. This goes to show how important it is to handle complaints optimally for customer retention purposes. 

 

With the help of NLP, keywords and phrases can be extracted to identify the core issues of the complaint correctly. Data accumulated from AI analytics can also filter genuine complaints from general inquiries. Combined with human intelligence, a more effective complaints handling system can be created; customers will be directed to the appropriate personnel quickly, the customer support system will not become overwhelmed, and resources will be better allocated to support prospective customers.

Conclusion 

Customer complaints are important. Complaints innately sound negative, but actually, are one of the most valuable sources for a company to understand how to improve their product or service. An efficient customer complaints handling system will increase customer satisfaction and retention. The manner a complaint is handled could be the difference between having a customer turned brand advocate and a displeased ex-customer. According to Salesforce research, if a complaint is handled well, 78% of customers would be willing to do business with the company again. Whilst a traditional CRM system may assist in handling customer complaints, it lacks several core functionalities to handle customer complaints properly. Implementing NLP technology such as the one Deriskly offers will be helpful in optimally resolving customer complaints and providing a positive customer experience.

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