ChatGPT AI opens the door for increased volume and complexity of complaints
An interesting example of how ChatGPT was used to support a customer complaint came whilst having a conversation with a friend recently. They had been driving home and hit a series of unavoidable potholes in the road, resulting in two severe punctures and wheel damage. This ended up being a very costly incident for them to get repaired, not to mention the 5+ hours they had to wait for a breakdown recovery service.
Due to the considerable number of potholes, my friend decided enough was enough and complained to the local council. However, as they wanted to ensure their complaint was taken seriously, they turned to ChatGPT to draft a legally toned complaint covering several points and supported this with photographic evidence. Once ChatGPT had drafted an initial version of the complaint, my friend was then able to refine it further by asking ChatGPT to add a legal style to the draft.
In the past, creating such a detailed complaint would have been difficult and time-consuming to pull together, usually meaning no complaint would have been written. However, using ChatGPT, the email draft was ready in seconds and after some refinement was sent off to the council. The council responded back, escalated the complaint, and the pothole was repaired the next day.
The fight for compensation is ongoing, but the interesting point is that my friend would have never complained without the use of AI. How many other people will start to use AI to draft their complaint emails/letters, removing what would have been a barrier to complaining at all?
Automation and beyond: what’s next for complaint management?
Using automation and AI is not just benefitting the consumer. Equipping organisations to deal with customer complaints is important, not only to help those firms meet their regulatory obligations under the new FCA Consumer Duty, but to use these complaints as a valuable tool for business improvement. In turn, this will help upgrade the overall customer service and products delivered by those firms.
Organisations that have embraced the world of complaints and feedback management solutions are already benefiting from automation technology, where elements of the process can be implemented 24/7 without the need for any human intervention. For example, a form submission (on a website or Smartphone App) can trigger a workflow that automates stages of the complaints process, drastically reducing the number of tasks requiring human input. This very basic workflow keeps things moving, and instantly reduces the workload of case handlers.
You may have heard of terms like Robotic Process Automation (RPA), Machine learning, Natural Language Processing (NLP), and Business Intelligence (BI). Understanding how these can be integrated into the context of complaint management can go a long way in developing a robust, industry-leading solution that provides you with the data to help level-up your organisation.
Robotic Process Automation
Automation of the workplace isn’t new. RPA is looking to do with digital industry what robotics has done so successfully for manufacturing. Like robotics, RPA is highly disruptive. It looks to take manual duties completed by employees on digital platforms and automate them. It can pick up manual, repetitive tasks and perform them twenty times faster than you or I. This becomes massively powerful when you think of it in the context of legacy applications, which rely on physical data input with little to no interfaces to other digital platforms.
Within the context of customer service, automation brings benefits to both organisations and customers. Processes can be completed faster, providing feedback or communications to customers within an expected time window. On top of this, there is a financial element. If things are progressing without the need for human interaction, workers are freed up to perform more value-added tasks within the business.
Machine Learning and Natural language Processing
Complaints, up until five or six years ago, used to be relatively simple for organisations to manage. They followed a tried and tested methodology. As social media has become ever-present in our lives, it has become an extension of the conversations we have in person.
If we remember, RPA automates tasks that we (humans) can carry out. If we can find those complaints on social media, then RPA can manage these. That’s why introducing NLP to the complaints that RPA is identifying can become incredibly powerful for customer service. Complaints are an ideal use case for NLP.
"As a society, we don’t tend to write simple one line complaints, especially when we know our friends and our wider social circle will be reading them. We’ll add detail, colour and most importantly of all emotion, or sentiment. The NLP model will read not just the ‘text’, but the ‘language’. From this, it’s looking to determine a couple of key items to drive an automated, intelligence led process."
NLP looks to identify multiple complaint types and, as well as this, NLP is looking to harness the emotion and sentiment in which complaints tend to be rich. How does the customer feel? Are they angry? Are they upset? Do they want a simple apology? All of these are valid queries that can help drive automated decision-making within a complaints process. This is what NLP seeks to extract and provide.
If RPA has scoured social media to identify dissatisfaction with a service or product, NLP has automatically read the complaint and figured out what the problem is, then we can use Machine Learning to take the outputs from NLP and provide predictive analysis on the steps that should be taken to address the customer’s complaint, automatically.
"The learning model would know what the customer is complaining about, then learn over time for a particular organisation what actions should be taken to address the complaint. The learning would get better and better as it trains using the volumes of complaints received. Its appetite is satisfied by our 24/7 robots that trawl social media for any hint of dis-satisfaction."
The opportunities here for organisations to improve based on feedback they don’t directly receive are enormous. Based on the data at our disposal, we can spot key trends, understand what makes customers grumble, and make decisions to move forward.
Reporting and Business Intelligence (BI)
We’ve mentioned briefly the ability to harness data, collected from cases and complaints, to improve business operations.
BI brings together data mining, data analysis, and data visualisation to give managers and decision-makers a comprehensive overview of data. This can be used to make business decisions in a more informed way.
Including BI platforms in your complaint management system helps you visualise areas for focus. By developing dashboards, you can visualise where the organisation sits in comparison to KPIs (Key Performance Indicators). This helps measure against goals, targets, and SLAs (Service Level Agreements). Using the information for the ‘greater good’ is where real gains can be made; helping guide both operational and strategic decision-making.
The key to a successful complaint management strategy is having a process and dedicated complaints management system that lends itself to ease-of-use, accessibility, and timely communications.
As we all know, complaints should not be used as a yardstick to beat organisations with, as they are the business tools for improvement and innovation. With the FCA’s Consumer Duty and many other regulatory initiatives looking at customer feedback to drive evidence of business improvement, making the best use of complaints and the root cause analysis they provide can help build trust with both your customers and regulators.
To find out how EQ Customer Resolutions can revolutionise your complaints and feedback management capability, please click the button below.