The UK customer services team of a leading global financial institution was inundated with 60,000 emails daily. Unsurprisingly it was taking up to two weeks for the team to manually categorise the emails for the right department to respond. This meant it was taking up to a month to get back to customer queries. Which naturally antagonised consumers further.
Emails were categorised into three sections: complaints, feedback, and queries. AnalogFolk was challenged to reduce the amount of time taken between receiving an email, categorising it and forwarding it to the correct department to reply.
The answer? Automation.
With machine learning we taught computers a sample of scenarios so it could work out what the general rule and concepts were from that sample, and then apply those rules and concepts to future scenarios.
For this global financial institution, we had a series of support emails that needed to be sorted into three queues. The solution was AI and natural language processing - reading the emails for intent, context, and entities, amongst other things - allowing people to crack on with the more complex tasks that require heightened emotional intelligence.
After all, automation is not about removing humans, it’s about putting people first.
We set about undergoing a four-step process:
Stage 1: Emotional analysis
Firstly, we used emotion algorithms to screen a database of emails. Everything from rage to loathing to grief. Ouch. This highlighted the levels and intensity of emotion so we could gauge the level of complaint.
Stage 2: Identify themes
We then identified recurring themes that were driving customer contact. We highlighted the scale of each theme, levels of intensity and how they matched with the existing three categories. We soon found out that there were in fact 12 common themes, not just the three previously identified.
Stage 3: Tech discovery
Next, discovery time. Using natural language processing (NLP), we were able to discern the intent of an email according to the business rules of the company and automatically triage emails. We built a prototype that tested the hypothesis and ensure the proposal of using NLP to automate the email process was possible.
Stage 4: Recommendation
The prototype validated the use of AI for the automation of the email sorting, and is now being used by the customer care centre. AnalogFolk also provided a recommendation and road map on how the initial pilot could be scaled to start automated replies to customer queries.
This financial institution is leading the way to a new and exciting era in which computers are more human and banks connect directly with their customers.
The initial pilot reduced the time taken to categorise the emails by 500%.
Our recommendation and road map is now being implemented by the internal R&D team with our team providing technical consultancy.
The working model of using AnalogFolk to develop and validate a hypothesis through rapid prototyping before handing it over to internal development teams was a success story for the client innovation team. Allowing them to make their ideas a reality, at a much faster rate than previously possible.