Case study
Unlocking efficiency gains in the financial services sector by classifying unnecessary customer support calls
Need
A provider of B2C financial services was receiving a large number of customer support calls. They lacked a structured way of understanding which topics customers complained about most frequently, so they could prioritise resources accordingly.
Solution
Using a combination of computational and human intelligence, we classified customer support calls into distinct topical clusters. Overlaying metadata such as the duration of calls, we helped the client understand both the relative volume and complexity of different types of inquiries.
Process
The analysis identified considerable efficiency gains to be obtained through an updated self-service solution and a revised communications channel strategy, pre-empting a large chunk of support calls. We were also able to pinpoint highly specific deficiencies in current communications, e.g. demonstrating how one specific type of announcement was interpreted as ambiguous by customers, driving completely unnecessary calls.
Cost reduction · Text-as-data
+24,000
customer support calls analysed
35%
of calls could be handled by an improved self-service solution rather than requiring 1-1 human support
5%
of calls identified as ‘self-induced’ due to a misconfigured onboarding flow
Your boutique consumer insights partner
We left traditional consulting to found the human-centric insights consultancy that nobody else was going to.
Based in a former butcher’s shop in Copenhagen, we help businesses make sense of the human needs that drive consumer behaviour.
Partnering with us means dealing exclusively with trusted senior experts. We plug directly into your team, we move fast, and we execute projects from end to end.
“I still think about that slide all the time.”
Lead Consultant, months after we delivered our key findings
Curious to hear more about this project?
It depends ApS
VAT: DK44107244
Guldbergsgade 57
2200 Copenhagen
Denmark