Many businesses and government agencies have sound practices and processes to manage the key metrics related to how they deliver customer service. This includes metrics such as Net Promoter Score, First Time Resolution, Call transfers, Escalations, etc.
But when you start to look at what is really driving these metrics, most people have an idea on what it might be, though can’t really say for certain what the root cause is. From our experience, these root causes are often hidden – your analysis needs to sift through the layers to get to the underlying issues.
Once you have been able to peel away the layers through a deep dive analysis, you will be in a much better position to understand root cause/s and put in place actions that will drive better outcomes for your customers and the business.
Sounds straightforward right?
Over the past four years we have completed numerous engagements with Banks, Insurance
companies, Government agencies, and Retailers, all focused on discovering the root causes driving certain metrics, and then providing actionable insights. The methodology we use is based around leveraging our Speech and Text Analytics engine, domain expertise and hands on analytical experience. There is always something of value to be uncovered, though as we’ve learnt it’s often something totally left field that is uncovered and that makes a significant impact.
To make it real and tangible, we will provide 2 key use cases from diverse customers, based on projects we have delivered.
The first use case is below and the second will be provided next week to mitigate the risk of WOWing you all at once.
Use Case 1.
How you greet your customer can significantly impact Average Handle Time (AHT)
In one of our engagements with a government agency, the focus was on how we could help them drive down AHT. After conducting our analysis, we uncovered one actionable insight that they were able to implement within a week, that instantly provided a 5% reduction in AHT.
This revelation was centred on inconsistencies in the way that the contact centre agents greeted customers. We identified through analysis that there was a significant variation in AHT across agents. After sifting through some layers, we eventually split agents into 2 groups (for data profiling) based on their approach to greeting, and it showed a high correlation to AHT. What we saw across the sample of 50,000 customer conversations, was that 50% of the agents greeted customers along the lines of “Hi, I am Jane, how can I help you today”, whilst the other 50% greeted customers something similar to “Hi, I am Jane, can I start with your claim number” … and then once the customer was confirmed asked … “How can I help you today”
The first approach led to a very open conversation that generally ended up with the agent needing to confirm the claim number eventually regardless. The second approach was far more directive with the agent in control of the conversation pace leading to a reduced AHT.
The recommendation was simple – create, implement, and track a consistent opening greeting starting with the customer claim number. The implementation was as simple as the recommendation and was rolled out through a set of memos with sample scripts leading to positive results within a week.
Important to emphasise that on this project we were asked to validate and quantify certain assumptions the agency had around Average Handle Time. This finding was left field and a “gold nugget” as we call it, that presents itself in varying forms from project to project, though quite consistent in that we typically dig them up.
Stay tuned for Use Case #2 next week!
