Process Driven Service Excellence
Updated: Aug 15, 2022
Across functions and industries, Lean Six Sigma has served business well as a methodology for eliminating waste and driving operational excellence. The process of identifying problems, collating data, and analysing it to see where improvements can be made has proved very effective for Operations teams.
There’s nothing wrong with Lean Six Sigma as a guiding framework for operational excellence. Where it often falls down is in its execution. The application of Lean Six Sigma methodologies to communications and conversational workflows tends to inflame, rather than reduce, operational inefficiency.
Operational efficiency: The communications blindspot
Business is built on communications. At some point, almost every operational process will require people to communicate – whether it’s having a call with a customer or sending an email to a colleague. In business services and service-focused organisations, employees will spend most of their day engaged in conversations. Quality of service, response times and the customer experience are the drivers of competitive advantage, differentiation and success.
As such, service is fertile ground for process improvement. Operations is well aware of this and has long applied Lean Six Sigma approaches to uncover problems and improvement opportunities. The challenge is that communications and data collection tend to clash – and the product is greater inefficiency.
Business conversations are complex and unstructured processes. They don’t leave behind actionable and useful management information (MI) that can then be analysed for process improvement. Metrics like the handling time of a call or the number of threads in an email conversation are relatively easy to extract. However, if Operations wants to know valuable information, such as the reasons for contact or the sentiment of the customer, this has to be recorded manually.
Traditionally, this is exactly what has happened. Employees and service agents have been required to manually log or categorise conversations after the fact. Logged as unproductive ‘wrap time’ agents usually have a limited window of opportunity to log the conversation before they’re put back in the queue. Time is short and pressure high, meaning mistakes are certain. One Re:infer customer discovered that its customer agents were incorrectly logging customer issues 20% of time. This isn’t just time wasted for the employees, its incorrect insight that could knock operational excellence off-course.
In this way, measuring the problem has become the problem. Employees are absorbed in the task of harvesting inaccurate and misleading MI to feed improvement efforts when they should be focused on service and value creation. The wheels are spinning, but operational excellence is receding further into the distance.
Services excellence starts with the customer
Manually labelling contacts isn’t the only method used to extract MI on service processes and business conversations. Operations teams will also use customer surveys and stakeholder interviews to try and map processes and identify the most widespread issues. But regardless of the method used, the limitation is always the same. These methods don’t scale. You can’t manually extract data from, or analyse, every business conversation. The scale of digital communications in a business is just too vast.
Whether you use manual categorisation or stakeholder engagement, you’ll only ever receive a narrow, largely random cross-section of opinion. You’ll lack coverage and the information you do extract won’t be trustworthy. How can you be sure you are uncovering the most damaging problems and valuable opportunities? If you let this information guide your operational excellence programmes, you’re likely to focus on the wrong areas and achieve negative ROI.
Improving service has to begin with the customer, whether internal or external. By mining their conversations, Operations teams can extract the insight needed to achieve operational excellence. Fortunately, the latest AI tools make it possible to overcome the challenge of scale by extracting valuable MI from communications at scale.
The value of Process AI for Customer Communications
Process AI to analyse customer communications is the use of machine learning and natural language processing (NLP) technologies to extract valuable data and insight from service and communications channels. The latest advances in NLP allow Communications Mining solutions to reliably interpret unstructured communications and transform them into clean, structured data that’s ready for analysis. Most important of all, it does this at speed and on an enterprise scale.
With Communications Mining it’s possible – for the first time – to understand and analyse all of your business conversations. Regardless of whether they are calls, emails, chats or other formats. While tools like Process Mining and Task Mining show you how processes are performed, Process Driven AI reveals why they are executed in the way they are.
This is what makes it so valuable for Operations and operational excellence. Perceptif helps you identify the root causes of operational issues, and shows you how much volume these issues are creating. Process Mining shows you when a process isn’t running efficiently – Process AI explains why.
Not only this, Perceptif also helps you measure the effectiveness of process and service improvement programmes. You’ll be able to see how operational changes are impacting service levels and delivery. If the number of threads per conversation goes down, or a specific issue stops appearing in business conversations, you’ll know you’re on the right track. ROI isn’t just achievable, it’s provable and measurable.
Your customers, employees and partners – the people who interact with your processes everyday – are constantly telling you what’s wrong with your business. It’s just that surveys, interviews and manual logging are a poor way of listening to them. To collect and analyse all of their feedback, at speed and scale, you need Process Driven AI. This will give you the data you need to identify and resolve the issues that are creating inefficiency in the enterprise. Best of all, it frees your agents from monotonous admin and lets them get on with their most valuable work.
Please reach out to our experts to find out more.