Nick Frank, Founding Partner at Si2 Partners, discusses the importance of understanding the metrics you are measuring to asses both internal performance and external perceptions of your service delivery in the eyes of your customers, and how the two are closely aligned…
As a Field Service manager, imagine having one Key Performance Indicator in your business that could predict how your customers will experience your equipment. One simple measure that your teams could use as a focus for their primary mission; to ensure customers remain satisfied, loyal and profitable. The limitations of most measures of customer satisfaction and loyalty are that they look in the rear view mirror, in that they ask questions after the fact. Far better to create a leading indicator, but how?
To get a better feel for customer satisfaction, many managers spend time in the field talking to customers and their teams.
Some will create rafts of measures to monitor and improve their operations. Their logic being a great performing team is more likely to have loyal customers. However there is a temptation to measure everything, which can start to confuse teams.
The key challenge is to create measures that drive the right behaviours and culture, and not ones where people start to find ways of working around. So it is not quite as simple as many make out.
This balanced approach is pretty sensible, but a can be too ‘management speak’ for the people at the sharp end of the business.
The key challenge is to create measures that drive the right behaviours and culture, and not ones where people start to find ways of working around. So it is not quite as simple as many make out. From my own experiences of managing a european service operation, I always felt it would be extremely beneficial to develop a simple measure that was:
- Easily understood by everyone.
- That gave us a forward view that a particular piece of equipment was potentially going to lead to severe customer irritation and dissatisfaction.
Our business was injection molding systems, and we knew that something was going wrong in the customer when the spare parts spend of the machine increased, fault reporting was high and the same problem re-occurred over a 12 month period. We created a ratio of these 3 indicators and found that at a machine level, we could start to rank problem systems and identify those that were likely to turn into an irate customer.
Our thinking was that not only could this be used by the local teams to bring focus to a specific customer issue, it also gave an indications of how well teams were managing their installed base. Unfortunately for a number of reasons we were unable to operationalize this strategy and I often wondered how effective it would have been.
Recently I heard Mark Noble, Customer Support Director at Inca speak at a Service Community meeting in the UK. Inca design and manufacture digital printers and gave themselves the goal to improve the equipment productivity and hence satisfaction of their customer base. For their technology, it is the performance of the print head that controls up to 256 ink delivery nozzles, which is critical to uptime.
By combining 3 key performance parameters of the machine, alarms, nozzle deviations and productivity, Inca could rank their equipment in terms of the likelihood to cause customer dissatisfaction. They created simple dashboards that clearly identified the priority machines to be working on.
The analytical techniques are in fact relatively simple, it is more having the right mind-set to try a different approach which is the challenge.
A second example of this approach is at Peak-Service, part of the Qiagen corporation, a €1Bn technical services supplier for medical, analytical and industrial equipment. As part of their transformation journey, they created a customer experience indicator which aggregated measures of machine utilisation, revisits, call response time and call completion time.
They used this to help focus their teams and people on the drivers of customer experience as they moved through a transformation programme. This gave them one measure, which was easy to action and could be used to demonstrate results.
This thinking shows that by using operational data that already exists in most businesses, it is possible to create leading measures that drive action. The analytical techniques are in fact relatively simple, it is more having the right mind-set to try a different approach which is the challenge.
As products become connected through the IoT, so the opportunities to gain greater insight into customer experience and satisfaction will expand. Some might call this predictive and others a big data opportunity, but the name is not important. The critical insight we gain from these examples is that these companies are applying their deep know-how of their equipment and customers business, to identify problems before they happen.
Fore-armed is fore-warned!
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