Measure for success
Traditional call centre metrics have increasingly become a barrier to exceptional contact centre performance, and Sabio’s Consulting Director, Kenneth Hitchen believes it’s time that organisations shifted their focus towards more relevant, customer-focused metrics.
The problem with the old approach is that average metrics always hide inconsistent performance. Here Kenneth explains why it’s much more sensible to concentrate on more balanced measurements – such as Net Promoter Scores or real time customer feedback – that always match quantity with a qualitative aspect.
Organisations don’t have to look far to find a measurement approach that could work for them, whether it’s post call IVR or feedback survey solutions, datamart mining, ACD statistics, scorecards from vendors such as Verint, or intelligent reporting approaches such as IQ from Avaya.
We all recognise the classic contact centre performance report – it’s quantitative, detailing the number of calls received, how quickly they were answered, and how long they lasted – the average handling time (AHT). These reports focus on average statistics, and we tend to use them because that’s the format that our ACD equipment typically generates. Many organisations just accept whatever these figures, and – providing they’re broadly around the 80/20 mark – they tend to be happy.
However, the problem with average statistics is that they often lead to average performance levels, and as a result, contact centres get locked into quantitative assessments that do not provide the insight needed to move an operation forward. Contact centre managers are not asking critical questions ‘what data is required for change?’ and ‘how do you acquire it?’
It’s widely recognised that in order to change any organisation or operation you need to alter what you’re measuring. Businesses need to move away from traditional contact centre metrics and be aware that by concentrating on averages they are effectively hiding the performance variants that can help them to improve key factors such as quality or agent adherence.
Moving towards more granular metrics
Organisations ought to start looking for more granular metrics that will allow them to identify performance exceptions and adopt an approach that shifts the focus from quantitative measures - such as abandoned calls - to a more balanced set of metrics that introduce quality into the mix. By measuring over time, contact centres can start to look into more meaningful metrics such as quality scores based on a sample of calls. Once these types of measurements become established, organisations can start to use exception-based metrics to build Balanced Scorecards featuring essential performance drivers such as First Contact Resolution and customer satisfaction.
Once the core operational metrics are in place, it’s possible for contact centre managers to apply the same approach to other key parts of their operation, including customer contact history, self-service, customer feedback and agent classification.
With contact history data, for example, managers can move from logging summary quantitative data on call volumes to keeping notes on individual customer records. They can also start to look at a customer’s contact history across multiple channels, storing the data within a more broadly accessible business application, with the ability to access contact details on an ad hoc basis. The next stage would be to put a more in-depth contact centre datamart in place across all channels, with recordings of actual transactions and details of interaction outcomes.
Capturing self-service metrics
You can also apply the same escalation of metrics to the self-service process. Perhaps initially only recording high level volume data on the numbers of customers or transactions flowing through the web and IVR self-service channels. A next stage would be to track data on self-service usage, with success and failure reporting, as well as the ability to zero in and identify any points of failure in the process. Ultimately the goal would be to have a record of all self-service interactions – including success/failure markers – stored in the customer record.
Customer feedback data plays an important role in providing an external counterbalance to internally generated records. You can start the process with quarterly customer satisfaction surveys – carried out by mail, by email or via a call – that cover all aspects of service. The next stage is to gather more data, maybe with monthly surveys or by setting up a customer panel that lets you gather feedback around new products or changes to your service. Looking more deeply, organisations can progress to post interaction surveys across all channels, gathering customer viewpoints, incorporating social networking opinions as well as customer satisfaction surveys and panels.
It’s also important to gather data from the agents. It’s surprising how many contact centres don’t actually log post contact classification data around the reasons for a customer’s contact and its outcome. You can start with simple categorisation in a standard business application, collecting reasons for a call and providing the ability to search and report on this field. The logical progression here is to move towards multi-tier post contact classifications by the agent of the reason for the customer’s contact as well as the outcome. Automating the agent’s entry into the customer record can provide managers with an element of real time reporting, and provides the ability to carry out ad hoc queries to establish the reasons for current contact volumes.
Establishing analysis and reporting processes
There’s no point collecting all this data unless you do something with it. First stage reporting lends itself to regular manual reports that can be sent to contact centre stakeholders with high-level summary metrics. Progressing forwards, regular automated reports that feature a balanced spread of service metrics can be broadcast to stakeholders at regular intervals. This kind of data is also available for ad hoc queries. A more advanced reporting process would be to deploy analysis and reporting tools to allow more complex queries to be made against specific transactional data. Here organisations might use the latest speech generation tools for more meaningful interrogation of raw contact records.
They say you are what you measure. We believe that if you limit your performance measurement to the most basic record keeping and reporting, then you’re probably going to limit the overall potential of your contact centre operation. A business that makes sure its agents are performing efficiently, that takes note of what its customers are calling about, that asks them for their feedback, pays attentions to how their brand is being discussed on social networks, and actively measures their customers’ propensity to recommend their service to others (using Net Promoter Scores), are much more likely to be getting things right and moving forward. The right measurements really can make a difference.