Gaining a true insight into customer experience can be difficult to achieve because customers tend to only offer feedback when they have a negative experience and keep their own counsel when they have a positive or satisfactory encounter.
To get real insight into what is causing customer effort and reduce that effort in order to increase retention rates, it is imperative that enterprises don’t just analyse the contact or interaction data, but also look into all relevant data points along the customer’s journey to get insights.
In the contact centre environment, we had a look at what agents were doing on a transactional level on their desktops, and have enabled them to access certain personal info and profile info when a customer identifies themselves.
A key issue we have found is that there is contact centre infrastructure which can’t tie into other channels. By attaching customer data to an agent’s desktop so they can analyse a customer’s activity in real time as they are communicating with them, better outcomes can be achieved from those interactions, as agents can use the info they have to hand on that customer to provide a more targeted service that understands their preferences and be more intuitive.
By having a combination of data and the ability to repurpose data or tie all customer interactions together to determine all customer effort, enterprises can better identify customer pain points, bottlenecks and abandonment causes and rectify those issues. It’s all about being more intelligent.
Australia has been a bit late to follow when it comes to customer analytics. There remains a reliance on SLAs and NPS, although the signs are that we are starting to move away from that and becoming more innovative in our approach to customer retention.
We certainly have innovators here in Australia but we are typically more conservative, and as such we are lagging a little behind, particularly in terms of customer effort analysis, which we are not really seeing in Australia today.
Around 90% of organisations still rely on metrics like NPS. What they need to understand is that if they don’t have a greater range of information on their customers, they can’t deliver better customer service. Having a high level of customer insight is a cornerstone of what feeds into AI.
It’s not just about tracking contact interaction data but also the reason people are contacting you in the first place. One of the biggest things a lot of contact centres currently fail to do is harvest the most relevant source of insights they have, which are the agents themselves. They should be asking agents to provide real-time insights at end of each customer interaction in order to identify issues and improve operational performance.
Typically, agents don’t give this type of feedback as they think it is ignored, but if it is built into their processes and recognised and valued, it will become second nature to do so. Agents are often aware of significant issues in the call centre that the business may not be aware of, so by providing agents with the time to offer their insights can be very beneficial in helping to resolve these issues.
That’s where vast improvement in the ability to analyse unstructured data analysis can help – speech analytics, applying capability of CRM notes to look at verbatim comments in customer surveys, and allowing agents to socialise issues.
The cost barrier to entry for speech recognition was very high and as a consequence was also massively prohibitive. But now, with Amazon Web Services or Microsoft Azure, you can establish a five seat centre and gain all of the insight without the initial cost hurdle. Without big data, AI and machine learning falls down.
When one considers that billions are spent on transformation initiatives without real insights into issues and opportunities, it makes a lot of sense to invest in this area.
About the author
Phil Smith is CEO and Founder of QPC Group. He has more than 20 years’ experience in creating and building effective management teams and successful companies.