Eric Fischer

Automation: The Power of Observation

Alistair Niederer

Chief Sales and Marketing Officer at Praxidia

21/11/2018

This is the second in a series of blogs from the Praxidia team focused on exploring the themes raised in my colleague Doug Overton’s white paper titled ‘Automation – Are You Ready?’ Doug’s paper explores the realities of planning an automation strategy for customer service processes and in this blog I will be looking at what the paper says about the power of observation.

Doug’s paper explores the need to learn about contact drivers, the real reason why customers get in touch. Unfortunately, I can say with confidence that 100% of all the companies I have ever advised are lacking any adequate mechanism or process for the capture of detailed contact driver intelligence.

So why is this? How does it usually work today?

In a traditional contact centre setup agents will be expected to select a Call Reason Code at the end of each customer contact. They are making a note of why the customer has called, but the Call Reason Code needs to be organised as a dropdown, so the agent is given a list of options to choose from, such as billing, customer care, support, etc.

Usually these are extremely broad topics and not very useful, so companies add a second tier – the agent chooses the top-level reason and is then presented with another list of sub-reasons. In many contact centres, this call reason choice extends to 3-tiers. The agent may have a choice of literally 8,000 options to describe the call reason. No agent ever completes this information accurately – they have 5-6 favourites and they stick to them.

And this is the revelation at the heart of Doug’s paper.

You cannot use your existing call reason data to plan an automated future. It’s like feeding ‘garbage’ into the design of a new strategy – ‘garbage in, garbage out’. You cannot design the right processes or types of service interaction to automate when none of your call reason code information is accurate.

You may think that you have a database of call reasons, but it is almost certainly not accurate. If you really want to plan which customer interactions can be automated then you need to observe what is really driving customers to get in touch. You need to ask questions such as how are they getting in touch? What do they need / what are their symptoms? What are the root causes behind these needs / symptoms? What is the solution, and where does the solution reside?

Most management consultants ask for your data, analyse it, then propose solutions, but when planning an automation strategy you simply have to understand why customers are getting in touch and most existing data is wrong. You need to build a new dataset based on real observation so you have a reliable set of data that describes the real customer drivers.

If you rely on existing data to plan an automation strategy then it is likely to fail. You can only build a successful automation strategy by stepping back and observing your real contact drivers.

Please leave a comment or get in touch via my LinkedIn if you would like to receive a copy of Doug’s complete white paper.

Photo by Eric Fischer licensed under Creative Commons.

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