A lesson from The IT Crowd

Hello. IT. Have you tried turning it off and on again?

Source: The IT Crowd
Speaker: Roy Trenneman

On numerous occasions, we have a one liner requirement that require us to implement the request in our application system. Getting such a requirement always lead me to seek solace in a the famous funny classic show “The IT Crowd”. The phrase is one of my favorite in the show because I always feel like a reboot could be great!

Eager to Please

We are sometimes the result of our own action. It is natural to please the customers without know why they need it. Usually, the person conveying the message are not a suitable person to articulate the customer needs. Thus, do we “shoot the messenger”? Unfortunately, our old code of chivalry have prevents us from doing so. So, what are the next course of action we should consider?

Customer is Not always Right

Firstly, the organization must understand that customer is no longer always right. It is interesting to find that many demands are actually made from internal to implementation team with the thought that giving what customers wanted will please the customers. In summary, the purpose of giving is to please the customer. It is time to throw our old ways and consider a new way of handling requests.

Shooting the Messenger

A request should not always be translated to a task or action. This enlightenment means we must drop our chivalry and “shoot the messenger. Of course, this is not literally and meant that we must drop the practice of the reliance of passing the messages. With Cloud technologies, data can be easily be obtained to confirm or disprove requests. The key is to understand what customer really needs.

Customer Experience

After “shooting the messenger”, organizations shall turn towards a new role called Customer Experience (CX). This role is like an ambassador who learns, understand and even feel the customer wants and needs. The role go beyond passing message to needs analysis and “deep learning” to provide a positive experience aka consumer satisfaction (You could refer to my initial study on overall consumer satisfaction). Deep learning requires capture of data points to measure with the purpose of improving customer experience. So, is your organization agile enough to take the step to “shoot your messenger” and transform to Customer Experience?

Meanwhile, I can only stick to Netflix watching The IT Crowd.

A basic CX read, Customer Experience: What, How and Why Now


A Quick Recap of My Consumer Satisfaction Research

It was 2009 when I had published my first paper Multi-Dimensionality of Overall Consumer Satisfaction – Socio-Technical Perspective. Fast forward more than a decade later, I feel it is a good time to do a quick recap to see if the gaps I identifies are still relevant for 2021 and beyond.

✅ Multi-channel Environment

Looking at current trend, Multi-channel environment have been the correct identified settings in the research direction. However, Multi-channel is now superseded by Omnichannel. This shows that the divide between online and offline contexts are now almost negligible and encompasses various physical location, virtual world, formats and applications. You could check out OmniChannel Marketing: The Roadmap to Create and Implement Omnichannel Strategy For Your Business for more information.

✅ Overall Consumer Satisfaction

A decade ago, the measurement of satisfaction was often done with satisfaction survey that was laborious and time consuming. With so many satisfaction types identified in the conceptual framework, it was nearly impossible to collect all these data. With the advancement of technology in data analytics, it is now easier to collect data to measure these satisfaction variables. The conceptual framework is more relevant and be measured in the real world.


Overall, I am happy to see that gap of multi-channel have been superseded by omnichannel and measurement of overall consumer satisfaction is made possible with data analytics. However, more can be done to made easier for users to setup on how we can improve overall consumer satisfaction across omnichannel. One such method is to use machine learning to compute the satisfaction index based on the identified satisfaction variables and suggest improvement recommendations.