Deploying employee-centric Artificial Intelligence (AI) to repair competency gaps for individual agents, reduce errors and optimise operational efficiency
It occurred to me the other day – whilst crammed into a doubtless Covid-infested tube on the Waterloo & City line – that the monotonous “please mind the gap” announcement really was quite prophetic – and it triggered me to write this article.
I thought it was a wonderful phrase that underscored the considerable operational performance improvement opportunities open to mid to large contact centre operations. The mind (brain) isn’t a perfect learning organism and just because an employee may have been taught something, it certainly doesn’t mean they have learned it, retained it and can translate theoretical training (aka knowledge-based training) into in-role competence
Quantifying the ‘agent knowledge’ gap
It is fair to say most employees suffer a considerable ‘gap’ between what they have been taught and what they have correctly learned and retained.
We have quantified this particular ‘gap’. Our AI – Clever Nelly – conducted over 100 million individual knowledge interventions in organisations (not dissimilar to yours) last year, and the diagnostic data point showed that – on average – employees knew just 54% of what they have been taught. Of course, humans being humans, the actual knowledge gaps also differed between individual agents; making fixing the problem all the more challenging.
Imagine the impact on your contact centre efficiency if your agents presented as A-typical and scored an average (specific to your business and their role) knowledge score of just 54%? Now, imagine if you could fix that ‘gap’, gently, respectfully – in the flow of work – at minimal cost and with zero impact on agent availability to work.
Closing the ‘agent knowledge’ gap
The good news is you can. Increasingly, more and more contact centres are now realising that traditional training methods do not necessarily address how (often) knowledge-heavy training translates to specific in-role competence. Sure, it’s important that agents understand what they need to do (the theory) but – crucially – they need to develop the competence of applying it on the job.
Almost every agent faces this dilemma; and it is made all the more acute in a hybrid environment where peer-to-peer learning is reduced. Speed-to-competency, for example, isn’t about speed to complete training; it is about the time it takes an agent to translate the training into in-role competencies.
Few Learning & Development practitioners need any convincing that agents forget a lot of what they are trained. Few would also debate that this is not problematic and erodes value from training interventions and employee onboarding. And almost all of them would unite in agreement that fixing this problem using traditional methods is little short of impossible, which is why – broadly speaking – nobody has until AI has made it possible.
On the basis that no human can act on – or put into practice – training they have failed to learn, or a competency they don’t have, identifying – and then subsequently fixing – these individual agent knowledge and competency gaps is the first step. As previously expressed, it is important to recognise that these knowledge and competency gaps are often unique to individual agents, which should mean that training methods must also be unique to individual agents and their requirements.
One (traditional) option would be to re-train all agents in the contact centre, perhaps on some sort of regular interval (every year). The trouble with this approach is that it is very time-consuming and inefficient. By its very nature the training must remain generic, so if an agent already has strong knowledge/competency in a subject, the time spent re-training is largely wasted. On the other hand, if an agent has weak knowledge/competence in a subject, the generic training is unlikely detailed and specific enough to help them achieve mastery of that subject matter.
Many contact centres recognise this approach is too expensive and ineffective and thus find themselves limiting this “refresher training” to what is mandated by legislation of the markets they serve. Typically, regulatory-type training; especially in the financial services markets, for example.
We also know that agents, particularly the younger generation, who have grown up in a digital world, where personalisation is pretty much a given, dislike the one-size-fits-all nature of this approach. It might be the cheapest mass-market solution, but it is disrespectful, disenfranchising and ineffective. If it worked, agents would not present with an average baseline in-role knowledge level of 54%.
Training to move the ‘performance dial’
Most organisations strive to do their best whilst acknowledging that agent knowledge fade and competency shortfall is an “operational fact of life”, which – when combined with typical agent tenure – is filed in the “too-hard-to-fix” box. However, the fact is that technology exists that easily fixes this; technology that enables organisations to quickly, easily and permanently fix the agent knowledge gap to deliver sustained improvement to the business.
Increasingly, contact centres are now turning to agent-enhancing (not agent-displacing) technology to add greater value to their businesses by using employee-centric technology to specifically target poor performing KPIs or recurring and value-destroying errors.
The interesting point here though is that few organisations curate specific training aimed at targeting individual KPIs. Take, for example, First Contact Resolution (FCR). All service providers want this KPI as high as possible, as every single repeat call costs money and drives lower customer satisfaction. Thinking about training though, we have yet to meet a firm who has a training course/module aimed specifically at FCR and that’s because good FCR is a function of broader agent competence, and it is hard – if not impossible – to curate a narrow or condensed individual training course for this outcome.
Similarly, when we consider error reduction, research suggests that two primary drivers of poor FCR performance within organisations are attributed to ‘repeat agent errors’ (38%) and ‘agents failing to follow organisational policies and procedures’ (49%); meaning that the overwhelming majority of poor performance drivers are directly manifested by human error in the workplace .
Learn more about the benefits of deploying employee-centric AI
Minding the ‘agent knowledge gap’ is likely to be a reality in your contact centre today – and it can be easily and cost-effectively fixed. By closing the knowledge gaps, the value to be harvested by your business would be sustained and materially outweigh the investment in the technology.
So, if you are looking for new strategies to improve people performance and operational efficiency in your contact centre in 2023 – in a way that your agents will enjoy (nine out of ten employees prefer a personalised approach to learning when compared with traditional training methods), get in touch and speak to a member of our team to learn more about the benefits.
A version of this article was first published by Contact-Centres.com. Available here: Mind the ‘Contact Centre Agent Knowledge’ Gap – Contact-Centres.com
 SQM, available here: https://www.sqmgroup.com/resources/library/blog/fcr-metric-operating-philosophy