Monthly Archives: October 2013

Who dares wins

A few weeks ago I was speaking at a conference for a client who wanted to be inspired to move forward with social media initiatives to improve communications, learning and productivity. I did my thing and shared examples of how different tools could be used well for the different goals they had and shared best practices and helped them to discuss risks and opportunities. During the pause, the big chief came to see me and said:

 

“You know Dan, the trouble is that in all these kind of conference sessions we do, we hear a lot of good things, but never any examples of success stories from within our own domain. It was the same with cloud computing, big data, etc.. So I remain reticent.”

 

To be clear: The organisation in question is a large public non-commercial bureaucratic organisation in Belgium and it is true that there are not a hundred relevant examples of other organisations already doing the same thing. But here’s my point:

 

If no-one is willing to try out new things and accept some risk in being the first, how is anyone else going to be able to show relevant examples to the late-adopters? If no-one dares, no-one wins.

 

Am I being naive?

 

 

Big Data for Learning in a Call-Centre

Whilst researching for a conference speech I will give soon for a Belgian government organisation on new learning trends, I have been checking out some of the ideas and literature around Big Data. This is a hot buzz-word with a lot of applications in the world of marketing and sales, but I am wondering about its application to learning. I don’t know yet what is truly possible today, but I wanted to share an idea that came to me of how Big Data could help learning and performance improvement in a specific environment: Call centres…

 

When I was Training and Development Manager for Sitel in Belgium (2002-2006) I would regularly meet with my colleague Peter to discuss learning needs. Peter was the head of the quality department. If you’ve ever called a call-centre before, you know those guys exist. They are the ones listening to your calls that may be recorded for quality and training purposes.

At the time, there were around 15 quality monitors for something like 600 call agents. In order to “assure quality” and “assess learning needs”, Peter’s team would spend half of the day listening to calls and assessing quality against a check-list of standards. The other half of the day would be spent side-by-side helping the call agents with whatever issues they had.

Suppose a call lasts 3 minutes and the after-call assessment/admin time might take a quality monitor another 3 minutes. One call treated in 6 minutes. 10 in 60 minutes. That means that in every half-day, 1 QM would hear 40 calls. 15 QMs would hear 600 calls. If we had 600 call agents each taking only 4 calls an hour, that’s nearly 10000 calls in a half-day. Of those 10000 calls a day, 600 are being heard by the QM team. That’s 6%. Heard and helped.

 

What could Big Data principles do to help here?

Imagine that instead of a Quality Monitor listening to only 6% of calls we had a voice and speech recognition tool listening to every call. Programmes within the QM analysis software would recognise key words or phrases, questions or objections and analyse their frequency or position in the call along with changes in frequency or volume and many other data. These data packets would then be laid out against data concerning call-times, frequency of calls and all other previous customer data, time of day, absenteeism in the call-centre, seasonal information and any other data about employment of the call-agent or his team members…. With all the data collected, the machine would run queries on the data, assessing trends. The Quality Monitor would then pull out his report and analyse further, perhaps dipping into more specific and targeted and useful moments of a call-recording in order to bring the all important human ear and evaluation to the data already provided.

In some cases, the machine would recommend specific learning points all by itself. It might, for example, instruct sales agents to use keywords X, Y, Z in sales calls concerning ______ in order to close more sales. It could even provide predictions for staffing and potential quality problems for future promotions or services offered by the company.

In many cases, the quality monitor would be able to spend more time working with the people who need on-the-job training and less time listening to the generic call moments that bring no added-value to performance improvement. We cannot imagine that the work of the QM would be redundant. Absolutely not – those people will be required to make (emotionally) intelligent evaluations that the machine cannot and to analyse in further and more creative ways the data collected.

But in all cases, it is clear to me that such voice-recognition software and Big Data computing power along side good statistical analysis and human evaluation would in this example create improved efficiency and could have a massive impact on learning.

 

Larger and more diverse sets of well-collected and organised data, better needs analysis, with clearer trends and more time to focus on understanding and improvement. Big data for learning.

What do you think? Where could your organisation innovate its learning needs analysis if all the available data could be efficiently captured and quickly organised and treated?

 

Thanks for reading
@dan_steer