Some time ago I wrote piece about Talent Analytics. After some discussions with them I realized that my thinking about talent or HR analytics has been too limited. Sure, I have seen how it might help with the kind of recruiting, team management and merger assessments that Talent Analytics does.
Some time ago I wrote piece about Talent Analytics. After some discussions with them I realized that my thinking about talent or HR analytics has been too limited. Sure, I have seen how it might help with the kind of recruiting, team management and merger assessments that Talent Analytics does. But Talent Analytics made the point that one of the things the analytics can do is see what kinds of motivations people have – what makes them enjoy their job and be positive about it. And this got me thinking.
One of the challenges faced by decision management systems that must be used by front line staff, in the call center for example, is adoption – how to get the people in the call center to use the retention or cross sell offers that the engine is providing. To address this I have always advocated making sure that the metrics for the people using the system match the behavior of the system. So, for instance, don’t build a retention offer system that uses future predictions of profitability to downgrade the offers made to unprofitable customers if your call center staff are rewarded for saving anyone, as this is a mismatch between the system and their incentives.
But the kind of analysis that Talent Analytics does offers something more. The Decision Service making offers – the software component that answers the question “what offer should we make to this customer” by applying business rules and analytics – “knows” who is being shown the offers, it could be toldwhich call center representative is talking to the customer. If the rep does not make the “best” offer because it does not seem right to them or if they make it but do not sound enthusiastic then the best offer won’t get accepted. Given the point of the Decision Service is not to make the best offers but to have customers accept them, this seems ripe for improvement.
We could, in fact, take analytics based on the call center representative themselves and use them to change the offers being made. We could build a Decision Service that found not the best offer for the customer but the best offer for this conversation – one that reflects the customer and the call center representative they are talking to. Given the importance of the interaction between the customer and the call center representative it seems likely that this would improve the acceptance rate. One could imagine similar opportunities with systems in other circumstances also, though the call center is the most obvious.
As far as I know no-one has tried this. If you are using analytics to manage you call center representatives and want to see if this would improve your offer acceptance rates, let me know…