A former sales operator describes how machine learning uncovered a 30% improvement in hiring attribution for a casual labor force worth over $10 million. Edge cases and unconventional approaches, paired with data-driven tools, reveal breakthroughs that gut-feel methods miss.
Chapters
I mean, 30% of a more than $10 million labor force around them. And that's just through taking these different views. I mean, it sounds a bit twee, but I guess it's always about focusing on what our customers' problems are and what tools do we have available to help that, to fix that. And approaching that with that open mind to say, well, you know, how do we go about this? As an individual contributor years ago, I was always always that guy that the sales ops people hated because I was always trying to craft some unusual deal, you know, because I'm looking for that win-win kind of an outcome or to, you know, find an edge case. And I think that's often where the interesting things are to be found, are in those edge cases. You know, how do we solve some problems that, you know, are not deemed unsolvable, but perhaps aren't being solved in the optimal way? I've got a great example. We've got a client right at the moment that's trialing some machine learning algorithms that we have as part of our platform. And, you know, very quickly we've been able to show that there's a 30%, potential 30% changing in how they can attribute and how they hire their casual labor force. I mean, 30% of a more than $10 million labor force. Around them. And that's just through taking this different view to say, well, actually, how have you been doing it till now? Gut feeling. So we just take a slightly different view and suddenly we find this new solution for the client. And so, you know, and obviously it's trying to keep up to date with the technology.
