At ColoredCow, we’re fortunate and proud to say that we’re always hiring. Even in times of pandemic, we are looking for talented people to join the team. We’ve been receiving several applications every day. That’s good, right? Well… not really.
The world is facing a huge business discontinuity due to COVID. There’s no clarity when all of this will be over. Larger businesses might’ve been able to cope up with the situation, but for smaller organizations like ours, we need to hire and spend our money (very) carefully. The best possible way for us to sustain and also ensure we can scale up in the future to maintain a talent pipeline.
We figured that with certain metrics, our decision-making processes can be better and we can hire talented staff even in these uncertain times. Here’s what we think using Data Analytics, we can get the best out of data to create a talent pipeline.
- We need candidates who are a good fit for the talent pipeline. There can be evaluation metrics to figure out who all are willing to spend time on training and learning. For example, someone who lost their job and is seeking immediate employment may not be a good fit.
- There’s a market surge right now. Lots of people are losing their jobs and lots of people applying to our job profiles. And it needs huge engagement from our team to filter best-fit candidates. We can figure out patterns that can help filter applications faster.
- Once we have a decent talent pipeline, we’d also need to know how we can nurture it. The team needs to figure out what are the things needed to nurture a fresher and a lateral entry. The answers to these questions will help to structure our preparatory exercises in a better way.
- It’s also important that we keep a communication channel open with people who couldn’t join ColoredCow. Maybe it was not the right time, maybe due to financial conflicts or some other reasons. But if we are in touch with them, it’ll keep open ends for future opportunities.
- It’s important to figure out the duration of the application evaluation process and the time-frame candidates are spending in preparatory exercises. If we capture relevant metrics from this, we can figure out if a candidate is worthy or not significantly early in the stage.