This week's podcast is brought to you by Paperfly, brilliantly simple employer brand software that allows HR professionals to take control of their employer brand marketing. Paperfly delivers over 70% savings on global production spend whilst ensuring it is delivered authentically and consistently in every market and in any language. To find out why Paperfly are the trusted partner of companies such as BP, Ferrero, Rolls Royce, PNG and Unilever, please visit www.marketingmadebyu.com. Hi everyone, this is Matt Alder. Welcome to episode 33 of the Recruiting Future Podcast. I've not published an episode of the podcast for a couple of weeks. That's because I've been off busily recording interviews for a new spin-off series I'm doing, a podcast on the future of work. I'll give you more details about when and where you can find that in a couple of weeks time. Back to this week's podcast, one topic that's really interested me this year has been recruiting analytics. Lots of people talking about it, but not many case studies about how it actually works in practice. My guest on the podcast this week is Matt Bradburn, who's talent acquisition manager at Fashion Tech Company List. Matt spent the last couple of months setting up recruitment analytics at List and in the interview we talk about his experience of doing this and some of the findings they've been getting from their staff. Hi everyone and welcome to another recruiting future podcast interview. Today I am in Hoxden, Hoxden Square, in fact, at List talking to Matt Bradburn. Hi Matt, how are you doing? Very good. Thanks Matt. How about yourself? Yeah, I'm good. I'm good. It's good to be in the fashion hub, Hipster London. I feel like I fit in there. I feel like I fit in. I'm very sorry. Definitely. Excellent. So could you just sort of tell everyone who you are and what you do and give us a sort of a bit of background on how you got here? Sure. So my name is Matt. I'm the talent acquisition manager here at List. Prior to this, I was head of talent over at Cubit and I just moved here a couple of months ago now and Keats talked today a little bit about the use of data in home. Fantastic. And before we do that, I suppose we should probably just tell people if there is anyone up there who isn't aware exactly what List is and what List does. Sure. So List is essentially a discovery and purchasing site on Web and Mobile for fashion. It allows people to both discover and then run through a purchase cycle live through our own online checkout, simply held in List, to purchase their products. Fantastic. And how many, how many sort of people have you got working here? Yeah, so we just ticked over the 100 mark. I think we're about 108 people globally now with about 80 in our London office and the rest of them in New York. Cool. Now, I know that you have a massive sort of focus on data and analytics and I think that a huge amount is written about data and analytics in our industry. I'm not always convinced by some of it and I'm not convinced that, you know, people are kind of necessarily really understanding the value of what to do and all that kind of stuff. Talk us through your take on that. Definitely. I think one of the things I've discovered over the last couple of years is looking at or using data, you need to start by understanding that data can't necessarily predict the future. It's not mystic, Meg. That's not what you're looking to get out of it. What you can do, however, is utilize small sets of data to give you incremental gains by understanding how things have happened in the past. So that's pretty much my kind of key take. So we make sure that we focus on collecting as much as we can and then making sure as well that we apply a kind of human level of nuance to that. Cool. Okay. And how did your approach to data develop? Because I know it was something you were doing in your previous job. Tell us about that. Absolutely. So I was working for Cubit, which is a SaaS business focused around marketing technology. And they ingest huge amounts of data from their clients and kind of use that to make decisions. Again, combining that with a human element from their client services team. So we took a lot of the kind of key concepts behind Cubit's business and started to apply that to recruitment. And I think that's one of the key things that started the journey into that site. So the way we did this was firstly, getting an understanding of what was going to be useful for us. So one of the biggest things in recruitment is obviously these guides to metrics. Here are the top 100 metrics you should be tracking. Here are the top 10 metrics you need to be better at your job. But I don't think you can kind of generalize on that level. I think it's much more about understanding what is useful for you. So we spend a lot of time getting down to that and kind of working that through as a business. Cool. And what have, you know, since you've kind of been at here at least, what have you been measuring? What are the... Yeah. So having moved here, again, it was an opportunity to start a fresh. There was no data available. No nothing historical. No kind of prior recording of anything when it came to recruitment or tracking. So what we did was sit down and start, again, methodically working through with different hiring managers and as part of the talent team to understand what was going to be useful for us. So some of the key things we noticed were that list has been historically very good at hiring a very wide balance when it comes to gender. And we wanted to maintain and improve upon that. So we were tracking that very heavily. In terms of sources, lists kind of historically had done a lot of its hiring, particularly on his marketing at Com's team, but also in engineering from agencies. So we wanted to find ways of tracking our outreach and tracking our sourcing. And so that we could essentially show that you don't need to be using agencies to actually be getting strong candidates. And so we've been doing that. That comes to fold from both applicant tracking when it comes to, you know, did they come from stack overflow and then maybe they came from, you kind of classify that as a website, or did they come from a referral, and if so, which person would further. So again, one of the most more standard ones there, but again, very useful for us. On top of that as well, we've done a lot of work looking into the ratios. So I think one of the key things is understanding your process. And here at list, we've put a lot of effort into changing that process from day one. Prior to us coming on board as the team, so that's myself, Deanna, Triss, and Matt Buckland. They process, particularly for engineering, led to about 10 to 12 hours of face-to-face interviews, which is a lot of time. So we wanted to reduce this from day one, which we did. And now we've been tracking the data on the kind of interview side of things so that we can understand whether this has been a good thing or a bad thing. So we were starting to track whether the kind of aggravated interview scores were starting to track how interviewees are scoring. So can we understand whether that person needs maybe some more training? Are they giving the score to you high on average at this going too low? And we can start to look into all of that side of things as well. And again, it's kind of proven through the good results of getting for the candidates coming through during a short interview period and the fact that they are sticking it out through the notice period as well, sorry, the notice period, the probation period. There we go. The fact that they're making it through that as well is good proof that we were, in fact, right to reduce the time. And this has obviously improved as kind of a data point that isn't necessarily tracked, but the happiness of our engineering team and the less amount of time that they have to spend on interviews. And what was the most interesting thing that you found from putting all of this all of this data together? Because obviously, you know, it's been driving improvements, but you know, yes, was it interview time? What was the, where was the, what was the most surprising or interesting thing? In terms of, in terms of surprising, I think it was the amount of, it was the amount of direct applicants that were making it far through the process. And it showed that the level of outreach that we were doing through both blog posts and advertising was enough for us to be able to hire great people without relying on any further external help. And I think we were all surprised as a team by how much that change was, how easy it was to make that change. Cool. Okay. And how, is there a sort of specific way this data, is it, is it, is it just spreadsheets? Is there technology, you know, are you planning on making it more sophisticated in the future? What, where's the colour system? So we looked into a range of things. So firstly, we're using workable as an optically-system at the moment and workable is great for many things. Reporting on data is probably the one area where it's not so strong. So we have been looking into applicant tracking systems like Greenhouse as well, which are a lot better for that. However, unfortunately, Greenhouse's price is fairly prohibitive at the moment. So we've gone for good old Google documents. We've set up everything in a sheet there. What we do is track any applicants, anyone that we've sourced, anyone coming through advertisements, any referrals, all on one single sheet. We thought of you down by the recruiter to make it easier for ourselves. And then we have a gigantic dashboard coming off the back of this. I think some of the key things that we track in the dashboard, how many interviews are we getting per month? For example, how many people have been messaged, how many at phone screen, how many at final round, et cetera. We track the dropout rates at each stage, again by month and by recruiter. We track the number of people that are engaged at anyone's time by recruiter. It's all very well and good for one of us to go out and message 200 people. But if only two of them make it in the process versus messaging 50 people and having 20 make them make it into the process, it's kind of points. So do you use that kind of metric to improve the quality of the engagement and the communication that you do? Absolutely. I think that's one of the bigger things that we prove straight off the bat as well, is the fact that bulk messaging 200 people with a very similar message doesn't get you very far. Directly messaging people with a very personalized, very tight message will get you a lot of people engaging. Okay, that's interesting. Just kind of as an assignment map because there's lots of stuff gets written about technical recruitment and particularly recruiting engineers. Engineers don't answer messages. They never use their phone. These are some of the, if you like, some of the favorite stories I kind of hear around this space. Is that true? I think that is entirely unwell. I'm sure it is true for plenty of people who aren't necessarily sending the right type of message and we are not 100% correct all the time, but we're getting on average about a 60% response rate for messages to engineers. So when you consider that, it puts us in a nice position. I think it proves again that it is the quality of the content that you're writing, not the volume of the content you're putting out there. And the recruitment metrics are interesting. You're obviously tracking a lot of stuff. You're looking at different sources and all that sort of stuff. It's still a reasonably unsophisticated world compared to the world of Marks and metrics. Absolutely. Your dig is unsophisticated in general. What do you think would be a really killer thing to track in recruitment? If you could, if you could have a way to say, I wish there was an easy way to track this particular thing, what would it be? For me, I'd be very interested to see, and this is more of our long-term aim here at NIST. It's very interesting to see how the candidates then form over time when they've been hired at NIST. So it's applying and bringing everything together from what source that candidate came from, how they did in the interview process, how they did in their sixth month review, one year review, two year review. Can we then use that information to inform our decisions when we're reaching out to people? For example, you know, are the people from one particular team at Google? Are we hiring several people from there? If so, does that tell us anything about that team? Is that a piece of useful information? I think it's just being able to track over the longer term and being able to tie in with HR and performance reviews. It's an interesting change in the world of how talent is viewed and people operations, which we have the joy of being able to actually drive here at NIST because we have a very small HR function, but a much larger talent function. So I think a lot of the responsibility is kind of falling in our direction. That's interesting, I think, especially with this sort of massive focus on HR analytics in the marketplace at the moment. It would be, I suppose, it would be great if there was sort of a war discussion on how they enjoyed that on recruiting analytics. But I think, you know, in our experience, recruitment analytics is still evolving and evolving entity. Final question, what do you think's next for recruitment? I mean, obviously you guys are, you know, at the kind of cutting edge in terms of recruiting, you know, technologists and engineers and, you know, where are things going in the future? How is recruitment? You know, going to change what sort of technology might be useful? I think that's a very interesting question. I think one of the things when it comes to recruitment is we need to not just focus on technological solutions to things, but understand how to train people to be better at their jobs as well. There are a lot of people who, in the industry, who aren't necessarily very good at recruiting, who almost would look to add technology to then use as a blaming device over there, you know, because of their own vulnerabilities. I think, as an industry, particularly in-house, and I can only speak for the kind of in-house side, it will be, there will be more companies leaning on their recruitment teams. I don't believe there is any kind of talent war going on, but I believe that people understand the value of having exceptional staff working for them, so they will place more emphasis on hiring strong recruiters to do that, so we need to make sure we keep training people. I don't think technology is going to be the answer to that. There are some very good technological solutions, but I think if I were to pick out one area, it would be, again, going back to that time with HR analytics. There are too many data silos in businesses at the moment, and it's being able to tie all of this information together. Can you make informed decisions as this is something we're setting up at the moment, and based around seating plans, based around budgets that we have, can you run hiring as a bit of a P&L when it comes to what salaries of people who are leaving the business are available, how does that tie in with finance, and then actually be able to present all of this information in a clear and concise manner to the CEO, the CFO, and the board, which they don't tend to have at the moment. They, you know, they hand separate individual silos, but we need to be able to track everything from a the day a candidate is first spoken to to the time that they leave the business or ideally stay with the business, but, you know, constantly looking to want to have one single space for all of this. Matt, thank you very much for talking to me. Thank you very much as well. My thanks to Matt Bradburn. You can find past episodes of the podcast and find out more about me at www.rfpodcast.com. You can also subscribe to the podcast in iTunes and on Stitcher. I've got some great interviews coming up in the next two or three weeks. Next week, I'm talking to William Oranger at Go Daddy. Very much looking forward to that. So I'll be back next week with that interview, and I hope you enjoyed it. [Music] [BLANK_AUDIO]
In this episode Matt Alder talks to Matt Bradburn Talent Acquisition Manager at Lyst
Talk of analytics and data has been everywhere this year with their importance empathised by numerous, blogs, whitepapers and conference presentations. Very often though getting started with analytics can be difficult and with a seemingly endless array of technologies on the market many Talent Acquisition professionals are confused about how to get started.My guest this week is Matt Bradburn, Talent Acquisition Manager at Lyst where he has spent the last few months implementing recruiting analytics.In the interview we discuss: • How data can’t predict the future but can help you to make incremental gains by understanding what has happened in the past • Why you should ignore some of generic advice on analytics and focus on what is useful for you • How Lyst have used metrics to reduce interviewing time while simultaneously raising quality • How his team measured the engagement of their sourcing outreach and now achieve a 60% response rate from the messages they send • Why you don’t necessarily need complex technology to be successful with analyticsMatt also gives us his views on the future and talks about the one metric which he would love to able to measure.You can subscribe to this podcast in iTunes
