[MUSIC] 0:00 >> Thank you all very much for coming along to this rising star track. 0:09 My definition of rising star I think is scared person 0:15 who once upon a time somebody heard say something interesting. 0:18 So stick them on a stage. 0:20 So bear with me, this is my my first talk at a conference. 0:22 I hope you'll enjoy it. 0:25 Incredible data. 0:28 [COUGH] as thanks for the wonderful introduction. 0:29 I,I have been lucky enough to be in a position in my life 0:33 where I started a web dev firm when I came out of university. 0:37 And through the opportunities and the people I've met from, from that 0:40 web dev firm I have, had the opportunity to buy other businesses, basically. 0:45 So clients of ours who have then gone and 0:50 taken shareholdings, and some of them, up to 100%. 0:53 In industries that I know absolutely nothing about. 0:57 But all of them have involved data. 1:00 Either the selling of data or the fact that we've been using data to 1:02 go and change the industries So, today, I'm gonna talk to you about three things. 1:05 First a little bit about incredible data and what on earth I'm 1:11 talking about, the second is how we use data, two methods to use 1:14 data to become more profitable in your business and the third and probably 1:18 the most interesting is that we decided to do this internally at storm. 1:22 We spent an entire month a couple of months back where we 1:27 literally tracked everything from the people 1:30 were wearing pedometers, the number of 1:33 stairs they climbed, the number of coffees they drank, the number of 1:34 times they went to the toilet, 1:37 the temperature, the light, the heating everything. 1:37 Everything, everything. 1:40 Their perceived mood, their perceived productivity. 1:41 And I'm gonna tell you about what we did with that data. 1:43 And to be honest with you, the main reason I wanted to give 1:46 this talk is cuz I found quite a lot of it quite funny. 1:49 And there are a few people who are actually in 1:53 the audience that, who, whose names would have been obscured 1:55 for obvious reasons, but I'll point to them when the 1:58 facts about them, so you know who I'm talking about. 1:59 Some funny stuff up there. 2:02 So, I'm, I'm gonna crack on and talk about incredible data. 2:04 A few little stats to start with, which you've got nothing to do 2:07 with very much but 4.1 million hectares of forest have been lost this year. 2:12 Which I'm sure as you all really wanted to know is 2:18 an about an area the size, twice the size of Wales. 2:20 But at least we're doing our bit to be green. 2:23 We've produced nearly 112 million bicycles this year. 2:28 And I know what you're all thinking, how big is that 2:32 if you lay them all down flat in terms of Wales. 2:34 Well, it's 1 86th the size of Wales. 2:36 This is actually quite a nasty statistic when you think 2:39 about it. 2:44 Nearly 900 million undernourished people in the world. 2:45 Which when you put it in the context of the 2:48 fact that there are 1.5 billion overweight people in the world. 2:49 Which is when laid flat about 112 the size of Wales. 2:54 >> [LAUGH]. 2:58 >> But that's not very nice to think about. 2:59 By the way, if you took all of those people, 2:59 you could actually fill the Seven Estuary, rejoin them in 3:01 negating the six pound 20 toll charge, which is a 3:03 net saving of 84 and a half million pounds a year. 3:05 But, that's the world spending on illegal drugs this year so far. 3:08 Gonna go down now Breaking Bad's finished. 3:13 But still, ten people a year. 3:15 Are killed by vending machines. 3:17 I found the bugger. 3:19 Its him. 3:20 I would be watching out if that was the vending machine I was using. 3:20 Now I'm, I say data. 3:23 You're probably looking at this saying that's that's not actually data. 3:25 That's that there's a fact. 3:28 They're stats. 3:30 They're jokes. 3:30 And you'd be right. 3:31 And here comes the first management program of the day. 3:32 [LAUGH] Don't run. 3:34 data. 3:37 Is, is nothing at all on its own, it means bugger all to anybody. 3:38 When you contextualize it in the form of those, kind of, stats, jokes, etcetera. 3:43 It starts to gain meaning. 3:48 But in terms of business it can't help you until you feed into some sort of process. 3:50 That might be a learned behavior. 3:55 That might be a model. 3:56 That might be some business theory. 3:57 And that is really what I'm gonna be telling you about today. 4:00 Two pound fifty is some data. 4:03 It means nothing. 4:04 When we can textualize it, a pint costs two pound fifty. 4:06 We're thinking learned behavior that might be a 4:08 really budgie pint especially if it's in London. 4:10 But maybe I'll get my wallet out and maybe that's actually a really good thing. 4:13 And the end result might be 11 pints, great! 4:17 But it is useless and so the context and the application is where. 4:20 The becoming more profitable comes in. 4:25 And what I'm gonna show you is two different methods, the 4:28 first of which is going to tell you about how you put 4:30 data in context of your business, why that data is important, actually 4:34 add some logic to the fact that they, it should be important. 4:37 And the second example to making more 4:40 profitable is gonna focus around the application. 4:41 I'm gonna give you a real life example of how 4:44 you can find uh,one number that probably runs your business. 4:45 Whether you're freelancer or a multi-million 4:49 pound business, the chances are, there's going 4:51 to be one number in there somewhere which is the application of data. 4:52 And then at the end, I'm gonna show you how Storm actually. 4:56 Took this found loads of data and found some funny things. 5:00 So let's talk about becoming more profitable, and again a little caveat. 5:03 I say more profitable I don't mean more profitable. 5:09 I mean more successful because for some 5:13 people success is this rolling around in money. 5:15 But it is perfectly reasonable if for you success is having the time to do a hobby. 5:19 Just enjoying yourself at work, you know, having 5:24 time to chill out with your, your colleagues. 5:27 For some people they'll accept nothing less than world domination. 5:29 It doesn't really matter. 5:31 The point is that, just because I'm talking about money in terms of success, 5:32 that doesn't necessarily mean it's the thing, that you have to be focused on. 5:37 So on to the first of these methods. 5:41 I call it, using data to create your own luck. 5:44 and, those of you who were paying attention of 5:48 course and not asleep would have heard me at 5:52 the very beginning of the talk say that I feel I've been lucky in my life so far. 5:54 That I've been in the right place at the right time. 5:59 There was Here we're gonna go back in time a bit. 6:03 A Roman philosopherum, who [LAUGH] yeah, I know, 6:06 tech talks and all um,let's get the Romans involved. 6:08 Luck is what happens when preparation meets opportunity. 6:12 And I really believe this. 6:15 The, the, luck isn't something that kind of 6:17 just, stumbles on you, it's, if you're prepared by 6:19 whatever means, and an opportunity presents, and you grab 6:23 that opportunity, other people will see you as lucky. 6:26 How come he got to do that? 6:28 How come he won that? 6:29 Well probably. 6:30 Lots of preparation and in the right place. 6:31 And bringing that up to, slightly up to date. 6:32 This chap here who actually if you Google 6:37 for the Roman guy it looks remarkably similar. 6:39 This guy a chap called John Boyd, was a military strategist 6:43 who came up with a stratagem for basically beating his opponents. 6:46 It's called the Boyd Loop. 6:52 It's actually a very well known piece of 6:54 management theory, but very rarely gets talked about. 6:55 Quite simply, observe orientate, decide, and act. 6:59 And putting that in the luck kind of bid world. 7:03 That's the opportunity bit, the observation. 7:06 This is the preparation bit. 7:09 And I'm gonna give you an example of how that works using two two corporations. 7:12 So have a massive corporation which we'll call Massive Inc. 7:16 And we're gonna have a, a small company which we're gonna call Brian's Cheese. 7:19 And the idea is if Massive Inc. 7:24 See, for example, that someone brilliant is about to come onto the market 7:26 as, as a really good hire, they're just leaving their, their current job. 7:31 Massive Inc might be looking and thinking that guy, that's the one we want. 7:35 So, they'll have a meeting. 7:39 They have to decide whose cost base it's gonna come out of. 7:40 They'll have a chat about who he's gonna 7:43 be reporting to, and what his objectives will be. 7:44 At some point a decision will be made. 7:47 We should offer that guy a job, really we should and then there'll be an action. 7:48 Shall we send him a letter and see if we can get him in. 7:51 Unfortunately, Brian's Cheese in in the middle dancing 7:53 around in circles going, I wanna hire that guy. 7:55 Do you wanna job? 7:57 Very simplistic example, but it makes the point that 7:59 small teams can almost always out maneuver, large opponents. 8:02 Simply because they can get decisions made more quickly, they can be more agile. 8:06 There are some brilliant examples of this. 8:10 Fitbit and Nike with the fuel band. 8:14 Both of these companies started talking about this tech in 2007. 8:17 Which one of the two had a product in 2008? 8:23 Which one didn't have a product till 2011? 8:26 By 2011, there were two more of these out, and by 2012 another one. 8:29 The fact is that [UNKNOWN] may well have had a little team, 8:33 but those decisions about this had to go right to the top. 8:37 There were strategists involved. 8:40 There were all sorts of people involved. 8:42 They didn't take an opportunity and go. 8:43 Can we get something out there really nice and quickly 8:46 and I mean there are a, a, another example which I 8:49 really like is actually the fact that everyone forgets that once 8:51 upon a time Apple was being run out of a garage. 8:54 And there were people like IBM and 8:56 Microsoft who are these massive, massive companies and. 8:58 The, the, the biggest for me is when, Apple had a,a period just 9:03 before the, the Northeast where they, where 9:07 they were actually in decline, where they 9:09 weren't doing so well and it was only when they released the new unified 9:10 one body Mac in the early 2000s they started to pick back up, but. 9:14 This is, I think, now keep in mind at that time they were way 9:19 smaller than Microsoft cuz this is kind 9:22 of, slightly, just before pre, pre-iPods and pre-iTunes. 9:23 And there's a couple of quotes from Steve Jobs, Steve Jobs, I'm 9:26 sorry, about Steve Jobs from Bill Gates specifically about iTunes, which I think. 9:30 Demonstrate that this can happen even in the biggest business. 9:35 And that is that Bill Gates said, Steve Jobs has 9:38 an ability to focus in on a few things that can't. 9:41 And this time somehow he's applied this talent to 9:44 getting a better license deal than anyone else's got. 9:47 I think we need some plan to prove that even 9:52 though Jobs has left us a little bit flat footed. 9:55 Again, we move quickly and both match and do stuff better. 9:57 Basically, this was Bill Gates going,how did they manage to do that so quickly? 10:03 How did they manage to get that problem solved and out the door? 10:08 What the hell has happened here? 10:11 We've missed something. 10:12 And you can see that literally across the board in business. 10:14 So what on earth has that got to do with data? 10:18 Well, it's this bit. 10:20 Opportunity, as we called it earlier on, kind of 10:22 presents an idea of something coming on to find us. 10:25 I'm sitting down in my deck chair and an opportunity comes along. 10:28 The way Boyd puts it, is that it's observation. 10:31 You're there looking for the opportunity. 10:33 You don't sit back and wait for it to come to you. 10:35 You're actually actively looking, and that is data. 10:37 In your business, there is so much data 10:40 flowing around your business in terms of the market, 10:42 in terms of your competitors, in terms of your 10:44 friends, in terms of legislation, the list goes on. 10:46 And you're probably not looking for it. 10:49 You're probably not looking at it. 10:54 You probably find out about it when 10:55 your competitors do something really amazing with it. 10:57 And, usually the reasons small more businesses aren't looking is quite simple. 11:00 Haven't got any time to look. 11:05 I'm too busy. 11:06 I'm too busy to do that. 11:07 You're too busy to succeed? 11:09 If you don't leave some time in your business to actually go and look 11:10 at the things that could make you 11:13 more profitable, that could give you opportunities. 11:14 How are you ever going to? 11:17 Now it doesn't matter if you're a freelancer, who 11:18 should probably be keeping an hour a, an hour 11:20 a week to just generally keep an eye on 11:22 what's going on, or whether you're a multi-million pound company. 11:24 Who probably has got the resource to do the looking, but when 11:26 they see something it takes them bloody ages to do anything with it. 11:30 [COUGH] So to put that idea of, if you're not 11:34 looking you are never going to find it, into context. 11:36 Let me show you how you can find one, one number in your business. 11:39 That will help you run your business, and it 11:42 should put that into context a little bit for you. 11:45 So. 11:47 This is something that we run with a lot of the 11:49 companies who, who we work for as a, as a software [UNKNOWN]. 11:50 If I didn't say, make it clear in the beginning, my role within 11:53 the store is, is mainly sitting with new business in a strategy sense. 11:56 So I basically sit with them and I will 12:00 work out what their idea is cuz it's probably in 12:02 the industry I'd understand how you can apply tech 12:04 to it and actually make some money out of it. 12:06 So we, we run through this with a lot of our clients. 12:08 And the answer to the question either. 12:11 Why am I making money, or why should I be about to 12:13 be making money, or quite often, why am I not making any money? 12:15 Which is not such a nice conversation. 12:19 And it revolves around this very scary thing of a critical resource limitation. 12:22 Quite simply it is the thing that when you run out of it. 12:29 You can't make any more money. 12:33 Now bear with me on this it's not necessarily the thing you sell. 12:34 It might be the thing you sell but 12:40 it might be something that comes slightly earlier on. 12:42 So, very obviously if you've got a certain number of bedrooms in 12:43 your hotel or seats in your restaurant or whatever that might be. 12:46 And you've run out of that thing. 12:50 You can no longer sell it. 12:51 But the example I'm gonna give you is actually sales hours. 12:52 So if you imagine that you've got ten devs who've sat around doing sod all and 12:55 the thing that's actually limiting you is, you 13:00 don't have enough sales time to feed them. 13:02 So you could be doing the work but you're not. 13:05 The thing that is your resource that is limiting you is your salespeople. 13:06 You haven't got enough of them making enough phone calls. 13:10 So it's not the thing we're selling. 13:12 So we're not selling sales time. 13:13 And you've got to be really honest with yourself and 13:15 ask how much of that thing have you actually got? 13:17 Cuz, I mean, no offense to some of the guys who 13:19 work for me, but they're supposed to be on 36-hour weeks. 13:21 But they don't do 36 hours a week of work. 13:24 You know, productive week might be somewhere close to, say, 20 hours. 13:27 So let's say we've got two sales people in the business. 13:30 They're producing 20 productive hours a week, so we've got 40 sales hours. 13:34 And as I said. 13:37 We're assuming, for the sake of this, that there is unlimited 13:38 resource down here, we're feeding, we're feeding them, it's not a problem. 13:41 The second thing we need to do to find this 13:44 one number is a couple of fairly simple bits of, data, 13:46 this can be projections if you aren't yet a business, 13:50 but your projected income, your projected expenditure, or your income expenditure. 13:53 Looking at this, we can tell one thing hm, profits. 13:57 There is 250 thousand pounds in there somewhere because of 13:59 the fact that we are spending less than we are making. 14:02 Great. 14:04 And what we want to know, is, what's the value add for our critical resource limit. 14:04 So for every hour we're spending on the phone, trying to make 14:09 the sales calls, how much value is that adding to the business. 14:11 And the way you work it out is, quite simply, you've got 52 weeks in the year. 14:15 We divided that by the number of productive sales hours in 14:19 the week, and that gives us 120 pounds per sales hour. 14:21 Bare in mind here, that that assumes no one has any holidays, [LAUGH] 14:24 and you work 52 weeks a year, so I'm just keeping it simple. 14:29 I'm just going to recap on that, because it does get slightly complex. 14:30 The only deal here is, that in our business, 14:34 we are not just making money from selling dev time, 14:37 cuz we've got as much of it, if we could sell a million hours of dev time we could. 14:40 The thing that's stopping us making more money. 14:43 As we cannot get enough sales calls through to feed that dev time. 14:45 So, we've divided our business profits by that 14:49 limitation which has given us 120 pounds an hour. 14:52 Okay. 14:54 Okay, now let's bring it all back to some sanity here. 14:55 How does that fit with what I've been talking about so far? 14:58 Well, quite simply. 15:03 [COUGH] What we can observe are three opportunities. 15:04 If we want to increase that 120 pounds we can do one of 15:09 three things, we can increase the number of sales hours available, lets hire 15:12 someone, more sales people, four more sales people go on lets go get 15:15 them, but obviously we don't know is that a good thing for the business. 15:19 How much is that gonna cost? 15:22 Can we get them? 15:23 Will they be here in a years time? 15:23 Not really sure. 15:24 Increase the costs to the client of thing ultimately being sold, 15:27 the app, the room, the hotel room, the cost of the dinner. 15:30 Whatever it is. 15:33 We can stick that up, and that will mean the effort the sales people are 15:34 putting in are ultimately resulting in more 15:37 money, but is the client gonna like that? 15:38 Are we still gonna be positioned the same in 15:41 the marketplace if we go and stick our costs up? 15:43 Number three is the opportunity that most people like, 15:46 because it essentially preserves the status quo of your business. 15:49 You're not putting your price up, you're 15:53 not introducing any more people into the mix. 15:54 You're just going, you lot, work harder. 15:55 Please, cuz I, I don't want you to be doing 40 productive hours if 15:58 you can do 50 productive hours, I can make some more money in my business. 16:01 I can become more profitable. 16:04 But even when you've observed in, in your business, 16:07 that you've, you've, you've identified a critical point in your 16:11 business, where if you can shift this number, if you 16:13 can make that more efficient, you can make more money. 16:15 You know what to do. 16:17 The chances are, you're actually gonna need more data. 16:18 To inform, which of these is correct. 16:21 You're gonna need data to tell you whether you can afford more sales people. 16:23 You're gonna need data to tell you whether or 16:26 not the client's gonna accept an increase in cost. 16:28 You're gonna need data to tell you whether or not 16:30 the people who are [UNKNOWN] there are already doing it. 16:31 Even remotely have the capacity, the mental capacity, 16:34 the physical capacity to go and do that. 16:37 Essentially, to make themselves more productive. 16:39 And so, if you haven't got that data, you're stuck. 16:44 You've found an amazing business problem. 16:49 You know that it fits from a business point of view, 16:52 and that you should be, as a small business, acting upon it. 16:54 You've observed it and you think my advantage here is that 16:57 I can do the Boyd loop quickly, I found this thing. 17:00 I need to do something about it. 17:02 And you haven't any data to base your decision. 17:04 So, that's kind of where the experiment at storm came in. 17:06 But we realized that, thi-, this was us. 17:09 We, we could do the, the maths and work out what our one number was. 17:11 For us it happens to be the value out per developer hour sold. 17:15 So we are actually adding value onto the actual thing we're selling. 17:18 And what we realized is that if we 17:22 could make our productive developer week slightly more productive 17:24 we don't have to change anything else but 17:28 we make more money which makes me happy obviously. 17:29 I am one of the people with the rolling 17:32 in the money I'm not the gardener, so just, we,we 17:35 embarked upon this experiment with some of the lovely 17:38 people at Storm and basically as I have already identified. 17:41 We wanted to increase our productivity. 17:46 And the way we did that was to, [LAUGH] this 17:48 is very scattergun, and I don't suggest that every business 17:52 does this, but this is the kind of having fun 17:54 while you do it, like, I thought this might be interesting. 17:56 We actually tracked everything. 17:59 Everything. 18:03 So. 18:03 People's steps, they were wearing pedometers, their actions and 18:05 activities around the office, the number of times they went 18:08 to the loo, the number of cups of cups of 18:10 coffee they drank, the number of soft drinks they drank. 18:12 How high were the noise levels at any particular of time of 18:14 day, the light levels, the humidity, we looked at people's perceived mood. 18:16 We looked at their perceived. 18:21 Productivity so we're actually asking how productive they felt. 18:23 We were looking at the loads of our 18:25 other KPIs that the businesswas already looking at. 18:27 Weather conditions, one that got added right at the end by 18:30 I think it was Scott who sat right at the back. 18:34 We are rather unfortunately we, we work in 18:36 the beautiful city of Bath and it is amazing. 18:39 The one thing that's not nice about where we are is the fact that. 18:42 About six feet from our office is one of the most popular busking spots in Bath. 18:45 Now some of the buskers are very talented. 18:51 They can play some great stuff. 18:53 Unfortunately they've only got three songs, and when 18:54 they busk over the course of a year. 18:58 That's a lot of repetition for the people who are working six feet away. 19:02 So, we even actually monitored what people thought of the buskers outside. 19:05 Crazy, I know. 19:10 And [LAUGH] here's what we found out. 19:11 And I'm hoping that by the end of this 19:13 you'll be able to see, I'm gonna, once I've 19:15 shown you what we found out, there's some graphs 19:17 and stuff in here which are a bit amusing. 19:19 I will tie it back into what we actually did in our 19:21 business, to solve the problem of, can we actually make people more productive. 19:24 So, 19:30 the lower the morning light, the more quickly productivity picked up. 19:32 I don't know why that is. 19:37 But when it was kept quite dim in the 19:38 mornings people got in to their groove more quickly. 19:40 And they were telling us they perceived they 19:43 were more productive and indeed we were, we timed 19:44 tracked which I'll show you a bit later on 19:47 and people's productivity did indeed seem to go up. 19:48 When it got darker earlier in the evenings people went to the bugger off earlier. 19:52 Hm, colder days, in general, were accompanied 19:58 by extended periods of above average productivity. 20:00 Whereas very hot days actually had much higher levels of 20:05 productivity, productivity but in a much more condensed period of time. 20:09 And obviously the kind of average temperature days 20:12 seemed to average productivity, which kind of makes sense. 20:14 But again, interesting. 20:18 This was a little bit more scary. 20:20 When you are interrupted by a colleague who 20:21 did need you you felt you were giving value 20:23 back to them, you were 24% more likely to have a cup of tea in the following hour. 20:25 [LAUGH] Even more worrying, if you, if they didn't need 20:29 you, you were 40% more likely to have a pee. 20:33 [BLANK_AUDIO] 20:37 Which obviously is kind of annoying for, for a majority of people. 20:41 This is one that I've taken to heart. 20:46 When it was raining, people were buying more stuff. 20:48 So this is a side note and there are any devs in here who want to make me, help 20:52 me make this which is a weather powered safety deposit 20:55 box I'm very up for talking to you later on. 20:57 Cuz I'm going to be locking that credit card away on days that it is raining. 21:00 We found the people's tolerance was about one 21:04 phone call before they felt they are being interrupted. 21:07 One phone call in the space of a 21:10 couple hours they didn't mind answering that was okay. 21:11 But as soon as it was over that people started registering. 21:14 They felt disrupted and their productivity did indeed drop. 21:17 And we found out we were losing, on average, around 21:19 an hour of productivity for the second and ongoing phone calls. 21:21 On to the interesting stuff. 21:26 This here is a graph of a a particular day 21:27 where [COUGH] I'm going to tell you about productivity and coffee. 21:29 You might be thinking that kind of makes sense. 21:34 We've got a. 21:35 People pick up their productivity and kind of peak in the morning 21:36 then there is lunch so clearly not so productive, then in the afternoon 21:38 you kind of ramp up a bit again, not quite as high as 21:41 you were before, before tailing off because hell it is nearly home time. 21:44 I'm, I'm gonna kind of throw this open now but, we split, we split the class into the 21:47 greens and the yellows and both of these drank 21:53 different their drinking habits were different in terms of coffee. 21:56 So the the yellows drank more than two cups of 22:01 coffee two or more, sorry, cups of coffee before 9 a.m. 22:06 On average. 22:09 And the greens were one or none. 22:10 Can anybody, would anybody like to speculate 22:14 why those who drank more coffee earlier on. 22:16 Ended up losing productivity by kind of mid-morning. 22:20 They were peeing, you're right [LAUGH]. 22:24 This is the toilet break, the first recorded toilet break 22:26 on average for the yellows, whereas the greens were up here. 22:29 Now you'll notice that, that the climb in 22:31 productivity for both is actually very similar, but. 22:32 The, the because of the fact the yellow started 22:36 early essentially, gave themselves, they were getting out of sync. 22:38 Here we go, Buskers versus Mood. 22:43 Now, the first half of this graph so, 22:44 we've, we've got good buskers and good mood. 22:45 So, that's kind of, it ki-, th-, this has by the way 22:48 been smoothed slightly and it is over the course of a month. 22:50 But, this is in general, quite nice. 22:53 You know, the better the busker, the better the 22:55 mood, the worse the busker the worse the mood. 22:56 Only once, we get down to the pretty awful buskers. 22:58 All of a sudden, people are happy again. 23:01 Again, I'm, I'm very happy to throw open to any suggestions. 23:05 Dan. 23:08 >> Headphones. 23:08 >> Headphones. 23:09 You are correct. 23:10 That is exactly what it was. 23:10 This is the point in which people go, sort of that, 23:12 and stick their headphones on, and so they are happy again. 23:14 I like this one. 23:19 This is actually one person on one day 23:19 but again it just illustrates the amusement I had. 23:22 This is their contentment, how contented they felt over 23:25 the period of the day massive dip around, around 12,12:30. 23:28 This is a, a graph [LAUGH] of another member of staff and its 23:34 actually the frequency of time they got up and went away from their desk. 23:38 And there were a couple of events that happened very close to each other. 23:43 Event one was by the person who got up, saying, I had to break to chat to 23:46 John, feeling content, calm, funny, and jokey, whereas 23:50 John won't stop interrupting me, feeling annoyed, frustrated, interrupted. 23:52 I like this one. 23:59 This one is two weeks worth of productivity across the 24:01 company, and you'll notice there are some spikes in there. 24:03 And there's actually something, there's actually I have a theory 24:05 about this which I'm going to tell you in a moment. 24:07 This was the day that was Scott's birthday so cake was brought in. 24:10 Hm. 24:14 On this day, Andrew brought his dog into work and everyone was very amused. 24:14 Liam won some. 24:18 Quality streaks on this day. 24:20 I will genuinely give a prize to anyone who knows 24:22 or can think of what happened on this particular day. 24:25 Borrowing people who may well have been in the office. 24:28 That might of caused some mirth on that day. 24:31 It was the day that a Bath bus came around the corner and 24:34 took out nine bollards in a row because it completely missed the corner. 24:38 So why are we more productive on these days? 24:42 And you know, you kind of have to hypothesize. 24:44 My, my personal opinion is that that's to do with productivity sync. 24:46 If you have events that happen at a particular time, they kind 24:50 of chop everybody's day but then get them back into sync thereafter. 24:53 Because our guys don't actually start the day at the same time. 24:57 We, we, we kind of do the tech company 24:59 flexible thing of yeah, you know, as long as 25:00 you're in by ten and you work the right 25:02 number of hours, and we're measuring output, not input. 25:04 That's all lovely, but genuinely, getting people's attention synced up seems 25:06 to be a repeating factor in the data we were looking at. 25:12 So I'm gonna actually tell you a few of the results now, because 25:15 I could go on with funny data for,for for a very long time. 25:17 But here are some things we've actually genuinely done. 25:21 We've started stand-up meetings at a very set time 25:23 in order to bring people's productivity back into sync. 25:26 So every, we've got one at 11 o'clock, we're 25:30 gonna start doing one in the afternoons as well. 25:32 The idea being that it kind of syncs people up 25:34 into relatively nice chunks, so that you kind of just. 25:36 Stop. 25:39 Hello, everybody good. 25:39 Back to work. 25:41 But it gives you a short mental break. 25:41 And we are going to encourage teams to start working at the same time. 25:44 That's not everybody in the company has to be in there in a particular time. 25:46 But, if you work in a particular group. 25:49 As we've got our designers that often work closely 25:51 with the front end devs and the back end devs. 25:53 We're trying to get people to kind of look at 25:56 their colleagues and go, I shouldn't come in at seven 25:58 if you're going to come in at ten because our 26:00 working day is going to be completely off the clock. 26:02 We've changed the way our phone systems ring in hunt groups. 26:05 So we're actually making sure that people who are, who, who 26:08 are more susceptible to being interrupted already, get interrupted again, basically. 26:10 And also, we're gonna try and do something very clever where if 26:14 somebody's phone's been rung once It won't ring again quite so soon afterwards. 26:16 We're turning off the lights in the morning. 26:22 We're only turning on a couple. 26:23 Where as it used to be, first one in, all 26:24 the lights on, you know, literally fire the place up. 26:26 We're, we're not doing that anymore. 26:29 We're leaving it dim. 26:30 Just because of the fact that it appears that people get more into their 26:31 productive zone when the lights can get turned on as and when people want them. 26:34 We're even we were, by the way, just a tiny thing. 26:38 We moved office a couple of months ago, which means we've been 26:40 very lucky that we actually have been able to do something about this. 26:43 So when we have our new heating system put 26:46 into the, the new office, we had some wires and 26:48 stuff so that they all hook up and we 26:51 can set them and start to do some clever things. 26:52 Which wasn't in the old office. 26:54 So But we are, we're trying a 26:56 bunch of different heating settings, including having. 26:58 Different settings at the office which are warmer and cooler. 27:00 Depending on whether you think alright, I need to get a bunch of work done. 27:02 I'm gonna go to a quiet space that's nice and warm. 27:04 Or we're giving it a go. 27:07 We don't know. 27:08 It might turn out that it's complete and utter rubbish 27:09 and it means nothing, but we're gonna give a go. 27:11 We're gonna collect the data. 27:13 Oh, that's a good idea. 27:15 We've created no interruption zones around the office so 27:16 you basically,again the new office has got enough rooms that 27:18 we were able to kind of go, we'll have 27:21 those two or three rooms where we'll just stick her. 27:23 On the door, and hopefully won't get up and get too distracted. 27:25 We've put, I've called them, busker-neutralizing stereos, 27:29 this isn't a thing you can buy. 27:31 You can't kind of go into Curry's and go, One busker-neutralizing stereo please. 27:33 This is a, this is a standard stereo, you know, a normal one which happens 27:36 to be turned up to the volume at which you can no longer hear the buskers. 27:40 So they're now in the offices say that when we get down to the really annoying. 27:43 You can kind of turn them up a bit. 27:47 We've started recording emotions, cuz this is not something we'd ever done before. 27:49 We, we'd never recorded what people were feeling in the company before. 27:53 And you know, I, I didn't even think people 27:56 were gonna be very forthcoming with how they're feeling. 27:58 But oh no, people love to tell you how they're feeling. 28:00 I'm gonna show you some of the funny things. 28:04 This is gonna be, this is the bit where there might 28:05 be a small live demo, and our first potential gawk up. 28:07 Bear with me. 28:10 We use a system called Minim, which is something we wrote. 28:11 Contrast is awful here, but basically it is a standard time tracker, so 28:16 I'm doing a forward talk, but you know what I'm finding it annoying. 28:20 So, I am, I'm just, I am not tagging it, if there was a hash tag there. 28:22 Okay. 28:29 Have I got annoying in there? 28:30 I think 28:32 I 28:34 should have got annoying. 28:38 [SOUND] On any particular project, so we can just call this FOWA. 28:39 And how long have I been doing it for? 28:45 Thirteen minutes, its not billable, I'm gonna log it. 28:47 The point being that we're encouraging people to put meta data into their 28:50 time tracking so annoyed interrupted phone call, 28:53 toilet break, whatever the hell they want. 28:58 with, with the results being that we can actually go and look at that data by tag. 29:00 So if I go into the you see annoyed, that's what it should be. 29:03 This is dummy I did ask the guys to go back 29:07 and edit some of their previous time entries to be annoyed. 29:10 So our company isn't actually as annoyed as you're about to think it might be. 29:14 But I've asked them to do this. 29:17 So we are able to go and actually have a look and say well 29:18 I can see when people are being most annoyed over a particular period of time. 29:20 I can see that the most recent activity for when 29:24 people have been annoyed, the projects on which they've been. 29:26 Most annoyed, the top contributors to the tag annoyed. 29:29 Whether or not that annoying time was billable or not. 29:32 And I can even go into actually explore data, so if I go back a week. 29:35 This weeks' probably not the best. 29:40 I can go, well, show me for example. 29:42 I wanna find out of that where Liam was annoyed particularly. 29:44 And it was showing me, well, Liam was mainly 29:47 annoyed just on the 17th of October, and I can 29:49 go back and find out what Liam was doing on 29:51 the 17th of October, and why he was particularly annoyed. 29:52 Now, we had already been using this system for our own general 29:54 tracking of what we're doing, what we're up to, and exploring the data. 29:57 Cuz we like data. 30:00 We hadn't been asking people to genuinely go for it. 30:01 And tell us when they were annoyed. 30:04 When they're pissed off. 30:05 When they were cold. 30:06 When they were tired. 30:07 When they had, you know, been kept up late by the baby. 30:08 Whatever. 30:10 And so we've actually started to ask people to 30:10 record their genuine emotions within the within the time tracking. 30:12 Because of the fact that, that it, it, it gives a 30:17 whole nother layer to To what people have been up to. 30:19 Now, you're about to see why it's important 30:23 to actually record the words alongside the data. 30:25 And we, we are kind of coming to a close now. 30:29 And I'm, there, there's gonna be a a few funny bits in here. 30:31 But first I'm gonna tell you the net result, which is the. 30:36 We haven't calculated this exactly. 30:39 But, and, and keep in mind that 30:42 we were looking at people's perceived productivity. 30:43 But combining the perceived productivity with some of 30:45 the data we got off our time tracker. 30:47 We're nearly up 10% productivity. 30:49 Nearly. 30:52 Which is pretty phenomenal, given. 30:53 All we've done is things like turn the lights off slightly later, 30:55 have an extra meeting where we stand up for a few minutes. 30:57 We've done very, very simple, micro things with our 31:00 business that have ended up making us way more profitable. 31:03 So, this is the, this is the any comment field, which I think was. 31:06 Respectively the best thing we added to our, our data collection and let's just 31:10 say the guys and girls at Storm have been abusing it quite, quite rampantly. 31:14 And I have not put all of them in, cuz of some them are a bit amusing 31:19 but anyway, this is why it's important to 31:22 record the words as well as the actual data. 31:23 This was a general comment, 11:30, fucking cold. 31:26 But later on don't worry, at 12:15, less 31:30 cold, have jumper, not sure who it belongs to. 31:32 Emotions, annoyed, frustrated. 31:37 Now if I were to ask you in a, in a web dev environment 31:39 reasons why you thought that this might 31:41 be recorded, you might be thinking, annoying client. 31:43 Bug which I couldn't fix. 31:47 Oh no. 31:48 Walked into Gladdy's bike and now my shin hurts. 31:49 Hm. 31:53 A general comment and this baffles me. 31:54 And frankly scares me slightly. 31:56 It's raining which means I'll get soaked on the way home and I like that. 31:58 Also get a shower when walking from the gym. 32:00 I like that too. 32:02 Multitasking. 32:03 >> [LAUGH] >> Who are we employing? 32:04 [LAUGH] Productivity basically unproductive. 32:06 Now this is probably a little bit more like the kind of 32:11 actual thing that people would be doing in a, in a dev shop. 32:13 Which was a, you know, it was great until 32:15 it ended on both feed backs and then whoops. 32:17 But nevertheless, [LAUGH] that, that shows that it 32:19 wasn't someone being unproductive because they were interrupted. 32:21 We actually know why. 32:23 A general comment at three o'clock. 32:25 Liam won't shut up. 32:26 General comment at 4:30, one hour 30 minutes later, Liam still chatting balls. 32:28 >> [LAUGH]. 32:34 >> Hm, Liam. 32:36 Emotions, excited, sense of achievement. 32:39 Now this is actually my favorite and genuinely makes 32:40 me think, what the hell is going on here. 32:43 But again,applying your brain. 32:45 Maybe we've had a, a particular win. 32:48 We, we've you know, won a new client, we've 32:49 delivered a little bit work ,something amazing's happened you know. 32:51 I'm feeling excited and I have a sense of achievement. 32:53 Because I'm back from home base baby, yeah. 32:56 [LAUGH] So that, that kind of is I'm gonna round up, like I 33:03 said I could go on with more stats, but I wanna leave some time 33:07 for questions and so I'm going to give you a few take away 33:10 tips uh,and hopefully you can remember back to the start of the, the talk. 33:12 Make sure that you've got the time, as well 33:17 as the ability to be observant with your new business. 33:19 Just, just tracking the data means nothing if you 33:22 haven't got the time to, to do anything with it. 33:24 You've got to leave yourself some time in business to do that. 33:26 And make sure that you're in a position 33:29 to quickly react when the opportunity has been observed. 33:30 Which kind of comes back to the time thing, but it also is, is one of process. 33:33 Not something that we have internally, but I know a company that has levels. 33:38 So an issue comes in, and they will. 33:41 Whoever gets it is, is told to make their best judgement on what level issue it is. 33:43 So on level 1 issue needs to go to management a level 2 issue can be dealt 33:48 with your general staff a level 3 issue is 33:51 something that can be handled by an admin staff. 33:54 It's quite simple they've got a simple process that means that when something 33:58 comes in it gets dealt with, there's a bit of a process there. 34:01 For quick wins you can focus on something very specific. 34:04 You don't have to go looking for 34:07 opportunities and hoping you stumble across them. 34:08 You can do things as simple as trying 34:10 to find the weakness points within your business i.e. 34:13 That point that when you run out of 34:16 something you no longer make any extra money. 34:17 So, you can find those and that's a quick win because you can go and 34:19 focus on it, collect some data around that and try and say I'm trying to improve. 34:23 Productivity of this type of person within my business, how can I do that? 34:27 And finally, you, you can have a bit of 34:31 fun while you're doing it, like we did and it, 34:32 it generally will give you sometimes a quite disturbing, 34:35 but nevertheless insight into the people who work for you. 34:37 So I hope you've enjoyed hearing a little bit about what we've been doing. 34:41 I'd be delighted to answer some of your questions about just our 34:43 general tracking experience, what we did, how we did it, or anything else. 34:46 Alternatively you are, of course, more than welcome to 34:51 find me and,and catch up with me during the breaks. 34:53 But for now thank you very much indeed. 34:56 [SOUND] 34:58