DataTalks.Club

Defining Success: Metrics and KPIs

Season 5, episode 3 of the DataTalks.Club podcast with Adam Sroka

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Transcript

Alexey: This week we'll talk about metrics and KPIs. We have a special guest today, Adam. Adam is the head of machine learning engineering at Origami Energy. He's an experienced data and AI leader. He helps organizations find value from data by building high performing teams – data teams, analytic teams – from the ground up. Adam also hosts a podcast. Is that right? (1:30)

Adam: Among other things. It’s slightly related to data and technology. But yeah, thanks very much for having me on. I am excited to talk about something I don't normally get a chance to talk about – KPIs and how to measure success and things like that. (1:54)

Alexey: Welcome. Thanks for agreeing to come on. Before we go into our main topic of metrics, let's start with your background. Can you tell us about your career journey so far? (2:11)

Adam’s background

Adam: I always start way back in the mists of time when I was a bartender – a competition bartender – I absolutely loved doing that. I traveled around Europe making cocktails, winning, and sort of being drunk for a living, which was good fun for a while. I realized I wasn't going to make enough money to buy a bar by working in one. So I thought “I'll go back to uni and get a proper job.” Went back to physics – really hot, really enjoyed that. Towards the end of it, I did a bit of coding. I went on to do a computational doctorate at the University of Strathclyde. That was modeling high power lasers. You might see some laser textbooks on the bookshelf behind me there. That was really good fun. It was towards the end of that that I started looking at reinforcement learning and data science things, just out of interest. I actually used some reinforcement learning in designing components for lasers, as part of that research. That was really good fun and it really opened my eyes up to what you could do with this kind of stuff. As much as I love physics and working with lasers, I realized that I didn't actually care too much about the domain. (2:22)

Adam: I just liked doing numbers on the computer, and I wanted to explore what you could do with some of these tools. What really appealed to me was the idea that if you learn some of the data science approach techniques and some of the ways of working, you can step into almost any domain – any organization – and offer value. Given a little bit of time and data, I can probably show you some charts or some models that might highlight some insights that you haven’t seen before, without being an expert in that field. Now, that’s quite a dangerous statement to make. I do stand by the fact that the absolute best people in the field are those that know their domain inside out. You can't really compete with those people if they have all the technical expertise and the tools and techniques as well. But you can go quite a long way as being a bit of a generalist and just being very good at applying statistical approaches and things like that.

Adam: So – physical doctorate, then I went on to start an online retail start-up with about six people in the east end of Glasgow. That was great fun. We were looking at trying to predict return behavior. Something like 30% of all orders for fashion (especially online) get returned. If you attribute, say, even a two pound cost per return to each item, for some organizations that adds up to millions of pounds worth of returns. They did a lot of clustering and you can really quickly spot some negative permanent/lifetime value customers by their early shopping behaviors. Actually, they're the same people that get flagged for lots of marketing and promotional material, because they spend a lot. It's a really good product, but it didn't do very well at that company. I left and then it kind of disappeared shortly after. It was just a hard thing to sell to companies. You're essentially telling sales executives to sell less stuff and that's quite a hard message to get across from this upstart startup in Glasgow.

Adam: From there I went into insurance. I absolutely loved that. That was really eye opening because the amount of information you get in car insurance when working in the pricing team – you can find out a lot about people. You can pull some really cool and interesting models. There's a lot of money involved, so there's lots of investment. There’s lots of impact that you can make by doing small changes. I talk a lot about that in some of the talks I do for beginners, like “Look, if you're trying to make a big impact, try and find massive revenue numbers or huge numbers that you can make small percentage changes in. They're the easy wins to get started with trying to make huge, 20-30% cuts in things from the get-go.” These are usually problems lots of people have already thought about, so thinking you're going to come in and completely change the game - apply a little bit of hubris. I just think that with insurance, there’s loads of money involved, so you can make a big impact. I had a great time there.

Adam: Then I went on to work for a consultancy. That's kind of where my journey changed a little bit. I've been a data scientist now for a few years. I was a senior data scientist at Incremental Group in Glasgow – a great consultancy. They do some really interesting stuff for small to medium enterprise customers. While they were offering positions, I restructured the business and I got offered the position to act as the data and AI director. That meant taking on the leadership/management role of the data science team, but also data engineers had BI consultants as well, and a few DevOps people and things like that. That was my first real exposure to BI – things like metrics and that kind of consultancy. (6:32)

Adam: We were trying to help organizations turn their ‘business as usual’ into stuff that was actually measurable. We could build the dashboards and charts. You're trying to sell them charts and dashboards – that's how we made our money. But that's when I started to come across some of the techniques for helping people in these workshops. Take a huge bakery as a customer example. They know how to run their business really well. It's family owned, it’s a huge company, it’s been working fine for years. Why change anything? Helping them take their ingrained experience and ‘gut feel’ and turn it into “What are the actual numbers that a layman like me could come along and understand on a chart?” “Okay, these are the things that drive performance.” That was quite enlightening for me to learn. I definitely wasn't the expert there. I'm definitely not the sort of person who claims to be an expert on it. I always think there's more to learn in that kind of space. Learning to work with BI teams and data teams in that regard was really helpful in a consultative role.

Adam: I was there for three years and very recently gone for the position at Origami. The stuff they're doing at Origami is super cool, it’s really interesting for me to try and help transform the energy sector. To be able to enable green energy to come in more easily, more readily, and do lots of very big, very complicated problems. Again, large scale stuff – high impact. I jumped at the chance, really. Even though doing consultancy was great fun.

Alexey: That’s cool. Doing reinforcement learning with lasers, I think that's the coolest thing I heard today. (8:53)

Adam’s laser and data experience

Adam: I did some cool stuff in my PhD, I won't lie. The core of my project was looking at using ray tracing techniques for laser software. In other words, using ray tracing software to design lasers. There are a few projects there. Z Max is the one that we used that worked for a huge, huge organization called Tallas. Z Max is lens design software – you can build basically any kind of lens and do lots of very clever stuff with it. You can't model laser material or laser-gain material in it. That was always a big thing that upset people. That was a shame - everybody in the company uses Z Max but it doesn't do laser materials, so the laser team was upset. (9:00)

Adam: Another piece of software that allowed me to manipulate it was MATLAB. Once I'm in MATLAB, I can do what I like. It was annoying because I built this stuff to automate Z Max and then a couple years after I finished, it became a part of the product. They actually had a big open library to do some of the stuff that I've done. I'd spent months building it that way. It was a bit ropey because I wasn’t a very good software engineer. Still am not. Yeah, it was great fun. Anyway, we got to a point where I did a few really cool projects, but one of them was looking at designing components that don’t only give the best performance for laser output via beam shape and power and things like that. But we're actually really resilient to misalignment.

Adam: Lasers are basically rubbish – all lasers are rubbish. There's an industry joke, which I actually think crosses over to data science and machine learning. A lot like in laser manufacturing, there's this thing that “Do it if it’s the best way to do something, or if there's no other way of doing it.” Essentially, with lasers it’s “Is there any other way of doing what you're trying to do? Don't use a laser. But if you have to use a laser, then go for it.” I feel like that's true for machine learning as well. If there's no other way of doing it – if you can do it with a basic model, something really simple – try and use that first. I bring that into the way that I work now. When designing these components, you would typically go and ask someone that's done it for four years, “What did you use for these parameters, blah, blah, blah.” Use their experience – they want to really help. We got this big piece of software that can simulate lasers now, because I've built it. Then I attached some sort of very rudimentary reinforcement learning thing with a few parameters to allow it to churn through.

Adam: I actually started with genetic algorithms to design the system, and then I did a little bit of stuff with reinforcement learning. This was back in 2014, and I was kind of self-taught. It wasn't as far on as it is now. Something I'd love to go back and do. It came up with some interesting stuff. Some of it was mainly poorly formulated – within the realm of the software, it was fine, but it would have been impractical to manufacture. It gave some novel insights and some that stuff was used or it's been investigated a bit further. I think. I'm not in the company anymore. I'm out of the loop. What they have done with it – Who knows?

Metrics and why do we care about them

Alexey: Coming back to the boring topic of metrics – I imagine that when you worked on reinforcement learning with lasers, you needed to track some metrics to measure the success of your project. What is a metric? Why do we care about metrics? (12:06)

Adam: Back to that thing of “why?” Like Peter Drucker says, “If you can't express it in numbers, you can't manage it.” There are a lot of ways to look at this and it's a point in the sand. I'm a big fan of getting going with metrics before you've got the right one, or before you've got them measured properly. I feel that people, sometimes friends, spend too much time trying to find the perfect metric. They do too much planning without actually getting going. Think about calibration errors – as long as you keep measuring the same way, you can kind of see where they are. Once you figure it out later, you can go back and rectify things. (12:24)

Adam: A metric is essentially just a form of measurement. It's something to measure against. You take an output and turn it into a number so that you can apply it on a chart and start seeing where it goes. For example, in laser design, you want high power and that's got to be above a threshold. It's a really easy example to use. You have a technical specification that the laser – the piece of equipment that you're building – must meet. It must operate to minus 30 degrees and to plus 70 degrees. It must withstand the force of exit. I must have a pulse length no longer than this and no shorter than this. Bah, bah, bah, bah, blah, there's also safety standards. These are all numbers – they're threshold metrics, a lot of them. You have to hit all these threshold metrics. That's really handy for something like a reinforcement learner, because you just fire all those in and you say, “Build me a system that hits these metrics.”

Adam: Now, in a system like that, we've got maybe 10 threshold metrics. There are probably an infinite number of solutions to that, right? You can find lots of solutions within that space. The challenge comes in when you want to find the optimal solution. Then you've got a really difficult problem to solve. That is where you start comparing your metrics to one another and try to improve them. When you're in high dimensional metrics, it becomes a messy bit. That's where a lot of things like running businesses and turning your models into business value are. I’m a big fan of using merit functions. Essentially, much as you would with a model, you take certain values – some of your metrics – and give them a weight. You add them up and you give that a score. People can get really lost in the weeds with defining what those weights are and how important certain things are. You can gain your own metrics by just keeping tweaking the nodules until you get what you want. There's a really good XKCD comic about that, which says you can just keep stirring the numbers until it gives you the output you desire. Don't do that, obviously – pick something and go for it.

Alexey: That's what you do in machine learning, right? (15:05)

Adam: Exactly. (15:06)

Alexey: Poke it with a stick until it works, right? (15:07)

Adam: Yeah. I was trying to convert things into a valid merit function. I think that gives you a certain number to push to – to measure against. It allows you to say, “Well, this thing is objectively better than that thing because of X.” If you even go back to basic consulting, any consultant you ever get into your company will always draw the grid of impact and cost as their two axes and then plot all your potential projects. This is a good thing to do with your potential data science or data projects that you might be doing. This is genuinely a worthwhile approach. You plot, “How hard is this thing to do?” against “How much impact is it going to have?” and then you just pick all the ones at the top – all the ones that have high impact and low cost. You do them first and you work your way through. If you calculate those axes and put numbers 0 to 10 on them, naturally, you can get a score, right? One number. Then you just rank them in order. That's a really good way of prioritizing the projects you're going to do. I get flack for that, because people argue that you can't put time bounds with data science. You can't put tightly defined things based on that. Maybe we can get into that and running teams a bit later. That's something that I think is really valuable to do. (15:11)

Alexey: Basically, a metric is a number that converts an output of some system to a value, right? Then it tells you how the system is doing and we can use it to compare multiple systems – multiple things. Then we can say, “Okay, this thing is objectively better than that thing, because the metrics say so.” Is that right? (16:28)

Adam: Units are really important. Being a physicist, units are super important. Even in business metrics and things like that. Because you have to make sure you are comparing like-for-like. (16:51)

Examples of metrics

Alexey: Can you think of some simple examples of metrics? I think you mentioned revenue? What are the other commonly used metrics that you saw when working at a consultancy company? You probably worked with many clients. Are there some metrics that most of the clients have? (17:01)

Adam: Yes. There's all the ones you hear about, sort of “Accounting 101.” You've got revenue, profit, costs. You can go a little bit deeper than that. If you start working with sales functions and marketing teams, there's a very common language lots of them use, and you'll hear about “the pipeline.” In a sales team, they'll typically be expected to gauge projects that are going to land. That is, they will rate them by probabilities. They’ll say, “Right, this project will land,” and they'll give it a weight. Say it's a 300 grand project and it's 50% likely to come in. So the revenue of landing that project will be 300 grand. But what they will then do is multiply that by the 50% and say that that’s the ‘weighted revenue’. This is a really common metric. So, the weighted revenue is 150 grand if you trust your probability ratings, which no one ever does. That's a pretty hard thing to do accurately, right? If you trust your probability ratings and your estimations of the project size, you can then say, “Yeah, the 10 million pound long shot that I've got down as a 5% probability, is more valuable than the 300 grand that I've got a 90% probability on, because you can multiply them off.” You pay attention to the weighted revenue and then make your decisions and focus your efforts on that kind of thing. (17:22)

Adam: You've also got things like marketing interactions – that one is really good. You get qualified leads and sales. Qualified leads and market-qualified leads. If you've got websites or people download your content, that's a marketing interaction. That's a great metric to measure or maybe even turn into a KPI for your marketing team. You want people to download your stuff, right? It's maybe not the only thing you want, though. If you're just ticking that box off, you can go and get 10 to 100,000 more interactions by doing a few TikTok videos than you can by writing really good content. Those TikTok videos might not, and probably won't, convert to any actual money. This is the point where someone that attends an hour-long webinar or a day-long event is far more likely to give more of their time and commitment over to you. So you go from market directions to leads and market-qualified leads.

Adam: Marketing organizations might have criteria and they use all these funny acronyms like BANTR. It was, “Did it have a budget? Do they have the authority? Is there a need? Is there a timeline? And is there any risk?” They will have these acronyms and you answer these questions to see if they tick off all the boxes. Then it goes from a lead to a marketing-qualified lead. Sales have another checklist and they'll take it a step further. This is what they call ‘the pipeline’ – as you go down the steps, you’re getting ever closer to actual money in the bank. You draw this for most organizations with a sales function.

Adam: These are really handy things to learn, especially as a data scientist, because every relation charges money. Well, the vast majority of organizations charge money and they sell things to people. They're the things that your company is going to care about. You also have costs and stuff like debt. When you get to professional services, you can start to come up with some pretty interesting derived metrics. You can go from simple stuff that you'd understand to things like “burn-down rate”

Adam: In the professional services business, I've got 100 consultants – say they will cost X pounds a day to hire out to people. Well, I've got a backlog of work that we're churning away at. At what rate do I churn through my whole backlog of work? That burn-down rate – if I'm churning through 50 grand worth a day because I've got so many people, I need to be selling at least 50 grand a day. Otherwise, we're going to start having redundancies for people. Thus, if your burn-down rate is higher than your sales rate, you've got this situation where you're going to run out of work for people to do and eventually bankrupt yourself or make people redundant. That thing is called ‘maintainability of earnings’. We're now into the tertiary layer, where these kinds of derived metrics will come out. They're interesting things and they’re bound to some organizations – it depends a bit on where you go. You'll have similar stuff. (20:46)

Adam: We did a lot of professional services. I worked at and ran a professional services company, so that's where I'm more comfortable, I'd say. But you get the same thing for manufacturing – things like Lean and Six Sigma – they look at how many defective products in x1000 builds and things like that. These are all good metrics to look at because they allow you to turn fluffy terms like “quality” into something you can measure. When a new person gets brought in to improve the quality of our product – what does that mean? “What do you mean quality? Define quality.” That’s always my first question. That can get really hairy. If you start doing big projects and investing money to improve quality without defining what that means, you'll get arguments down the road as to whether or not he was effective.

KPIs

Alexey: One thing you said was that some of these metrics are so good that you want to turn them into a KPI. So what is a KPI and how do we turn the metric into a KPI? (22:41)

I always think of KPIs as sort of the metrics that you want to look at from a top-down point of view. So from bottom up where anyone could use metrics in their own work, I have my own metrics that I don't really use for anyone else – the way I work, things that I do that I need to check off. I’m a big fan of checklists, but they don't align with everyone. They don't guarantee good behavior across teams and people might not understand them. Whereas KPIs are things that organizations may have highlighted to say “This is going to drive behavior.” They're essentially metrics – key performance indicators – where you are measuring the performance of a team or an individual or a function against.: =Back to the sales example, you might say weighted revenue, the number of sales, and revenue might be KPIs – you pick a few. It's quite important that you pick a few. I actually encourage people to pick some that maybe conflict or maybe aren't quite necessarily all in line with one another, because it helps encourage good behavior. But KPIs are essentially the things that you are going to put in front of the CEO to say, “Thumbs up, thumbs down – is this team/function/project/whatever doing well? Is this a good thing? Should we put more money into it?” There are a lot of rules around making good KPIs. But you want them to be easy for people to understand and grasp quite quickly. (22:53)

I always think of KPIs as sort of the metrics that you want to look at from a top-down point of view. So from bottom up where anyone could use metrics in their own work, I have my own metrics that I don't really use for anyone else – the way I work, things that I do that I need to check off. I’m a big fan of checklists, but they don't align with everyone. They don't guarantee good behavior across teams and people might not understand them. Whereas KPIs are things that organizations may have highlighted to say “This is going to drive behavior.” They're essentially metrics – key performance indicators – where you are measuring the performance of a team or an individual or a function against.: Make a nice chart – make it nice and easy. I might look at a map and there are like seven different colors on it and it's trying to tell me things that are confusing, but if I see a heat map and it's just going from blue to red, then I know very quickly exactly what I'm looking at. Turning stuff into KPIs that are easy and visual, that becomes really important. Then you just use them as milestones or checkpoints. You frequently measure whatever it is you're looking to monitor against those KPIs, and you would hope that they drive performance.

I always think of KPIs as sort of the metrics that you want to look at from a top-down point of view. So from bottom up where anyone could use metrics in their own work, I have my own metrics that I don't really use for anyone else – the way I work, things that I do that I need to check off. I’m a big fan of checklists, but they don't align with everyone. They don't guarantee good behavior across teams and people might not understand them. Whereas KPIs are things that organizations may have highlighted to say “This is going to drive behavior.” They're essentially metrics – key performance indicators – where you are measuring the performance of a team or an individual or a function against.: The big thing for me is, you want to make sure your KPIs are aligned to the company strategy. Typically, we would say “Your company should have a strategy of what it's trying to do,” then your function or whatever needs to align to that strategy, but more specific to what they do. You just follow that hierarchical alignment all the way down to the individual. To a point where you, as an individual, can say “My quarterly objectives/my performance/my bonus/ my appraisals are all aligned to something like a metric or KPI that feeds higher-up KPIs, which feed all the way up to delivering part of our company strategy.”

Alexey: Basically, a KPI is an important metric. This is a metric that we want to put maybe in a dashboard, and then maybe on a screen, and then everyone can watch it. (25:56)

Yeah, or on a report. Basically I think that's what a KPI should be. But they end up being just more numbers that you, as a data professional, have to go and find, collate, produce, and put in a report and no one will ever read. People love them, but the challenge with KPIs is exactly that people love them. They just want all of them, so they make KPIs that may be too specific or too niche. I always think – when you're defining KPIs, what behavior are you trying to drive? If they don't drive a behavior, cut them out. They must drive individuals or teams to behave a certain way. There should be consequences, both positive and negative for that behavior, which goes in the line with or against the KPI. That or KPIs should be used to make decisions. That number should go towards making a decision. Unless it's a vanity metric, which is a common thing you hear.: =These are something like “number of customers spoken to.” That's something that can be an alright sales metric, but sometimes it's a vanity thing “Well, I did this!” But it's putting ‘busy’ over ‘important’. There's a really good quote from, I think, McNamara. Unfortunately, it's about the Vietnam War, but it's a good quote around metrics, in my opinion. He says, “We should be careful not to make the measurable important, but to make the important measurable.” That's basically saying, “Don't get numbers that are easy to count, get the ones that we make the most important.” So when someone says, “My project’s 5000 lines of code,” that’s an easy number to get and it's not important. I think even Bill Gates said something like, “That's like measuring an airplane by weight.” That's not a good thing to measure an airplane by. Lines of code is not a good metric to measure your code products by. Make what’s important measurable. That's what can actually be quite difficult, depending on what you’re doing – especially in software. (26:07)

KPI examples

Alexey: You said that a KPI should drive some behavior. In your example, you were working at a startup and you were trying to minimize the number of returns. So would the number of returns could be a good metric that drives behavior? The behavior is that people get the clothes that fit and they don't need to return it. Is this a good example? (28:04)

Adam: Yeah. They get really dangerous though, don't they? This is one of the things about KPIs – you have to be very careful. When I was consulting, one of the things we encouraged people to think about was malicious actors within your company or within your customers or wherever. Think about people that could unlock a really big bonus – a 10x salary bonus. Say you started a new position and someone could get 10 times their salary if they hit the KPI for the year. What would they do if they were malicious, or they were cheaters? Think about, “Okay, so you've set a KPI to reduce the number of returns. Alright, then. Make it cost 100 pounds to return any item to this shop. We’re not gonna get any returns anymore.” Or, actually, I'm just not going to sell anything. I stopped selling stuff and stopped getting returns at the same time. It’s a stupid example, but it highlights the point, right? So, think about malicious actors. (28:33)

Adam: That is a bit of a physicist thing. Anyone presents any kind of model to you, and you immediately go to the extremes to try and break it and see if it still works. That's a really good thing to do with KPIs. It's not good enough to think about the spirit in which they're written. The spirit in which that has been said is “I want to reduce returns because that's going to save the company money.” But if you don't link that to sales in some way, or inversely get the sale, or you don’t give the same person a KPI to drive sales up, you could get into a really sticky spot. That's why competing KPIs are really good. If I increase sales, I'm going to increase my returns. So actually having both of them as KPIs – increase one, reduce the other. Use that on that stupid example I've just come up with.

Derived KPIs

Alexey: Okay. That's interesting. Yeah. So, we should try to make them less hackable? A good idea – maybe in this case, we can derive some other metric from both returns and sales that covers what we really want to do. We want to maximize the margin, right? Because when people return things, we lose money. We don't want to lose money, so let's do something that maximizes the margin. (30:30)

Depending on whom you're working with. There are two approaches and I think they're both valid, depending on your situation. I mentioned maintainability of earnings, which is like a tertiary derived KPI. If I said that to anyone on the street, they wouldn’t have a clue what I'm on about. Say I'm talking to a huge organization about one small part of this derived metric, their executives might not really understand it. Think about your audience. Derived something over the other, like sales over returns, or something like that. That's good if the people I'm sharing it with, or using it with, understand what it is really easily.: =If I'm talking to a data scientist, I don't have to explain the difference between supervised and unsupervised machine learning. But when my mom asked me what I do for a living, I say “Work in IT.” You have to think about the level that you're communicating at. When you're communicating at that higher level, or maybe a bit zoomed out, or people with less time or that just aren't as close to it, I consider reporting both numbers. Don't just report the derived metric, report both of them. Yes, sales have gone up and returns have gone down. I get that. That's great. You've used up maybe one extra thing. It's easy when you're talking about that. When you get to more complicated stuff, you don't want to report 10 KPIs on a chart because that gets confusing. There's a balance to be made. You have to really think about your audience. But that's true of anything, I think, when data is part of the game. (31:01)

Creating metrics — grocery store example

Alexey: We already have quite a few questions, and I think I want to combine two questions into one. The first one is, “What is the process of coming up with the best metrics?” Then another question is “What KPIs are important for retailers and grocery stores?” I don't know if you have experience with retail stores. Maybe you do. I was thinking that maybe we can try to come up with some metrics that are important for grocery stores – for retail. And then also go through the process of coming up with these metrics. (32:44)

Adam: Look, I still do a bit of consultancy myself on the side, actually. If anyone's really keen and wants to go through the workshops with me, I can come up and do it – just reach out. I need to know what kind of grocery store this is. What is the company's strategic goal? Now that is a rubbish word to use, I think. I say it a lot because I've been a consultant, but ‘strategic’ and ‘tactical’ and all that stuff really does annoy me. What I mean is, ultimately – What is this company trying to do? What is this organization trying to do? Because if this grocery store is maybe a third sector organization trying to provide healthy foods to a deprived area, revenue’s not going to be a metric for the KPIs I’m going to use. I might look at your number of new customers or the number of return customers. Things like “Can I reduce the average basket cost compared to the closest supermarket?” “Can I count the number of meals produced per family?” If that is my strategic objective – to bring a healthy low cost food option into a community, that's great. (33:20)

Adam: If I'm a company that wants to grow, then profit might not actually be my objective. For anyone who's worked in a start-up, ‘profit’ is this long distance thing no one thinks about for quite a while. Because you might have lots of investment to be the next Liddle and you're going to disrupt the UK supermarket space by coming in and putting sites down across towns. Then you’re trying to get things like cost leaders – products that will bring people in the store to try and capture people from the biggest supermarkets. In that case, ‘new customers’ is going to be really important – just the volume of people coming through my door. Again, marketing interactions and stuff like that. “How many people can I sign up for my club card and my loyalty scheme?” That's a really important metric to me, because I'm hopefully going to be able to get them back. People I can get data off of, like their email addresses – “Can I pester them with offers and deals?” That's about widening my net, drawing more people to the store. That’s not the kind of stuff my lovely, healthy-eating, local supermarkets are going to do.

Adam: How do we go about defining that? Well, I come in, usually waffle on for a bit like I have here, get the whiteboard out, and then start asking the people at the top of the organization or the top of the team or the function, depending on what level we're doing this in, “What's important to them? What do they think good looks like?” Steal other people's hard work as well, as I say some of the time. Who's done what you're trying to do? Well, can you find blog posts by them? What did they do? What are their metrics?

Adam: You mentioned North Star metrics to me in the lead-up to this, I think Spotify’s North Star metric is something like “number of minutes listened to.” That is it. “How many minutes of audio are people listening to on their software?” Brilliant. Just increase that number. That's really all they want to do, because that captures so much stuff. There's loads of ways of doing that. Yet, people aren't really obsessed with it, there’s only 24 hours a day to listen to music all the time. And to get people like me to share it and spread it to other people. That's all gonna pyramid up into that one number at the top and lots of stuff underneath it. So – you come in, do a bit of a workshop. You don't have to have an idiot like me do that – you can do that yourself. You just talk about what's really important. “What are we trying to achieve?” Once you've got that, think about “Okay, well. Actually, is profit on the table? Is profit still really important to us?”

Adam: In most cases it will be. So we'll maybe keep ‘profit’. But “Do I want my individual customers in the grocery store to spend more each?” “Do I want the people coming in for their weekly shop to also buy gift cards and expensive electrical items?” That's a great way to increase the average basket price. Or “Do I just want more people through the shop?” and things like that. Once you've got those, these are the important things to us, put more than you need up on the board and then basically rank them in order. You don't want to have like 50 KPIs – you just want a handful. If every individual can't remember them… again, these need to drive behavior. The people whose behavior they are driving, they need to be able to remember all of their KPIs. If I've got 15 KPIs, I'm not going to keep them all in my mind. If I've got five, I can probably keep track of “All right, I need to do these things and I can see why that's important.” (37:19)

Adam: They should then help smooth conversations and stuff going forward about decisions. Someone wants to do something and it seems a bit weird, you can go “Right. Does that affect KPIs?” If it's all nicely lined up, it makes the decision-making process easier. Then, don't be afraid to get going and to change. You have to find a balance of “try them out” without ‘perfection is the enemy of good’. Try them out and get some data. Give them a shelf life, but have a set review point. In other words, give them a chance, but not forever. Say 6 weeks, 12 weeks, and go “In six weeks, we're gonna have a look at these. Did anything change for the better? Has it helped?” Don't be afraid to change them. Don't be too precious about them.

Adam: One of the other great things you can do is look at historical data, if you've got it. This becomes really important. How you're collecting the data is really important. It's all well and good to say “I want my customers to be really satisfied.” But I don't have any means of contacting them. I can't send them surveys. It's all anonymous web transactions that even get email addresses on them. Okay, that's not gonna be a great KPI, because you're going to struggle to get the data. I actually have a sporting events company as well, and we've run events and ‘satisfaction’ was one that we wanted. We had to run focus groups to get that as a feedback thing. But I have to incentivize people to attend them, because I'm taking time from them. That was really difficult.

Adam: But it was really important to me. If I put that in as a KPI for my organization going forward, even just doing the KPI becomes a whole industry in of itself. If it's difficult to do, it's going to start causing people to cut corners, and you want to automate that as much as possible, really. Being a data nerd, you should want to do that anyway.

Metric efficiency

Alexey: How do we evaluate the efficiency of a metric? I think you mentioned that metrics should be easy to measure. If it's difficult to measure, then people will try to get away from measuring it. So how do we measure effectiveness? You came up with a list of metrics, and then you said we need to reduce the number to just ‘a bunch’. How do we go from that to a smaller set of metrics? I guess we need to evaluate each one, and then reduce. How do you usually do this? (40:24)

Adam: For the ones we've come up with, say we've then got a shortlist of five metrics – or five KPIs. I would then, as a warm up to the workshop, say “If it can be shared with me, bring in old board reports or old performance review stuff from the team or the organization.” Typically I do this at the company level when they have these management information board reports. So bring them in and then you can look at historically, “Okay, can we create these metrics?” And then I would maybe go away with that data and create a few slides saying “This is what I roughly think these metrics might have been if we had the data.” Then, have an open discussion, “Would we have made a different decision having seen this trend line of this metric?” That's the other thing – obviously, collecting the data is important, we all know that. But you also have to report on it. You have to make it super visible. Lots of people, or companies, have this weird thing about having KPIs and then never sharing them with the staff. They make sure the executive team knows them and no one else. Why? Make them obvious. (41:07)

Adam: Make Power BI / Tableau dashboards and mount them in your SharePoint or your Slack – put them in places that people can check them really quickly. “What are the current marketing interactions?” and stuff like that. This way everyone's informed and involved. Actually, in a few places I’ve worked, they've done this and I tell customers to try this – During your ‘All Hands,’ if you do a weekly or a monthly one, have the executives explain the KPIs regularly. What they mean, what the executives think of them. It brings everyone on that journey.

Adam: Then ‘effectiveness’ – this can be a tricky one. I've done a lot of work with charities back at Incremental Group. One of the things they really struggle with is what they call ‘outcomes’ – “How do we measure our outcomes?” For a childcare charity, I interacted with children with difficult pasts and tried to keep them on a path to education and good health and all that. Have them stay out of trouble. How do you measure the outcome over the course of that person's life? There's no control group. That becomes a really difficult thing. You have to agree upfront, “Right, we're going to set these as targets.” KPIs should trend towards something. It's not good to have KPIs that sit stagnant – you want them to go one of two ways. Effectiveness then is looking in one of the regular reviews, going back and saying, “Are we using this? Is this a useful number to us? Does anyone actually care? Have we made decisions based on this number? Or do we always just say ‘Yeah, but actually, we'll ignore that because of this.’” And if that's the case, bin them. Don't keep them because you thought they were a good idea. Iterate on them, improve. Agile, right? Make better ones.

Adam: Try and find something that works for you. That's why I say ‘steal other people's hard work.’ Look at what other people have done, but make it yours. Take it and try and tweak them to fit your situation because it'll be different. This isn't easy stuff, alright? This is why companies like KPMG get paid millions to do this kind of stuff. Because it's not straightforward – it's a difficult process. The advantage of consultancies – and I'm not a consultant anymore, so I can extol their virtues without sounding like a salesman – the advantage of consultancies is that they’ve done this more than once. A lot of consultancies will have done this multiple times and have frameworks and processes to help you with this as a grocery store. Maybe you've never done this. Maybe this is your first time thinking, “How do I measure my business? This sounds like a thing I want to do.” Become a bit more forward thinking. Do it from scratch, be advised by using people's experience and go from there.

North Star metrics

Alexey: Thank you. You mentioned the North Star metric. In the case of Spotify, this is the number of minutes listened. In the case of YouTube is ‘how many minutes of video have people watched. So, the North Star metric is what exactly? Is it just a single number that’s the most important KPI for the company? (44:59)

Adam: Yeah. Often you’ll find, like the Spotify one, you can capture lots of different things rolled into something super simple. That's the best metric and the best KPI – ones that are really simple. Simple to the point where you can tell anyone on the street in a couple of minutes what your company does and what the metric means, and they'll understand. That's the number you want to go up or down and that's pretty much it. It’s trying to find something that that does that for your organization. Like the North Star, you use it to guide your decision-making and your other metrics should align to it. It should capture, in essence, what you're trying to do. Some of them – like for these big companies, they're very good at this – some of them are marketing tools a little bit as well. The metric itself is a bit of a marketing tool in the sense that “That is our mission statement. This is what we're doing. Here's how good we are.” It becomes easy for me to look at that and go “That's a big number. Oh, it’s gone up.” or “It's doubled in three years, blah, blah, blah.” (45:21)

Threshold metrics

Alexey: You also mentioned at the very beginning, when we were talking about lasers, you said there’s a thing called “threshold metric”. What is a threshold metric? (46:34)

For me, threshold metrics – as I've seen for most KPIs, you want them to be above or below a certain point. That is, you always want them to go either up or down. This shows improvement in growth. But there are some that just need to be at a certain level. If I'm running an airline – passenger deaths – I want this metric to be zero and I want it to stay at zero, right? That's it. If it goes up – as soon as I cross that threshold, I have to do something about it. That's an extreme example, but probably a good one. These will be like health check factors within your organization. Things that, if you cross the threshold, there's a significant issue that needs addressed. For a SaaS company, this might be a data leak. You might have data breaches.: =They can also be more vague – I've named a lot of very binary examples there. I'm trying to think of some that aren't binary. You might even have things like customer churn rate – the number of users you lose every month. You might not actually care too much. If your plan is to acquire loads of users, then churn rate might not be super important to you in the short term, but it will be in the long term. But in the early stages, it’s just “acquire, acquire.” So instead of saying “We'll just ignore churn,” you might say, “We need to keep churn above or below 5%,” or something like that. “If it ever crosses 5%, we do a review.” Then we start to think “Do we now need to introduce something else to drive behavior? Do we need to change the way we operate? Do we need to change the way we work?” (46:45)

For me, threshold metrics – as I've seen for most KPIs, you want them to be above or below a certain point. That is, you always want them to go either up or down. This shows improvement in growth. But there are some that just need to be at a certain level. If I'm running an airline – passenger deaths – I want this metric to be zero and I want it to stay at zero, right? That's it. If it goes up – as soon as I cross that threshold, I have to do something about it. That's an extreme example, but probably a good one. These will be like health check factors within your organization. Things that, if you cross the threshold, there's a significant issue that needs addressed. For a SaaS company, this might be a data leak. You might have data breaches.: Until it hits that 5% warning light, we'll kind of be happy with that. You measure it, you do all the reporting on it, and then if it's a thumbs up, it's a thumbs up. You carry on. That's how I see threshold metrics. You're not going to actively drive a threshold metric – you just want to make sure it stays up on the right side of the threshold.

Health metrics

Alexey: Is it a similar concept to the “health metric”? (48:48)

Adam: Yeah, or like “hygiene factors'' as they get called sometimes. Those are things like, “This must exist. If it doesn't exist, the game’s off. This has to happen no matter what.” These could be regulatory things. These could be health and safety. These are quite common in these kinds of fields. (48:52)

Alexey: So, what is a health metric? (49:10)

Adam: It's just things like downtimes – that’s a really common one for SaaS businesses – the downtime over a number of days, months, years or whatever. Or the percentage of servers that are up and stuff like that. If that is trending the wrong way, you know you've got an issue in the health of your service. It's asymptotic almost, in that it is either good or it's something's going wrong. You don't want 200%. It's not a thing that’s gonna drive your business. As long as it's sorted, you can kind of ignore it. (49:12)

Alexey: So it's like a threshold. You don't want a high number of downtimes, for example. (49:58)

Adam: Yeah. But with a health metric, there's some leniency. Whereas with the threshold, you would say hardline – there's a problem. With health metrics, you might say, “Okay, there's gonna be some downtime because we can't control everything.” (50:02)

Alexey: Yeah. Are there any other kinds of metrics that are important to know? We talked about KPIs. We talked about the North Star metric. We talked about threshold and health metrics. Are there any other types of metrics that are important? (50:17)

Adam: Yeah, there are probably very industry-specific ones and things like that. I'm trying to get very general cases, or the actual theory behind them. This is the kind of thing that a really good BI consultant would be able to help you with and say, “This is how I would turn this in. This is what I've seen before.” Then we would start using other people's experience. As for kinds of metrics, I'm not sure. I draw very much on the specifics of what's important to the particular business. (50:32)

Data team metrics

Alexey: We also wanted to talk a bit about metrics that are specific to machine learning and data science. Let's say we have a data science team. What should the data science team care about? We can take the grocery shop example. Let's say the grocery shop went through a digitalization process. We have a data science team in the grocery chain. What are some important things for the data scientists to know for their work? (51:12)

Yeah, this is good, actually. What you'll find is – if you leave it to the data scientists, they'll come up with a load of metrics that are very technically focused, and they will be around model performance. It'll be around accuracy and things like that. These metrics are important – they are important. But I often joke that no one cares – no one outside of the data team will care about them. In a data team, in a wider organization, it’s different if technology or data is the core of what your company does. That's a slightly different thing. But in the grocery store example, or when I was in insurance, it was a nice to have or an add-on or added value.: =Try and convert everything you do – model, accuracy, and all that – try and convert it back to money or seconds. And leave it at that. Ultimately, if the CEO of the grocery store group comes along, he's probably got a background in selling groceries or running businesses. He doesn’t have a background in machine learning and data. So if you tell him, “I've improved my random forest accuracy by 6% by doing this, that and that,” that doesn't mean anything to these people. But if you say, “I've improved the model and it leads to 10,000 pounds a month improvement across our whole revenue.” Okay, I can see the return on investment. This is something that I did very early on. Then once you're in that mind space, it helps you as a data scientist individually as well, to not waste time on stuff like polish and gold plating. (51:48)

Yeah, this is good, actually. What you'll find is – if you leave it to the data scientists, they'll come up with a load of metrics that are very technically focused, and they will be around model performance. It'll be around accuracy and things like that. These metrics are important – they are important. But I often joke that no one cares – no one outside of the data team will care about them. In a data team, in a wider organization, it’s different if technology or data is the core of what your company does. That's a slightly different thing. But in the grocery store example, or when I was in insurance, it was a nice to have or an add-on or added value.: That's something else a lot of data scientists are really guilty of, “Keep tweaking the model!” It’s fun, right? It's interesting. That's right, we love it. But sometimes, the 80/20 is good enough. You just get the bulk of it done. Get to a point where you've paid off the bulk of the work and you know that it's going to take the same amount of time to do other ‘diminishing returns’ type stuff. Think in pounds and seconds – save them. The reason I like pounds is because everyone understands that – it’s the universal language of business. The higher ups will understand it. Seconds is a good one as well, because you can talk about ‘time saved’ and things like that. That's easy for people to get.

Yeah, this is good, actually. What you'll find is – if you leave it to the data scientists, they'll come up with a load of metrics that are very technically focused, and they will be around model performance. It'll be around accuracy and things like that. These metrics are important – they are important. But I often joke that no one cares – no one outside of the data team will care about them. In a data team, in a wider organization, it’s different if technology or data is the core of what your company does. That's a slightly different thing. But in the grocery store example, or when I was in insurance, it was a nice to have or an add-on or added value.: You don’t have to explain to the people in the sales department what you mean by “Your F1 score” and stuff like that. Or your ROC/AUC, right? These are all numbers that we love, because we get them. If you're talking to me, please tell me your ROC/AUC and your F1 scores – I like all that. But if I'm then going to help you try and sell your next project to the function lead, let's do it in pounds or seconds. They're the numbers that are on slides. Always think “I'm going to present my argument in four or five slides. Then my boss (or whoever I'm presenting it to) is going to copy exactly those slides and present them off on the chain.” If you think about that – is your boss's boss going to be able to explain your F1 score to their boss? If the answer's ‘no’, let's go back to pounds, seconds, or whatever. Again, in some organizations everyone's actually super technically literate. It might be that you’re the star and everyone's a data scientist, in which case, throw it out the window, go nuts. You'll find your own way there, but it is a useful approach for other cases. A rule of thumb.

Experiments: treatment and control groups

Alexey: You also mentioned measuring and control groups, so I guess this is something that’s also important. Once you have a model, you want to measure it. How do we usually go about this? Maybe you can just go a bit into the details of how we can do this? (55:42)

Adam: So do a sort of “implement a model how-to”? (56:02)

Alexey: Yeah. We have a metric. We have a model. If we say “AUC is 80%,” nobody will care, right? We need to come to the business people and say, “Hey, my model generated that percent of uplift in minutes. Now my new recommendation system at Spotify causes 10% more minutes to be listened.” So how do we actually measure that? (56:07)

It's really hard actually. That is really, really difficult. If you're lucky enough to live in a world where you've got a good simulator and the cost of simulation is low – then simulate it. Reinforcement learning is great, but that's a rare case. If you've got good historical data, whereby your actions don't make much of an impact on the state of the world, i.e. the stock market, you can play against the historical data. That's really good, like back-testing and looking at the way that it affects that. That's a really good approach. Again – really difficult.: =What we did in insurance companies, and what lots of SaaS companies do is – market teams are used to this – A/B testing or ‘champion challenger modeling’, we called it. For a very small percentage of our customers, if you've got high enough customer numbers, a very small percentage would randomly get selected as the ‘challenger group’. They would be served a different model to the one that's in deployment. Maybe there's no model in deployment at the moment, that’s fine. 95% of the customers that come through, they go through that normal procedure, but 5% get siphoned off. And it has to be random. Be very careful about how you randomize things. Because using things like birthdays and stuff like that, sometimes causes issues that turn out that it’s not quite random. So, 5% of them get through the new model. Then, once you've built up enough data, you can compare like-to-like. Because they were the same timeframe, they're the same thing. If the challenger model beats the champion – the one that's currently running – you then promote it to the main model. Then change something else. Don't change too many things at once. Change one thing at a time, and you'll be able to slowly but surely see impact, over 2-, 3-, 5-week period depending on your sales volume, your customer volume. That's a really good way of seeing, if you're in a live environment, how to measure your impact and things like that. (56:35)

It's really hard actually. That is really, really difficult. If you're lucky enough to live in a world where you've got a good simulator and the cost of simulation is low – then simulate it. Reinforcement learning is great, but that's a rare case. If you've got good historical data, whereby your actions don't make much of an impact on the state of the world, i.e. the stock market, you can play against the historical data. That's really good, like back-testing and looking at the way that it affects that. That's a really good approach. Again – really difficult.: I realized that I dodged the question “What kind of KPI should software data science teams care about?” I talked a lot about communicating your output to other people. But internally, there's quite a lot you can do as well. You can look at that outcome value and look at the impact made. One KPI I love for data teams is “number of reusable BI tools/applications/bits of software/pipelines” that are being used and reused. If I build a function to help me get data from somewhere and manage it a certain way, if that gets turned into your utility, you can use it as your next project in the same team. That's a really good KPI to look at.

It's really hard actually. That is really, really difficult. If you're lucky enough to live in a world where you've got a good simulator and the cost of simulation is low – then simulate it. Reinforcement learning is great, but that's a rare case. If you've got good historical data, whereby your actions don't make much of an impact on the state of the world, i.e. the stock market, you can play against the historical data. That's really good, like back-testing and looking at the way that it affects that. That's a really good approach. Again – really difficult.: There's a really good post, by Domino Data Lab. I'll try and find it to share. They talk a little bit about this. I think they’re old now, but they talk about some of these ones – they're really good. Also still a bit hard work for what software teams use. They're not always going to work, but things like ‘models deployed’ that can be quite good. How much stuff is in production? Are you putting things in production? How many service users, internally, are using your models? It's all well and good to have a super cool neural network, but nobody uses it. Don't waste time, right? Sorry, we lost that question a little bit.

Accelerate metrics and timeboxing

Alexey: We already talked about measuring the effectiveness of teams by how many users of the service there are, and how many parts of the pipeline are used. The question is, “As a manager, do you find accelerate metrics, like ‘lead time’ ‘deployment frequency’ useful for measuring your team's performance? (1:00:02)

Adam: Depends on the team… I do. Yes, but it depends on the team. I've run fairly big teams of data professionals – by the 10s – and I found that a lot of them really resist being managed. I don't know why. But it seems to be a thing: in data, we don't like to be managed. I don't know if it comes out of academia. I was like this as well when I started. You kind of want freedom to explore and solve problems and do stuff. That just doesn't work. It doesn't really work for many organizations. Sometimes it does, but most of the time, it doesn't. One of the things I learned from Elizabeth Halljoy, who is head of insight for Greco – she's done really good things and they've got a great data team there. She did a talk a while ago about how they do everything. I've kind of changed it a bit, so if I misquote now, please forgive me. (1:00:26)

Adam: Essentially it’s “Timebox everything into two weeks.” I do this with my teams and it's great. If I say to you, “How long is it going to take you to build me a reinforcement learner for that thing?” You would say “What? I don't know. Infinite? If you want a rubbish answer – 30 seconds. If you want a really good answer – six years. I don't know.” Instead of that – which is where we all get stuck and say we want the freedom to explore and we don't for how long – you just say “You've got two weeks. Build me something that's better than what's currently in play.” That becomes a really good way to discretize what you're doing and turn it into something you can measure. Then when you're in that mind frame, it lines up much more cleanly with the traditional agile stuff. You're not going to get user stories and things like that. It's more like running spikes all the time. But it will allow you to integrate your data teams more readily with those kinds of agile management practices and things like that. I find that works really, really well.

Adam: Then everyone can trust it. Your product manager, for example, can know that if they want an improvement on the sales model, then the cost is two weeks. But there's no guaranteed outcome from those two weeks. You set the guaranteed outcome as a report on what was tried and how it performed. There's no guaranteed improvement. But your product owner, who's running the team and is responsible for deadlines, knows that it's their budget. They say “Right, two weeks on that, two weeks on that,” and then they can reprioritize on other things. That's quite a good way of doing it. But it really depends on the team. If you're in a team of software engineers that have learned data then it’s really, really good. If you're in a team with lots of data professionals that hate software engineering, then…

Conclusion

Alexey: Anything else you want to add before we wrap up? (1:03:15)

Adam: It's been great talking to you. Sorry for rambling all over the place. I find this stuff really exciting and interesting. If anyone does want to talk about it in more detail, please reach out. If you caught the restart, I've just had my first child, so I'm useless. It came back to me in the last couple of weeks. But I'll hopefully catch up. Yeah, catch me on LinkedIn, Twitter, or wherever. I'd love to hear what other people are doing with KPIs. I'd love to hear anyone that thinks I'm speaking rubbish. If anyone really disagrees with what I've said, I'd love to hear it, because that's the only way I think we learn, is when we get challenged on stuff. So please, if you think I'm speaking rubbish – they're the best conversations for me to have, so I'd love to hear it. (1:03:20)

Alexey: Okay. Thanks a lot. Thanks for joining us and finding time to actually talk to us. I know it's not easy for you. So thanks a lot. And thanks to everyone for being active, for asking questions. Sorry that we didn't cover everything. But I hope it was useful for you. Have a great weekend, everyone. (1:04:00)

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