what Welcome to an telecast power by skip. Welcome to another episode of skip and telecast, a podcast about strategy, Intelligence and leadership. I'm cam Mackey was skip and today we'll be talking with Chris Mulligan of Mackenzie as a partner in Mckinsey's new york office chris co leads the firm's strategy and corporate finance practices, center of competence for financial and capital market analysis. He's also the leader of two really cool digital capabilities, mind spans and mind lens, which are both data and modeling tools used to improve decision confidence. Chris Welcome to a telecast. Thanks cam bushing party today. Yeah, thank you. So so you know, there's your brief bio so for those folks out there, of course everyone's aware of Mackenzie maybe just, you know, talk a little bit more about some of the work that you do and and uh you know how companies actually can use things like mind span mind lens and others. Uh well thanks for the ability to give a little bit of uh insight, uh how we think about things.

My role at the firm as a partner is to bring actual insights with impact to clients and uh and the world is changing. Right? So uh the job is getting much much more interesting and I'd say there's two kind of profound trends that are driving the ability to to do to drive insights with impact. The first is there's just a lot more data available. Uh and so there's a whole host of opportunities around the whole host of problems that comes with that like big data is uh is equally a boon as it is a curse and we can talk about that a little bit more. The second thing is there's just a ton more uh analytics that are available and we'll talk about some of those I think uh here in a little bit but analytics uh the interesting part to me at least, and what I see is that the analytics are moving from being quantitative analytics, the analytics that actually can manipulate qualitative sets in terms.

And that's really interesting because we we all operate mostly in qualitative terms uh in our day to day as opposed to quantity. Yeah, that's great. Maybe you know for those folks who are not data geeks, you know, talk a little bit more about that concept of as you say, analytics moving from quantitative ones towards one that can can have more kind of qualitative impact. Yeah, sure. So uh so I you know like when you think of quantitative analysis you often think of kind of Wall Street or or maybe engineering or something like that. And that's that's because there's just a lot of quantitative data and there has been for decades uh in those practices that allow you to uh you know kind of do analysis to get a good decision. What's really interesting are some of the machine learning natural language processing and even artificial intelligence tools, so Ml NLP. And ai if you see those uh those are really adept or getting better at being depth at manipulating qualitative terms uh to drive better decision making.

So like this shift from the kind of quantitative focus of analytics to the qualitative focus of analytics really is like from what we see is driving these now. New insights. Yeah, now and that's great and I'm going to pick up on something you said a moment prior to that chris the big data is a boon but also occurs. So so you've talked a little bit about the about the promise and the ability to make decisions smarter and sharper with greater confidence. What are some of the downsides of this proliferation of data? Well it's big, you know, we're ready. Oftentimes oftentimes it feels a little bit like you're looking for a needle in a haystack, right? Uh and we talk a lot about signal to noise ratio. Also there's a lot of called data cleaning or data curation that needs to happen before you even get to the starting line of analysis and the more data you have often more challenging those tasks are, yep, that's good.

Now, one thing that most of our listeners who haven't read chris was a co author on Absolutely dynamite piece. We'll share the link in the show notes called strategy analytics revolution. And as you noted in that in that paper, chris some functions, you know like finance or operations. They've they've made a decent amount of progress using advanced analytics, you know, NLP ai ml et cetera um to to drive efficiencies but also create value. Um But as he wrote the article, the strategy function itself is a laggard here. Why do you think that is? Well, I think the easiest way to ask that cam is that uh strategy uh in many um places is viewed as more art than science? Uh And and and maybe it is but there's definitely some signs you can bring to it and increasingly with tools that allow you to parse qualitative uh sets of information, um you can apply a lot more analytical rigor. And so our purpose in writing the article was just say hey you know this is some of the interesting stuff that we see happening uh in application of analytics to strategy and we're driving some pretty decent insights or or or or you know incremental insights that are really creating value.

Yeah any examples of that just you know broadly speaking, uh that you've seen where the insights are helping to increase decision confidence around strategy, helping identify new growth trends at any examples there? A ton actually. So um one of the things is um kind of reducing bias in creating strategic plans. It's really easy to have that kind of inside in view. Um and and not even know it because you don't have a good sense of what good looks like in a broader context. And so if you can think about almost as a strategic benchmarking to say well you know, what do you have to believe get to these certain outcomes that we want to achieve and how often does that actually happen for other folks. Uh And so you can start thinking about, okay, where do you want to set the dial on organic growth in organic growth or what is your expectation for uh margin or margin improvement? And that's great. But then being able to benchmark a bit and saying well hold on how many people actually achieve that type of performance can be very, very helpful in terms of an iterative cycle of creating a new strategic what that's really neat.

So so you know, put another way, so you're talking about applying, you know benchmarking is a discipline. So not just trying to reduce the number of of you know turns in the factory trying to reduce inventory levels actually using benchmarking um to pressure test the strategic plan and you know, some of this. Yeah, so that's so you can do this with with some of these, you know, some of the techniques, you're you're you described in the article then. Yeah. And then and then when you double click on that and a lot of people when they think strategy which has a lot of different components to it, they start thinking about growth right away is one of the big ones and that's a fair thing to do when you think about growth um trying to assess, what are the incremental growth opportunities, organic growth opportunities in organic growth opportunities. Uh The adjacent growth opportunities like there's a whole host of flavors of growth that you can engage in. And normally when we talk about growth and maybe frustratingly sometimes we talk about in qualitative terms.

Uh We rarely talk about growth in quantitative terms except for the growth that we've actually achieved only aspire to achieve just in terms of a flat growth rate of revenue. Um So uh talking about what fields do you want to expand into, which areas are contiguous to your current operations? Um It's we've been really um uh kind of um um surprised by how much we can drive the way people talk about their businesses to understand competitive market maps to understand adjacent seas uh and to understand potential opportunities uh for our clients. And so that to us has been uh a more interesting area than we thought it was going to be just using simple company descriptions and the way people talk about themselves on their websites and the way they are and and the way they talk about themselves and their public filings and press releases, you can actually use that as the substrate uh to drive really interesting perspectives on market structure.

So, so that's really interesting. And so and so as you're talking about this, you're really, it sounds like, you know almost having an ability to pressure test your various strategic choices or strategic options now um is that, you know, where where are these strategic choices and the hit rate of them tractors that mckinsey database you have? Well, uh, we do a lot of stuff bespoke for clients. Hey, I uh I want to expand into logical, contiguous areas that leverage Mikey friends. Um what might those areas like? Um, and, and, and, and, and and what is the performance in those areas? Really good question to ask another thing people might say, hey, I do want to acquire some capabilities or some operations. Um where would be the, you know, kind of best place to do that. I I have a sense of a map of where I might go both upstream downstream or you know, kind of in terms of continuous product services, where can I, where can I really take that developing a map of uh kind of contiguous areas for expansion and the relative attractiveness of them.

Again, based on some of that benchmarking boy can go a long way to center a dialogue and take it from being much more emotional or artistic to being like really rational like saying, hey, this is, this looks like a really interesting sector for you to expand into. But boy, it's a long way away from your core business and there's other places that may be similarly attractive but much nearer and so therefore maybe more execute able. Yeah, I love that because you're looking at the potential reward the market opportunity and, and but you're also as you've talked a moment ago chris you're looking at at the risk and and as as we think about where are these big, small or big strategic decisions made, its ceo cso board level and and the more that you can start to understand and maybe even quantify risk, that's priceless. I gotta believe that that you've seen clients and others embrace this kind of approach just because it helps you have a richer dialogue around the risk of, you know, the quote unquote wrong choice or or or the safe choice.

Well, it's interesting. Freeman is risk. Um it's more about for us, like great teams can do great things, but it's more about what do you have to believe to get to where you want to be right, You're stacking up a lot of low probability outcomes like even the best team like that, that's a challenge. And if you're stacking up a bunch of high probability outcomes, boy, it gives you a lot more confidence to to kind of make that investment or or chart that direction that you want to proceed in. And so I think that's that's helpful to many of our class. The other thing I'd say is helpful about strategy and Alex is again, when you're in the qualitative realm of data, um you can do some interesting things about picking up in the emerging trends. Uh and so I kind of think of it as a trip wire to understand, um, you know, kind of new competitors or folks who maybe who might have in the past blindsided you like, you can, you can really do a broader sense of monitoring for emerging trends that may affect your core business or places you want to expand to do in a way you couldn't before.

And so there are some of the kind of the automation uh, and and scope uh, analysis that enabled by ai and machine learning, uh, it allows you to have more awareness and more early warning of some of those things than before. Now, I'll tell you from hard won experience setting up the triggers for some of that stuff can be really hard, but once you, once you set that up, it can be fantastic in terms of making aware of things that are not just, you know, kind of a couple of months away, but a couple of years away from, from, from being on the horizon. So that kind of additional, um, um, uh, focus your distance to the horizon, where new value might be created or, or new competitors may come from is really, uh, like many companies find that to be very, very helpful. Yeah, and that I think really resonates with a lot of our listeners who are in the competitive strategy and intelligence fields and and one of the things that they're keenly interested in is weak signals, right?

And um, as as there, as you said early on, you know, big data being a boon and a curse. That's uh, it is critical to pay attention to those weak signals because they can be market threats and also market opportunities. So it's not a weak signal. It's an early signal. Okay. Okay. Okay. Good nuance it the a little bit because um, you know, you uh, we, we find ourselves tracking things through, um, the way faculty talk about where their resources focused or then a little further kind of down that early view where patents are getting filed and what patents like other pets in terms of getting a sense of kind of the importance and kind of how they're networked together and who and where is doing that stuff and what type of publications, what gets picked up in social media and what VCS are investing in what they say they're investing in. Like, like there's, there's lots of places you can pick up these early signals and, and start to and once you start seeing some of them, uh, see whether you, you see the next step in that chain emerged, which gives you a little bit more awareness that it might be an important No.

Yeah. So, so you know, this, this kind of begs a broader question chris so, so, you know, I think everyone would agree that, you know, the days of having strategic planning be a calendar driven once a year process that results in a big binder. I mean, you know, I think folks get that, that ain't the way you do it anymore, but but you know, what impact do these tools have on strategy development? Um you know, sensing uh you know, et cetera because it sounds like it really needs to be a continuous process with, you know, continuous benchmarks you're looking against. Well, you know, cam, I'm gonna take in a slightly different direction and make it even, you know, kind of the task even more complex or, or give a vision of a more complex tasks, not only do the timeframes for planning change and it gets to be more of a kind of near real time process that has to be responsive to things that are happening in the competitive landscape. Uh but it also is going from a view of a single view of the future for many of our clients Too much more of a multiple scenario analysis and and and really the last 18 months has driven that home.

Uh in terms of a lot of our clients, uh no one would have predicted some of the things, some of the kind of raw inputs to most people's models That have occurred over the past 18 months. If you're sitting back in 2019 you just wouldn't uh for for a bunch of good reasons. Uh so uh the models have come have changed between that kind of as you as you put it. Uh you know, kind of quaintly one big binder with what we expect the next year to be uh to kind of a set of models uh that tell that in which you put your best inputs and what you have to believe and the sensitivity around those things to say, hey, where's the envelope that we get to? That that makes sense in terms of how we want to talk to our investors and stakeholders and what we want to agree that we're going to do this year. Uh and uh and and and if those things change, let's change those and restart the model to say whether, you know, kind of how high is the hill that we're climbing and and should we perhaps changed and pivot direction here based on what we're seeing so much more understanding that the future is not static, The future is actually better viewed as a complex uh set of impact scenarios which drive from a couple of key fundamental assumptions.

And so the only thing I'd say is uh you know, that sounds really complex. Now, you're gonna come up in the complexity uh kind of reading both in terms of time frame and in terms of scenarios, the one thing that does make it a little better as we do our work. Uh we we've come to understand or we believe that there's there's really not that many levers or big assumptions that you need to make, uh that really drive the outcomes, you need to make some assumptions about what you're gonna do for your inorganic growth or or or you know kind of emanates um you need to make some assumptions about where you're going to reallocate capital among, you know, kind of the different investment opportunities you have within the firm. Um you need to understand where you're gonna make some big investments, you need to have an understanding about how you're going to drive productivity and uh and your competitive differentiation, differentiation, those things resolved down to like not thousands of choices, not hundreds but a couple of dozen choices.

And so in this new kind of more complex um you know kind of strategy in stan she ation world, you know like at least we, I feel paired down branches tree to a manageable amount that allow you that allow us to then give decent advice about what do you actually do because you know, strategy is really nice to say, well what might happen, but this has to come down to the conversation about, okay, what can you actually do to get to the outcomes you want? Yeah, exactly. Yeah and I like that because I think the it's easy to get overwhelmed with the thousands or millions of various combinations of choices you can make and I think it's a great point to remember there are only so many levers and you start to think about this from the perspective of probability, right? You need to focus and make your choices and investment decisions on the highest probability, um, you know, outcomes. So, so, you know, here in the, you know, strategy and intelligence world, we have these tried and true frameworks and you mentioned scenarios and you know, see I folks are actively involved in things like scenario planning, war gaming, you know, using Porter's five forces.

What are your thoughts on on techniques and models like those in this highly dynamic, complex world? Are they still relevant or are they relics of the past? Well, I like that. I think they're all relevant. Uh and I think there's a lot of truth to them. Uh, and, and there's a lot of usability in them. It's just now that we have, uh, kind of April mental layer of tools that you can do to take it a couple of levels deeper. Right? So, you know, I got a 45 forces makes a little sense of the world, but you can now go a lot deeper and actually have a fact based discussion about a lot of the branches on those trees uh, than you could before before you're saying, well, you know, we, we, you know, kind of, we feel that maybe, you know, we're gonna have more competition. Uh well, uh, now you can actually say, hey, we're seeing a bunch of trends out there that look like there's gonna be a bunch of patents filed, which may make our position, uh more competitive.

So you can just bring more facts to the framework and I think that's helpful but but the framework still hold, it's just that now we can have a more basic conversation around which which which is great and I want to want to take it back to a point you made a few moments ago about this notion of bias and decision making and and and man I I tell you I haven't seen it at companies. Sometimes it's the most senior person in the room that doesn't want to let go of that one brand who you know that really should be Sunset ID or you know that they think, you know this is the right price for the acquisition. So you know, I really uh you know, we've all seen in our careers examples of of bias and decision making, we did some pretty bad results. Uh obviously chris this isn't as simple as walking into a boardroom with a lot of you know, entrenched perspectives. You show them, you know the data or the analysis and they're magically converted to new ways of thinking. Any any thoughts on how executive teams can make that transition towards more data informed decisions rather than kind of traditional gut feeling and bias.

Well I think part of it is driving awareness that you actually have some facts that you can talk about. Uh we have Mackenzie need to take your own medicine sometimes too. Yeah, we suffer the same biases as any firm where sometimes we let our passions dr decisions as opposed to the facts on the ground. So uh just uh we're trying to make a decision and nobody really brought facts to it. And so I think it's fair. Uh that is part of the discipline of decision making, which is say, hey, like are there any facts here that we could uh we'll just and sometimes the answer is no in which case, you know that that's why that's why certain managers and leaders are better than others. They have a good time. But in many cases where there weren't facts before there are. Uh and so what we're trying this article to tease out is um you know, there are a lot more facts now than you might think there are.

And some of those facts are based on qualitative, not quantitative information, but that can help you get to a better outcome. Yeah. And I think for those who haven't read the article definitely recommend that you do. But you know, one of the really excellent points that you make at least implicitly, is that not not every problem as a data problem. And that that you know, they're of course still is a role for intuition and human judgment, but it's as you just articulated a moment ago, it's it's you know, before we make a decision, let's let's see what facts and data points we can obtain because chances are there is something that can help shape our thinking exactly. And and our class especially found around helping um uh folks understand what good looks like. Uh that's been a big deal, helping people understand the attractiveness of uh future growth opportunities on a relative basis has been really good. Uh watching early trends has been good. And then being able to go from a kind of call it more static uh strictly planning process to more of a scenario one, like all those things that helped a bunch.

It is really helpful, especially when you tie it all the way back around saying, okay what do I have to believe to get to the outcomes that we want to get to? And then you say, well how often do people actually do that boy? That type of closed loop thing is tremendously powerful in terms of getting to a good view of what you want to do with your strategy? Yeah, I mean and and I got to say for me that was one of the eye opening things is that as we think about, you know, different strategy and strategic choices for our business, you know, the fact remains that as much as every market is different, every business is different. That you know, let's be honest, you know better than anyone chris you know, people have been in that chair before and they've been faced with, okay, great. Do I go to an adjacency um you know, do I invest in in buying a competitor just to increase my market share? I mean, these are these are pretty much universal decisions. And so what I think is really powerful about what you're saying is let's understand what the hit or win rate is for those decisions. And that lets you make the choice for your company with your eyes much more wide open rather than, oh, this sounds good.

I mean, I gotta believe that's really getting great traction. I'll give you an example camp uh M. And A. Is a risky thing to do. Just uh we did a bunch of work and my colleagues work uh to see kind of what works and what doesn't Uh and what we found is um kind of pretty much over the last 20 years, there's one style of M. And a. That that produces excess returns uh ahead of all others. And what is what we call programmatic and it's that you do a bunch of it, you do it consistently. You have to process and it works uh you know, uh just taking that type of analysis and thing. Okay. Like you can always do M. And A. And and often it works. But if you want to, if you want to do it consistently and and play the odds in your favor. Well that's the style you choose. Yeah, that's, you know, obviously I won't name names, but in in my prior role working with manufacturing companies, we had a few members who were diversified industrials and, and they followed that playbook, you know, they, they, it was very programmatic.

They would do lots of little bolt on acquisitions every few years. They do something big. But I mean they just, they got it as a process that they were awesome integration. And I think kind of, you know, the research you're referencing is the attention grabber, you know, the big, you know, double the company acquisition. Those often often fail well, sometimes they work really well, but sometimes they don't know, knowing the odds of that versus other styles of a, uh, can be helpful when you're, when you're being tasked to make those decisions. Uh, you know, and also say from, from working with a bunch of clients on this particular topic. Um, you know, there's something different in terms of a proactive process on the part of the organization for the react, uh, that proactive discipline does tend to bring the rewards. Yeah. And, and that's, I mean, I think it's a good reminder that that this whole concept of intelligence and data feeding strategy that, that it can't be episodic or again, you know, the binder concept that we're even hearing some of our members use the term continuous intelligence.

Just just to underscore the fact that you always got to be at the very least listening doesn't mean that you threw out the plan every day of course. But you need to have those mechanisms for the, for the early warning. Um, yeah, it's, it's, it's important. Yeah. Just stepping back a bunch of the models that we use here internally. Um we, we update, you know, uh, with some frequency usually less than a year. Unless there's, unless there is something that changes our view and we actually have a, most models have kind of a list of, hey, if one of these things happens, we gotta go back to remind. And so just like having that discipline at the outset to understand what your boundary conditions are for the pace of your remodeling is. Uh, we found internally. And so I'm guessing the folks that we did the kind of the remodeling work had a very busy 2020, right? Probably lots of trip wires got triggered. I can tell you we did a ton of scenario analysis, right? Uh and also, uh, I was here in Kandor.

Um, we really didn't think we'd wind up where we are now, which is a relatively, you know, kind of productive place. Uh, we never would have guessed this year ago really, really, this was on like when you look at those scenarios, this was definitely one of the tail outcomes. Uh, you guys want alone. Yeah, you weren't alone in that. And so being able to be responsive with decent frequency allowed us, I think to take advantage of the opportunity that came up that we just didn't expect to come up. Yeah and that and that I mean that's a fantastic lesson learned I think kind of reflective of our conversation. It's it's um it's trying to improve the number of times you make the quote unquote right or optimal strategic decision. But it's just as important as having that listening and the agility that when something changes that you're able to capitalize on that opportunity. Yeah, agility is a good word for it. So so a couple more questions chris before we let you get back to doing the good work. Um you mentioned a few minutes ago this this notion of strategy kind of being you know more art than science and you know functions like innovation strategy.

You know there's still this baggage around those having you know being being driven by Eureka or lightning bolt moments. So so what are your thoughts on combining some of these more roll up the sleeves? Human practices like brainstorming sessions, ideation. How can you bring uh advanced analytics and similar tools along for the party rather than replacing similar traditional human methods towards capabilities like innovation and strategy. Okay, I'm going to dodge your question a little bit because I don't answer uh innovation. It really does still feel like art. Now you can apply certain processes which again different processes work in different cultures. Um uh get better outcomes but there's still a lot of variability around outcome. There just is and and it's something we're looking at for sure to see whether we can kind of get some better clarity around kind of science, uh, instead of the art, but I like I don't have a great answer.

Huh? But I mean that's important too. It's the no tool is one size fits all right. Whether it's making, you know, making decisions by gut sometimes that's all you have to go on. So that's a tool. But it's also I think one of the things you've articulated so well is that there are there's a much opportunity, a much greater opportunity to use, you know, facts and fact based decisions is a tool. So it's, you know, there's no silver bullet, I think is a good point that you make. Yeah, Well, that's my colleagues who focus on innovation. Uh, a bunch of them, uh, they would say it's more about running a structural well structured process and continuing to write, but that doesn't really answer your question about how can you increase the number of blue sky lightning bolts? Yeah, sure, sure. That's well, that's probably, you know, if if you and I could do that, we would be billionaires on the beach in Aruba or something. So, so, so last question for you. And it's something that I'm keenly interested in is that as we've talked through this complexity over the last, you know, 30 minutes or so chris, you know, you've you've talked about ways that we can can leverage technology plus, you know, plus, uh, you know, fantastic human judgment to identify opportunities to help us think through the risks and the hit rate of strategic decisions.

Now, you know, all this really, you know, leads us to question what is competition? You know, we have so many disruptive competitors coming who we never thought would be a competitor. So it's, it's, you know, the world's obviously not completely topsy turvy. But what's the implications for strategy and insights folks that the competition is far more dynamic? Um, where do you see this evolving over the next few years? Mm hmm. It's only a bunch of ways to answer that question I'll answer just along one dimension. It's just quicker camp, right? Like things can come up on you off the horizon a lot quicker than they could a decade ago. Uh, and so the agility and the, and the kind of forward looking that we discussed before by having some kind of, you know, kind of early trend identification just becomes more important. You know, the process of competition remains the same. I just think the pace of it is markedly. Yeah. And it's a, it's a good reminder that if you're going to be able to deliver value back your organization, it's number one, have the listening and sensing mechanisms number to have those processes in place.

As you say, kind of the trip wires on the modeling And number three, frankly be ready to be agile well and utility really is, it's not only um uh you know, kind of in terms of planning, it's also in terms of allocating resources. The firm absolutely. Uh the firms that are pure to do really well. One of the things they do um pretty consistently is radical reallocation of capital. Uh and so that's one of the hallmarks of kind of firms that outperform their competitors. But it's hard, as you said before, everybody has got their pet projects, everybody's habituated uh kind of what they like and so it becomes much more challenging to do some of this stuff because it does force you into this constant set of change. However, you know, again, the facts of it would say uh your odds are your odds of superior performance are better if you do it, yep. So I'm gonna borrow that phrase, Radical Reallocation of Capital.

That's uh that's a good one, chris Well, Alison, thanks so much for for talking with me today. Really really appreciate it. Um Again, you know, chris Mulligan is a partner with Mckinsey and he is an author of the fantastic article, the strategy analytics revolution chris thanks for joining us today, I appreciate you being so generous with your time Adam. Thanks for having me have a good day. Yeah