Business Experiments with Sarah Spoto and Vincent Ducret

The browser company mozilla reportedly spent four hundred million dollars on a new phone operating system these are decisions made on instinct that failed. So how does a corporate explorer replace or at least supplement

instincts and opinions with data and evidence to demonstrate whether an idea is worth pursuing , or if it requires a pivot or even a shutdown? How does a corporate explorer answer questions that have a major potential to jeopardize the projects progress and ultimately it's future success if not addressed up front. Big questions to get through on today's episode of the corporate explorer.

Just a reminder that the corporate explorer series is brought to you by Wazoku Was oku helps large organizations create effective sustainable innovation ecosystems that accelerate efficiency gains and new value growth and it does this through intelligence. Enterprise software that connects and harnesses the power of employees, suppliers, startups, universities, and the unique Wazoku crowd of over 700, 000 plus global problem solvers.

Wazoku calls this connected collective intelligence, and you can find our friends at Wazoku at www. wazoku. com. So today's episode describes how incubation tests on proven assumptions of an idea for a new business by de risking each element before committing

scarce resource to the project. Is a great pleasure to welcome the coauthors of the corporate explorer feel book, i'm a chapter entitled business experiments the risking execution spend through experiments, sarah spoto and vincent Ducret welcome to the show

Thanks so much for having me

And it was really an incredible opportunity for me. I had been working at the headquarters in Detroit for GM for a number of years. And to go to a region to really build a startup within this large multinational corporation was honestly a dream come true for me. I consider myself an entrepreneur. But of course, being in corporate sometimes you don't always get those opportunities to do something truly, entrepreneurial. And in this case, I did.

And so I'm really proud to say that we had leadership who very much supported our efforts in taking an experimentation approach to how we developed the offerings for this new business model and doing it in conjunction with more traditional methods like market research. Right? And I think those complement each other very well. So my role there, I was actually employee number 4 on the team. So, while I have this fancy sounding title, I wear a lot of hats.

And it was really helping to drive innovation an agile methodology and bring this experimentation approach to the business while at the same time also building our brand there as well.

early to all those extreme programming, agile way of thinking, of working. And I work several years for Sun Microsystems. Probably some of you might remember this company. And then I moved to the other side of the mirror, I used to say, because I spent 15 years in corporate environment. So when I joined SARA on this project, I was used, I will say, to the corporate world. So I know what might be the challenge. What might be, I will say the problems they may face.

So I spent 15 years in corporate environments, having worked for different departments and the last six, seven years, I was part of the transformation organization, moving a very famous company from business centric to Customer centric. So really adopting a much more customer centric approach for this company. And I have also the opportunity today to teach business transformation and innovation principles in different university in St Gallens or in Geneva.

And I joined ChangeLogic two years and a half ago as as a consultant to help company to grow beyond their core business. So really more or less, I will say helping company like Sarah was working for to explore, test and scale new venture and new business. And this is how I was, I will say working with with Sarah on this very interesting and passionate project for, GMPI in China.

And I'll show that on the screen in a moment, but just to tee you up you say the first impulse for many corporates is to trust their employees, experienced knowledge and skills to address uncertainties. They have earned this trust by delivering high performance results in the past from the existing businesses for many years.

However, the reality is. That rarely an idea survives its first contact with a customer.

The uncertainties of an emerging business make your first ideas susceptible to bias, meaning that there is a strong correlation between the trust approach and the failure of corporate Innovation and then you outline steps to perform iterative, experimentation from assumption identification through experiment design and execution to data driven decision making, and you use an approach that Sarah used in China with General Motors china premium import and we'll talk about that

in a moment but first i'd love if you'd introduce us and i'll show on the screen this great diagram

Explotations, you have probably decades of success where you have fine tuned everything to work perfectly. But usually you can not use this experience, this knowledge to explore new world. And this is why you need to adopt this de risking approach that we have illustrated through this loop called business experiments. And for this, you usually start by stating all the key assumptions hypothesis you are making about your new ventures.

Those might be related to the customers you think you will deliver a value proposition. It might be related to the problem you think you are addressing, as well as to the value proposition itself. And maybe the ecosystems you think you need to put in place to deliver the value proposition. So you start first by listing, establishing all those key hypotheses you are making, usually you can say, what must be true for my idea to work? Okay?

Because if this is not true, I might be, I would say, doing the wrong stuff for the wrong people. Okay. So once you have done this one, you can move to a step two, which is about prioritizing those assumptions, hypotheses, because you cannot address everything at the same time. Will you be able to de risk at 100 percent of business? No. Okay, if you do this in six years, you are still de risking, I will say, an idea.

But they are very burning risks that you have to tackle as soon as possible, because you know that if you are wrong, you might probably, I will say, to move back a few weeks, if not months in the past, to correct something. So this is why you need to prioritize your hypothesis and look, looking usually at what I call rats. Your riskiest assumption to test.

Okay. This is I would say the one that are the most dangerous for your project this is where usually sometimes you put the dust below the carpet expecting that the dust will never pop up again Don't do this. Okay. Look at those one Even if this one is putting in danger the project you are working on Then, once you have identified those riskiest assumptions to test, this is where the most interesting part starts for me, and probably the most creative one, is designing experiments.

How can you invalidate your assumptions by getting real data evidence from your target customers. And this is also applicable for B2C and B2B model from your partner as well. Okay. In the B2B models. And when I means this is where you need highly creative because most of the time You don't have anything tangible to show to your customers. You are still at a very early stage. So how can you test with your customers, typical willingness to pay, willingness to acquire your solutions, okay?

When this solution still does not exist. And when I mean you need to be creative, because you need to go beyond the traditional way of testing with your customers, which are asking them or making a survey, okay? Which is the easiest way, I will say, to get data from your customers, but maybe the easiest way as well to get buyer's data, okay? And to not get any proof of what you are really, I will say, doing, if it is right or wrong for your customers.

Once you have done your experiments, of course, you will collect learnings, okay? Aidan. And those learnings might be taken telling you that you are right or wrong. At the end, it doesn't matter. It just gives you, I would say, the data you need to take decisions. And based on this learning, then you can decide to go for the next iteration, for the next loop, where you will look maybe at the second riskiest assumptions you have to test.

Okay. And maybe sometimes you will have collected enough evidence after a few loops that you can move forward with your project. Okay. That for example, it's time for you now to start building really your first version of your product, like your MVP. Okay. To test it further with your customers. But this loop is to remind to the people that the most important in incubation is not about, I will say, prototyping is about de risking.

You need to de risk as much as possible to bring all the data, the confidence that you are doing the right thing for the right customer to address the right problems. So this is why as long as you don't have enough data evidence to tell you that you are right. You need to keep, I will say, looping.

Discipline to perhaps do it a second time it's really hard for a corporate explore cause you cause of this time you know you feel like the, sounds of time are slipping away all the time and i'd love to hear how you manage that. When you're talking back to HQ and say, look, I got to go through this process. Otherwise I'm going to be squandering money for the company or how did you position that

And get that buy in. So I think there's a few different tactics we use to accomplish this. I think making sure that you have strong leadership support is really key. Making sure that you have advocates in your organization who can support you and your different or innovative approach to doing business because it is very different, right? And the already the business model that you're working on, right? Is going to be different in itself.

And then you add a layer of, okay, our methodology is totally different as well. So make sure that you have the right voices in the right forums

to support what you're doing, which I'm saying it very simply, but it's not simple. Right? So we actually took a very strategic approach to thinking about who we needed to bring on board along with us. And thankfully, we were able to do that within our organization and the broader organization. But that's just 1 level, because the experiments are going to get done, by your working level team. And in a lot of cases, your team is really lean. You might be under resource.

Everybody is doing a lot of things outside of their typical scope and that can be hard to motivate folks to say. Okay, we're going to take an approach that you are not familiar with at all, outside of your normal work. But we think it's the right thing to do. So there's also, I think the buy in that has to happen at the working level as well, like the cultural piece of it and giving folks that that psychological safety to see failure as part of this process.

And that, takes a lot of effort and I really I think that's something I definitely want to emphasize. And this podcast in particular, because maybe you read it in a book and okay, great, the framework, let's follow the framework. But there's that other element of that cultural piece that's so important and making sure as a leader, as a corporate explorer in your own organization, that you're taking the time to get by and with your working level team as well.

So spend and dedicate that time to educate them on the process. They're the ones that are going to make it happen bring them along for it, make sure they understand the impact that they're having the power of the frameworks that you are using and then make sure also that you're celebrating the failures as well. And I remember the first moment a result of one of our experiments.

Came back working with Vincent and team and it was a failed experiment and it was a very celebratory moment for us because we got information. It was like, Oh, this is not what we thought. And that's great. It was actually clear, really clear, right. The experiment allowed us to have that clarity even in our failure, and it allowed us to pivot. So making sure that you're taking time to pay attention to the cultural aspect as well.

Okay. Because usually when you start experimenting, people have the feeling that you are slowing down everything. You say, Oh, but why are we experimenting? Let's just build it. Okay. And I say, yeah, that's fine. Because if you are measured just on delivering something, perfect, build it, deliver it. But then if nobody's using it and you are also measuring about the customer adoptions, you might be in trouble.

Okay. So that's why, yes, you might have to invest a little bit of time at the beginning to start understanding how to experiment. But as soon as you start doing this one, you are saving a lots of time, money, and resources that you can use on stuff which are demonstrating real progress, real data, and evidence.

Okay. But this is hard at the beginning for your leaders to understand this one because they have been so used to go to some leadership meetings where you just show the I'm sorry to be brutal, but the way you are burning the money you got for your project, here you are much more adopting a small step approach where you show progress and you are not measuring the progress in the same way that you are measuring an exploitation project, because here you have lots of unknown.

So you have to also measure differently. I will say your progress, but showing the way you are wasting, you are saving money. It's a nice way for some people to get it very quickly.

That's actually a huge win that's a very tough chasm cross for corporate explorers and i've been guilty of this as have so many of our audience where we're going to go if i build something even if it's not successful at least i've built it and i can point to it. But that's not what you want to point to. So there's a lot in there, but I wanted to bring it back a step. So you, so Sarah, I'm just thinking about you as a template for many corporate explorers. So been given this huge opportunity.

The first step is then you go, okay, I need to map this out. You had an existing relationship with change logic vans on becomes your account manager and then do you guys map it together and you can go this is how we're gonna approach it.

And then you go for it because there's a diagram that i have from the book that i thought it looks like this was the next step then i just want to help our audience go because there's lots of little bits in between that we skip when we have these conversations but i really wanna help other corporate explorers so they get it. So maybe i'll share on the screen. This diagram and maybe you'll talk to us cause this maps to very much to the framework and this is how you actually, stepped into china

Was why we were successful with implementing experimentation, because even before we knew who change logic was, we are the managing director and the rest of the leadership team, we spent a lot of time. Being really clear on what our objectives were as an organization, and I think what helped us be more open to accepting this process of experimentation is that our objectives weren't as more typically focused on get X, Y, Z done, but really more focused on the learnings we wanted to generate.

and so I, to simplify it, it was really making sure that we. Had organized the team around the right incentives that then made it possible to even explore something like what we did with change logic and with Vincent. And so we didn't explicitly have that relationship or that process in mind. I was not familiar with experimentation. At all not not since taking science classes in school, right?

I was very familiar with, like, at a broad, a broad level, obviously, the importance of getting data and we're very data centric business and the importance of market research and getting in front of your consumers and that kind of stuff, but not experimentation explicitly, but I think taking the time to build the right incentives and the right vision early on, then let us down this path of Thinking about how can we really be successful and it became clear that de risking was really

important and that we needed or that as a leadership team, we wanted to look beyond the typical approaches to doing that. And that's where change logic became involved. One of our leadership team members, he had a relationship with change logic. And so we brought them in. But honestly, it was, Midstream. And so that's why I think I mentioned that cultural piece, because even by the time that Vincent came involved, the team was growing really quickly.

And we had people running we had experts doing sales, right? We had experts doing marketing and they were running and down the path of the tasks that they needed to get done. And so we introduced this midstream and had to really get everybody. At that working level on board with us with experimentation. So that's kind of how how it happened.

Truthfully, it took it did take some time for us to to educate the team to get them on board to build the right group of folks who are going to focus on the experimentation. We wanted it to sit very cross functionally. We didn't want this to be siloed with market research experts or marketing experts or commercial experts. We wanted everybody from, or at least a representative from every discipline as a part of the process. So obviously that requires some education as well.

But once we had lined up those right teams, then we were able to work. Effectively with Vincent and team to start generating experiments, it took it always takes a bit to get the 1st few off the ground. Lots of questions and trial and error. And then I think once we did that, and we got some in market, then we were able to build momentum and really start to get the data and results and to Vincent's points earlier demonstrate to our leadership the value of this process.

As a compliment to the traditional methods we were also using to de risk our business. So tell us about, , so , the first experiment, say, for example, or maybe it was the one that , you proved was wrong, the wrong approach. So , what actually happens and maybe we'll use this diagram on the screen to, to map that steps, those steps that you took. The first piece was really the assumptions analysis. And I think this is so critical. It's important not to skip this piece of it.

And honestly, even as a corporate explorer, if you are not fully on board with experimentation as a process, just start with the assumptions analysis, because that alone is going to reveal so much about your biases as an organization. And. Help frame your thinking from the very beginning. It's still a tactic that I use even in my own work as a marketing professional. Even if I don't go into experimentation.

But anyway, so we started with the assumptions analysis and really saying what must be true for this business to work. And then, of course, applied that to different offerings that we were exploring. And that was not a short process necessarily because again, it was cross functional. We wanted to have input from the right teams. We wanted to be robust enough from there. We prioritized the assumptions based on some assumptions. We could really quickly.

Answer either with existing data or data that already existed publicly third party data or results from recent market research or things like that or some were just simply questions we could get answered within the organization and then some were true. Unknowns and that's where we focused the experimentation. Even building this framework during the process was What happened right so we had what on the screen here is this nice neat framework, but it looked different.

The first time we were using it involved to, to fit what I think you make it most effective for our use but yeah, so then we were able to use it to define the experiments,

In that case, we really use like community in China to test, I will say people's behaviors and figuring out without, I will say, asking them if they will rather opt for solution a rather than solution B. But they were not knowing that we were really testing them it was made in a way that it was natural for them to get, I will say this offer in front of them and select a or B, but we were here testing really customer behaviors and this is where experimentation is very challenging for most of the

corporate is how can I cross the border between people saying and people doing. And this is a good examples where we were testing this idea about vehicle subscription services to see if the model we had in mind where we have multi branded. Would get preference to a model where you have only cars for the same from the same brand Okay and again You can have just I will say question a few people around and say do you prefer to rent always car from this brand?

Or you would like to have a portfolio of brands and maybe they will have tell you yes But the problem is that they tell you yes and the day where the offer exists they don't use it. Okay. Because what they say is not what they do. So here we found a way using community that was existing in China to really test their behaviors and to see to which level they are ready to even subscribe for service, which was not yet existing at this stage.

Okay. So this framework is helping you structuring your assumptions and turning it into something that you can test, measure, and you can take decision and decision may be Just accept that you are wrong and that's okay. That's a learning. You are not failing You are just learning that you are right or you are wrong or you have enough data telling you that you are right , Aidan McCullen: Sarah did you want to add anything

But eventually we were able to pivot into experiments in in real life. And I think that's really important because it's so easy to stay within your corporate environment, say, within your your building and something that change logic really challenge us to do is to get out of the building. To get in front of customers, because a secondary benefit to doing these experiments is you give your team exposure to customers in a way that they might not typically get in their normal roles.

And I think those learnings go even beyond the results of the experiment as well.

And before I finish in four, I thank our guests. I want to thank our sponsor Wazoku who helps large organizations create effective, sustainable innovation ecosystems that accelerate efficiency gains and new value growth. And does so through intelligent enterprise software that connects and harnesses the powers of employees, suppliers, startups, universities, and the unique Wazoku crowd of problem solvers of 700, 000 plus what Wazoku calls connected collective intelligence.

And you can find Wazoku at www. Wazoku. com for now authors of. The corporate explorer field book and sarah spoto thank you for joining us