Entrepreneurs deal with high levels of uncertainty; probably more than any other business. The more disruptive the startup, the higher the level of uncertainty. Will the customers buy? How do we reach them? Will we be able to sell at a profit? Will our partners do their part? Who is out there that we can learn from? Are there threats that we are unaware of?
Not surprisingly, Eric Ries, the creator of the Lean Startup concept, describes learning as most important objective of a startup. Uncertainty can only be reduced through information; information coming from the experiences of prior entrepreneurs and from your own experiments. John Mullins, the LBS professor who made “Plan B” and “pivots” to be central concepts for entrepreneurs, talks about analogs and antilogs, these are other people’s experiences to learn from; either to copy from them (and adapt) or avoid.
Launching and growing a startup is about managing learning. A recurring effort to observe, share, debate, propose hypotheses, and test. This process benefits from having a management tool that structures these various aspects. The Landscape Monitor is designed to precisely fulfill these needs. Through it, startups accelerate their learning cycles and their growth; hence save time and money accelerating the process of transforming unverified hypothesis in real facts.
Market structure and business model
Entrepreneurs face two main types of uncertainty. One of them is uncertainty about market structure. For instance, you have a great technology for natural language processing (NLP) and you believe there is a market opportunity for it. A central question is knowing who the customer is. But this is not enough. You probably need to know which other actors are competing in this space already, what products are they offering, how they acquire costumers and how will they evolve. You also need to know about potential partners: technology partners giving complementary technologies or business partners that support business functions where you have no expertise. You also need to track how technology is evolving; in doing so you track universities and research centers working on NLP, other startups but also giants like Google or Facebook. Regulation might also be a relevant source of uncertainty as governments across the world try to catch up with the new possibilities that technology offers. Your successful startup needs more than just knowing your customers, but also how the market is structured.
The second type of uncertainty is how to design your business. In the entrepreneurship jargon, we call it the business model. The business model describes how your startup will create value. The questions begin with the value proposition; what does our idea bring to the market? How do customers benefit from it? The immediate next step is identifying the customer segment that will buy the product or service: to whom are we selling; who are our early adopters? And why? To further explore this question, we need to understand how much are they willing to pay and how; this is called the revenue model. For our NLP startup, we need to understand who will buy our product, to whom is our value proposition most adapted to? is it for marketing departments of large companies, for smaller companies or for researchers?
Once we have answered the what (the product that we are selling) and the who (who will buy it), the startup needs to answer the how. This question includes how will the product be delivered, what are the distribution channels (a critical question in the start- up competitive environment with limited resources available for customer acquisition), how will the product be produced, who are the main partners (including suppliers). The business model canvas, which is a broadly used business model framework, identifies within the how question the following aspects: partners, resources, and activities. Our NLP startup would need to decide whom to partner with and how to develop and customize the product.
Capturing information to deal with uncertainty
Often entrepreneurs believe that they have a great idea that nobody has ever had and they quickly run into developing the idea with little attention to dealing with uncertainty.
As experienced entrepreneurs and investors often say, the idea is a small part of the success of the company. So before jumping into whatever idea you have, it pays off to learn. As we said before, the starting point is knowing the structure of your market. Even before you start developing the business model and experimenting to shape your company, work on reducing market structure uncertainty: understand who are the main actors in the market that you intend to go after, what objectives do they have, which resources, what are they doing, where I can learn from.
Reducing business model uncertainty involves looking at other companies that will inspire the design of your business. Creativity is just relating ideas that already exist in new ways. So looking what other people have done before will certainly inspire you. Your experiments to build your business model will deliver a lot more learning if you incorporate the teaching from these other people and quickly test your innovative ideas. Knowing why and how other start-ups failed in trying to bring successfully to market similar value proposition is a competitive advantage for growth.
Leverage your founding team, advisors and friends who can provide new observations and insights about how the market works. The complexity of markets is such that one person cannot see and make sense of all the information out there. This is the wisdom of crowds. In your case, the crowd are those people that help you start your company. Diversity is important, it gives you a rich understanding of who is out there and how you can work with them. Think of it as crowdsourcing and crowd-analysis delivering crowd intelligence. Your team will source new pieces of information and help you analyze it to reduce uncertainty.
A typical mistake that entrepreneurs make is quickly closing down these exploration phase and turning their attention to building the company. In doing so, they often miss important information that they only find out later on when the costs of such oversights are large. This common mistake often generates higher costs of commercialization and, as a consequence drives up the CAC (Customer Acquisition Costs); a very important metric for venture capitalists. These windows to the outside world should be open at all times, more so early on in the startup phase, but never close them to avoid missing opportunities that can decide the fate of your company. People and companies out there do not stop coming up with new products and businesses when you decide to forge about them.
Information comes from your group of close advisors and colleagues, but not only. The web provides us with a rich source of information easy to tap. From it, you can learn about different views on your market and companies implementing similar businesses in other geographies. Together, private and public information offer your team a rich tapestry for being much more creative.
The concept of the Landscape Monitor is built on this management principle. Observations, experiments, and the analysis of their consequences by an informed group of people is the most effective way to build a startup. Simply, because it is the most effective way to create and learn. The Landscape Monitor structures observations around a shared view of the market structure and the business model, facilitates the debate, and synthesizes these efforts into insights. It is dynamic, so the startup never stops learning.
Analyzing the market structure
Reducing market structure’s uncertainty benefits from sharing a common view of it. It is easily done through a map of the actors that can influence the way you design and run your company. The core of the Landscape Monitor is a map. A common starting point is to organize it using the Landscape Canvas (Figure 1). Within each category, you can dig deeper and have a more detailed map.
The map helps with organizing the richness that exists in markets. Without a structure to observations, each person has a different perspective, which makes communication harder. The structure also gives meaning to observations and experiments. The combination of various observations creates arguments for insights such as new opportunities or competitive moves. These arguments are available to the rest of your team for refining, refuting or testing.
Our NLP startup would follow all those actors that can influence its success including competitors, but also partners or new technologies. The world is wide and large, somebody out there is doing something similar or has a complementary offering that can accelerate your learning. Now the team has its members’ eyes and the web to see the moves of these actors.
Developing the business model
The ideas emerge from combining observations. The richer these observations, the more likely to have a breakthrough idea. Observations might come from your effort to reduce the uncertainty around market structure; but also from specific efforts to tackle business model uncertainty.
Referents and substitutes are companies out there that inspire your own. Analogs are Referents from which you “steal” a feature; your final design might combine different features from different Referents. You can also “steal” what not to do! As Picasso said: “good artists copy but great artists steal.” Our NLP startup can get the inspiration for its pricing strategy from a supplier of statistical tools and its distribution strategy from a supplier of accounting software. Then, it experiments with these initial designs to decide how it will first approach the market. Substitutes are companies that offer or can offer an alternative to your customers.
Much like it happens with market structure, learning to reduce business model uncertainty benefits from having a shared map. The most common map of startup business models is the canvas. It identifies three different parts: economics, activities and customers. Economics includes revenues and costs and it captures whether the business model is sustainable. Activities identify key resources, key activities, and key partners. The customer part separates value proposition, customer relationships, channels and customer segments. As it happens with the map of the market structure, the original canvas can be adapted to the needs of the particular business and include other aspects such as pricing, competitors or complements. The same way that the Landscape Monitor maps the outside of the organization, it maps its business model. Because each startup is different, each map is unique.
For each of these components, team members share observations and insights to refine the design of the business model. Some of the observations might come from substitutes, others from experiencing similar services. These observations evolve into premises (or hypotheses) about the design of the business. Some of these premises will get refined through crowd-analysis (debate) before they deserve to be experimented in the real world. Experiments help the learning by confirming, discarding or suggesting changes to existing hypotheses.
The working of the Landscape Monitor
A startup will have two maps (which it can combine as in Figure 2), one of the outside to capture the market structure and another of the inside to reflect its business model. The starting point of the Landscape Monitor is those two maps. Of course, maps are dynamic and can change as the outside and the inside change.
Reducing uncertainty benefits from a diverse set of perspectives. Thus, the Landscape brings together founders, early employees, advisors and any other person that can add to the startup. A diverse group provides a diverse set of observations and a rich analysis of these observations to uncover opportunities.
The Landscape Monitor records the observations coming from the team but also from any web-based source relevant for the startup. For instance, our NLP startup benefits from the feed out of NLP research groups or technology companies. This external feed allows the team to know what is happening even if none of the team members experienced it directly.
Team members can vote and comment on observations. This aspect provides a first level of analysis to elaborate on observations and identify those that are more important. Face-to-face meetings become more productive when people have thought ahead of time about the signals in the landscape.
Finally, people in the team can suggest insights, ideas and opportunities that might require action. For instance, our NLP startup can see that various companies with similar value propositions are using a software as a service approach rather than the pricing model that the company considered thus far. This insight can lead to action through an experiment to test whether this new pricing alternative is more effective.
It is also a productive tool to better understand and manage the competitive landscape facing startups and communicate more effectively to board members the challenges ahead in order to align strategic decisions and day-to-day activities.
Entrepreneurship thought leaders all agree that building a company is about learning. Eric Ries defines a startup as an institution designed to create a product or service under extreme uncertainty. Steve Blank, the person behind the concept of customer discovery, defines it as a temporary organization searching for a business model. Entrepreneurship is about learning amid high levels of uncertainty.
Learning benefits from observing from different points of view and debating. You can refer to these activities as crowdsourcing and crowd-analyzing. Creativity is combining exiting concepts in new ways, the richer the observations and the debate around how to combine them, the more creativity. Having a structure facilitates these activities. This structuring comes from mapping both the market structure—the forces shaping your market—and the business model—how will you benefit from the opportunities opening up in the market.
The concept of the Landscape Monitor facilitates these activities. It maps market structure and business model, records observations both from team members and the web, and synthesizes these observations into insights that can be tested. It facilitates the learning that it is at the core of building a company.
About the authors
Tony Davila is professor of Entrepreneurship at IESE Business School. He has also been Professor at the Harvard Business School in recent years teaching in the MBA core curriculum. Before coming to IESE, he was a faculty member at the Graduate School of Business, Stanford University after receiving his doctorate from the Harvard Business School. He is co-author of The Innovation Paradox (2015), Making Innovation Work: How to Manage It, Measure It, and Profit from It (2006) and Malea Fashion District (2014).
Contact: [email protected]
Joost Korver is a Chief Entrepreneur and is Head of Growth at Gasbot, an exciting Australian IoT startup. He has many years of experience in the energy sector in commercial and innovation positions, and is also a startup mentor and angel investor who advises some larger companies on developing their innovation movement.
Contact: [email protected]
Mathieu is a lecturer in the Entrepreneurship Department. He is a seasoned entrepreneur and dynamic Business Angel with a portfolio of interesting start-ups and scale-ups.
Mathieu holds a Master in International trade from ESSCA in France and a MBA from IESE business School. Mathieu is also one of the founders of Venture Hub, a consulting firm supporting the scale of high growth start-ups and the development of entrepreneurial skills in the society at large.