How to Launch Products in Uncertain Markets

In volatile times, uncertainty can be turned into a competitive advantage.

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Predicting the needs of your customers has always been tricky. In one 2005 survey, for example, 80% of corporate executives said they believed they were delivering superior products to their customers — but only 8% of their customers agreed.1 It’s even harder to please your customer when things are as uncertain as they are today. Shifts in politics, immigration patterns, and trade policies are shaking the foundation of international commerce. Consumers are both more informed, thanks to the internet, and more fickle, thanks to social media. When you consider all that — and the rapidity and frequency of technological change — predicting the future needs of your customers may seem like a fool’s game.

Given the obvious costs of misjudging customer preferences, how should companies at the brink of a product launch behave in the face of great market uncertainty? Should they “wait and see” until uncertainty resolves? Or should they commit resources for a full-scale launch and ride it out?

The conventional wisdom these days is that being early to market is the right choice. But our study of 550 manufacturing companies and analysis of service companies with considerable sunk investments suggests that this is not always the case. (See “About the Research.”) Often, being better matters more than being first. We’ve observed that many companies can benefit by taking a mixed approach, which we like to call “act and see.” By deferring the large-scale launch of new products and using the time to conduct effective R&D, companies can glean valuable insights and develop capabilities that give them an edge on competitors that rush in with less caution. But implementing an act-and-see approach isn’t easy. Business leaders must ensure that the company has the personnel and the structure to make effective learning from experimentation commonplace.2

The idea of experimentation in the face of uncertainty is not novel for those who are familiar with concepts such as discovery-driven planning, probe and learn, disciplined entrepreneurship, active waiting, and lean startup. So, what’s new? Our work shows how prelaunch experimentation can build capabilities that help you create value in uncertain market environments. What’s more, those capabilities will make it harder for competitors to copy you.

A Wise and Active Approach

Some managers make decisions without considering the uncertainty in the markets they are about to enter.3 They make low-information bets on new products that may not be in demand. This all-too-common approach is so foolhardy that we hesitate to even call it a strategy.

A more conscious alternative to ignoring uncertainty is to avoid it. Investment theory argues that businesses should wait and keep their options open, putting off any irreversible commitment and keenly watching the market. But this approach has several problems. First, it assumes that uncertainty will resolve over time, which may or may not be the case. Second, the company is learning only passively. Waiting and watching, the company wastes time that could be spent acquiring the constantly changing skills and capabilities necessary to stay competitive in challenging markets. Third, the company runs a serious risk of falling behind its more proactive competitors.

Another conscious alternative, being first to market, is a prized tenet of Silicon Valley culture, but its value is often overstated. Without a keen sense of customer demand, such launches can be risky — especially when the company has made sizable and irreversible commitments. Iridium, the satellite phone company that filed for bankruptcy in 1999, invested $5 billion for the launch of a worldwide network that no one needed or wanted. Webvan, a startup from the same era, had a great idea: delivering groceries that were ordered online. The company expanded quickly, only to discover that the idea was way ahead of its time. Demand never materialized, and Webvan filed for bankruptcy in 2001.

An act-and-see strategy combines a realistic patience with active learning. By delaying expensive launches, companies acknowledge their need to lessen the uncertainty they face. But by experimenting actively during this delay — perhaps by dipping a toe into a limited market, with a product that can still be iterated, to gauge appetite, validate assumptions, and build capabilities — they learn more about their customers and the market. When the new product or technology does finally launch, these capabilities can be deployed straightaway to exploit the market opportunity more effectively and less expensively.4

Developing these additional capabilities can lead to critical competitive advantages. For example, acquiring better data about your customers ahead of the launch of a single product can pay off on later launches as well. Pablo Isla, CEO of Inditex, parent company of clothing retailer Zara, emphasizes that Zara’s business success is not about speed but accuracy — using the data it has collected over many product launches to understand what its customers really want and translating those insights into product offerings. Zara’s competitors have been hard-pressed to keep up.5

As MIT finance and economics professor Robert Pindyck shows, the learning curve is crucial for the value of real options at a time of uncertain demand.6 Learning more before the start of full-scale production increases the potential value of new products while lowering the cost of commercialization. It also creates a strategically valuable residual uncertainty among rivals that are less willing to experiment: As they struggle to copy you, they are incapable of aggressive responses and must “make room.”7

In the aftermath of the oil crisis, for example, Hyundai Motor shifted its learning orientation and capability building. Instead of learning by doing production, it spent more on R&D to learn more before doing. By actively learning while waiting to strike, Hyundai enhanced the upside of uncertainty and limited downside risks. When the economic climate became less uncertain, Hyundai launched strong products that its customers wanted, increased sales, and caught up with rivals.8

Three Ways to Learn Before Launching

Speaking about the value of prelaunch experimentation in times of uncertainty, Pankaj Ghemawat reminds us that “learning does not, of course, occur automatically, not even for new ventures.”9 At companies with clear processes for acting upon learning, allocating more resources to R&D for experimentation under uncertainty can enhance value. At companies without this capability, more R&D may simply waste resources. Our research suggests that companies that regularly turn learning gleaned from experimentation into value do these three things:

1. Hire people who are skilled at the experimental process. To turn learning into value, you have to unearth meaningful insights and then make something of them by informing, connecting, and motivating various parts of the organization.10

Scientists and engineers with training from top universities can facilitate both parts of the process. They are experienced at designing experiments that unveil meaningful data and explore cause-and-effect relationships. They are also accustomed to drawing on the assistance of many different departments along the way, for everything from securing proper resources to licensing discoveries. Those skills are of great value in a corporate setting, where, for instance, engineers in isolation may create brilliant products that are of no use to anyone, while marketers in isolation may yearn for products that are technically impossible. In a successful experiment, the product marketing team and the research laboratories (including process engineers) interact during the learning process. The skill of integrating insights into a company’s processes is critical for developing the capabilities that enable a company to introduce new product options. Business leaders must keep this in mind when hiring technical staff: Training in the process of experimentation is as important as individual technical brilliance.

2. Leverage assets of the core business. Sometimes, new product opportunities that emerge from corporate labs are not strategically central to the existing business. Turning those opportunities into meaningful value is particularly difficult, given the company’s inexperience and unfamiliarity with the new domain. That’s why this kind of learning before doing can be costly and inefficient.

We’ve found that it’s more effective to explore opportunities that are closely related to a company’s core business. This leverages past experiences and existing capabilities, ensuring that people know when a particular cause-and-effect relationship is meaningful. Learning rates from experimentation are higher when new launches are similar to existing products. The familiarity makes it easier to discern and apply meaningful lessons. Furthermore, the cost of experimenting close to the core is lower, since the company can reuse existing assets such as models and components.

Cost-effective learning has a high learn-to-burn ratio — that is, the rate at which information about a course of action is received divided by the cost of pursuing such experimentation.11 Rather than persuading scientists and engineers to execute experiments when learning cannot be transferred and leveraged, business leaders should encourage possibilities with high learn-to-burn ratios so that the company’s current competence serves as a tool for taming the uncertainty of the market.

3. Match experimentation to the industry’s life cycle. The strategy for facing uncertainty in a mature industry is very different from the one required when contending with the uncertainty of a nascent sector. In the early stages of an industry’s life cycle, extensive experimentation, iteration, and variation can deliver enormous value, since so much of the market is unclear and unexplored. In mature industries, however, companies must look to other areas for explosive growth, since the learning opportunities in the old business have already been exploited.

A good example of this is today’s automotive industry. Improvements in gasoline-powered engines are likely to deliver minimal value, given the maturity of the market. The learn-to-burn ratio in gasoline engines is very low. But the electric vehicle market holds much more promise. There, R&D spending on electric capabilities may pay off handsomely. In fact, the market is so promising that R&D spent on learning how to effectively switch from the old capabilities to the new might also be money well spent.

A Recipe for Smart Failure

Done right, prelaunch R&D can give companies a meaningful edge on the competition in uncertain times. Of course, this kind of act-and-see approach isn’t for every company. Some businesses that require commitments years before the start of production may not have the flexibility to respond to new information. But most businesses can and should consider such a strategy when uncertainty reigns.

As strategy and innovation experts Rita McGrath and Ian MacMillan point out, in uncertain times, businesses must let go of old approaches in order to direct resources into new opportunities for growth.12 One critical tendency they must abandon is their bias against failure. Product managers and marketers run into this repeatedly when trying to convince the C-suite that delaying a launch and directing more funds to R&D is a good strategy. Without a tolerance for failure, experimentation will never deliver the kind of learning that companies need.

And yet, it’s important to set the stage for failure that the organization can tolerate and learn from. By bringing skilled experimenters in-house, tapping core business assets to facilitate prelaunch learning, and focusing on underexplored areas of the market, companies can equip themselves to turn uncertainty into value.

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References

1.J. Allen, F.F. Reichheld, B. Hamilton, and R. Markey, “Closing the Delivery Gap,” Bain & Co., Oct. 5, 2005.

2.J.M. Ross, J.H. Fisch, and E. Varga, “Unlocking the Value of Real Options: How Firm-Specific Learning Conditions Affect R&D Investments Under Uncertainty,” Strategic Entrepreneurship Journal, forthcoming.

3.C. Bradley, M. Hirt, and S. Smit, “Strategy Beyond the Hockey Stick: People, Probabilities, and Big Moves to Beat the Odds” (Hoboken, New Jersey: John Wiley & Sons, February 2018); H. Courtney, “20/20 Foresight: Crafting Strategy in an Uncertain World” (Boston: Harvard Business School Press, October 2001); R.M. Cyert and J.G. March, “A Behavioral Theory of the Firm” (Englewood Cliffs, New Jersey: Prentice Hall, 1963); and R. Martin, “Strategy and the Uncertainty Excuse,” Harvard Business Review, Jan. 8, 2013.

4.G.S. Lynn, J.G. Morone, and A.S. Paulson, “Marketing and Discontinuous Innovation: The Probe and Learn Process,” California Management Review 38, no. 3 (spring 1996): 8-37; R.G. McGrath and I.C. MacMillan, “Discovery-Driven Planning,” Harvard Business Review 73, no. 4 (July-August 1995): 44-54; G.P. Pisano, “The Development Factory: Unlocking the Potential of Process Innovation” (Boston: Harvard Business School Press, 1997).

5.T. Buck, “Fashion: A Better Business Model,” Financial Times, June 18, 2014.

6.A.K. Dixit and R.S. Pindyck, “Investment Under Uncertainty” (Princeton, New Jersey: Princeton University Press, 1994), 339.

7.B. Kogut and N. Kulatilaka, “Capabilities as Real Options,” Organization Science 12, no. 6 (November-December 2001): 744-758; and N. Kulatilaka and E.C. Perotti, “Strategic Growth Options,” Management Science 44, no. 8 (August 1998): 1,021-1,031.

8.L. Kim, “Crisis Construction and Organizational Learning: Capability Building in Catching Up at Hyundai Motor,” Organization Science 9, no. 4 (July-August 1998): 506-521; and W.S. Shim and R.M. Steers, “Symmetric and Asymmetric Leadership Cultures: A Comparative Study of Leadership and Organizational Culture at Hyundai and Toyota,” Journal of World Business 47, no. 4 (October 2012): 586.

9.P. Ghemawat, “Commitment: The Dynamic of Strategy” (New York: Free Press, 1991), 132; and P. Ghemawat and G. Pisano, “Sustaining Superior Performance: Commitments and Capabilities,” Harvard Business School case no. 9-798-008 (Boston: Harvard Business School Publishing, 1997).

10.On how to design experiments, see S.L. Brown and K.M. Eisenhardt, “Competing on the Edge” (Boston: Harvard Business School Press, 1998); J. Fjeld, “How to Test Your Assumptions,” MIT Sloan Management Review 59, no. 2 (winter 2018): 89-90; R.G. McGrath, “Failing by Design,” Harvard Business Review 89, no. 4 (April 2011): 76-83; D.N. Sull, “Disciplined Entrepreneurship,” MIT Sloan Management Review 46, no. 1 (fall 2004): 71-77; and S.H. Thomke, “Experimentation Matters: Unlocking the Potential of New Technologies and Innovation” (Boston: Harvard Business School Press, 2003).

11.Ghemawat, “Commitment: The Dynamic of Strategy.”

12.R.G. McGrath and I.C. MacMillan, “How to Rethink Your Business During Uncertainty,” MIT Sloan Management Review 50, no. 3 (spring 2009).

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