Creating Great Choices

by Jennifer Riel

  So we ask, What currently causes the outcomes of each model (especially those benefits we most value)? In the community festival, what causes the powerful feeling of community engagement? Is it merely that anyone can buy a ticket? What might the addition of a jury and a prize, or industry-only events, do to that feeling? In the industry festival, what causes the revenues to flow to the city and to the festival? How might those revenues be produced in different ways?

  As teams explore the question of causality, it can be helpful to get ideas out of people’s heads and down on paper. The most effective tool for doing this is a causal model. The notion of causal modeling comes from systems thinking, a mode of thinking in which one attempts to understand a complex and dynamic whole by understanding the relationships between its pieces. In systems thinking, causal-loop diagrams attempt to capture “dynamic complexity, situations where cause and effect are subtle, and where the effects over time of interventions are not obvious.”5 Although there is a deep and sophisticated practice of modeling in systems dynamics, for our purposes, we’re happy with a diagram that captures your hypothesis of the cause-and-effect dynamics at play in your model, in which causal relationships are indicated via directional arrows connecting the different factors.

  In the film festival example, given that revenues were the most valued benefit of the industry model, we could sketch out a simple model to capture some of the most important causal forces driving financial sustainability at Cannes (see figure 6-1). Such a model could help shift our focus from a single outcome (revenues) to the different forces that help produce the outcome. It can help us understand the key forces and explore how we might produce those same outcomes with different inputs in Toronto.

  In the diagram in figure 6-1, you can start to see a virtuous cycle: at Cannes, the stars show up because the media is there, and the media is there because the stars show up. The presence of stars and media produces and reinforces a powerful buzz around the festival. Buzz builds the sense that the festival is good for business, and that also bolsters the reputation and legacy of the festival. Buzz makes the festival attractive to sponsors. And as sponsors flock to the festival, it becomes even more attractive to stars and media. In this cause-and-effect diagram, buzz starts to look like a very important component of sustainability.

  Figure 6-1. A Causal Model of Sustainability at Cannes

  Recall that sustainability was the problem Handling had set out to solve. He wasn’t simply asking how he might generate additional sponsorship dollars. He was asking how he might create a new model for his film festival that would set it on a solid base and ensure its longevity. The leverage point he found in his examination of the models was buzz.

  FINDING A LEVERAGE POINT

  At Cannes, buzz comes from exclusivity—from the careful curation of the films, from the jury of industry heavyweights, from parties and galas, and especially from the prize. Every major media outlet in the world reports on the Cannes festival and on the winner of the Palme d’Or. This coverage produces millions of dollars of free advertising for the winner and builds the reputation of the festival over time.

  But exclusivity is not necessarily the only path to buzz. The challenge for the Festival of Festivals was to produce buzz in a different way, finding a path to buzz that amplified rather than diminished the great benefits of the inclusive model. In other words, how might Handling take all he loved about his community festival, together with a new approach to buzz, to produce a sustainable film festival? The key to finding an answer was to find a new way to generate buzz using the assets in the community model to drive it. What was there about a highly inclusive festival that could be buzz-worthy?

  Ultimately, Handling realized, it was the audience itself. Because of its inclusive nature, the Festival of Festivals had an audience of thousands of Torontonians who would buy tickets for the films they thought they’d like, who would tell their friends about the films they’d seen, and who would laugh, cry, and applaud in response to the films on screen. This audience had the potential to be a test market, and a particularly powerful one. You see, Toronto is the most diverse city in the world by many measures (languages spoken, foreign passports held, and so on). Situated in the middle of North America, the city offers an audience that looks a lot like an audience in New York, or Minneapolis, or Austin. But a Toronto audience also has a great deal in common with audiences in London, and Shanghai, and Bangalore. So if a Toronto audience likes a film, it’s a good bet that the rest of the world will like it, too. Put another way, a Toronto audience is a predictive audience.

  The buzz at Cannes comes in good part from an award given by the arbiters of taste. Handling decided to combine the benefit of a buzz-worthy prize with the value of a predictive audience in an inclusive way. Rather than create an exclusive prize given by an industry jury, Handling took an existing but little-ballyhooed aspect of the festival and brought it front and center. From the first year, a low-key prize had been given at the Festival of Festivals—an award given to the favorite film of the festival based on audience votes. Handling and his team renamed the festival—it became the Toronto International Film Festival (TIFF)—and shone a bright spotlight on its People’s Choice Award.

  A GREAT CHOICE

  Under Handling, TIFF’s People’s Choice Award became a buzz engine. He helped the industry understand that access to a representative audience, and a prize tied to that audience, could be excellent for business. The People’s Choice Award, Handling argued, could effectively generate media and industry buzz, in part because the prize would help predict which films would go on to be successful at the global box office. That prediction could be worth a great deal—if Toronto audiences took the job seriously and did a good job of awarding the prize. Handling bet they would, because the audience prize actually makes the festival even more inclusive; it makes the festival belong even more to the community. Now the audience not only gets to see the films but also becomes the jury.

  Handling’s bet on an audience prize has been profoundly successful. Films that have won the TIFF People’s Choice Award include Crouching Tiger, Hidden Dragon; Slumdog Millionaire; The King’s Speech; and La La Land. The buzz has been so loud that TIFF has become one of the world’s most famous film festivals and, more importantly, its most influential.6 By 2015, the TIFF had grown to a field of more than three hundred films and 400,000 attendees. The winner of the TIFF People’s Choice Award is now routinely awarded front-runner status for the Academy Awards. And the festival’s budget is more than $40 million per year, making the event robust and highly sustainable. The secret to getting there was integrative thinking, including a robust examination of extreme and opposing models of film festivals in the world.

  THE STEPS OF EXAMINING THE MODELS

  To recap, examining the models is the second stage of the integrative thinking process. This step involves looking at both models together, holding them in tension, and asking a series of probing questions.

  When holding the models in tension:

  – What do we notice is similar in the models?

  – What do we notice is different between the models?

  – What do we value most from the two models?

  Looking at what we most value in the models:

  – What are the real points of tension?

  – What assumptions might we be making?

  – What important cause-and-effect forces are at play?

  Taking a step back:

  – What really is the problem we’re trying to solve?

  HOLDING THE MODELS IN TENSION

  The first step in holding the models in tension is to notice what strikes you about them: how they are similar and different in terms of the benefits and the weight of the benefits by player. In this step you’re warming up to dig deeper into the models and preparing to evaluate which of the benefits you most value in each model. See figure 6-2 for an example of how a student group began by turning a national retailer’s problem into a choice, then ske
tching out two opposing models. A brief pro/pro chart for this challenge is depicted in figure 6-3, which also indicates how the group followed the process of examining the models.

  Determining what you most value from a model is a highly subjective task. There is no single right answer, and different people may well value different aspects of the models to different degrees. This isn’t a bad thing; subjective evaluation enables a conversation about which outcomes you most desire, why you value what you value, and what might be potential paths to resolution of the problem. This step requires thoughtful application of managerial judgment; it demands that the team use analysis, logic, and productive communication to discern nuances, make connections, and align choices.

  Figure 6-2. An Example: Problem, Choices, and Sketches

  Figure 6-3. Examining the Models

  When you’re tackling an integrative challenge, asking what each person most values can help further clarify the models and benefits, as participants often must dig deeper into their own thinking to explain what they truly value and why. So this is a step worth taking seriously. Until now, we have focused on the positives of each model, so it may be tempting to say, “I love everything from both models.” Challenge yourself and your team to parse what is truly most valuable to each of you. You may wind up valuing everything from both models, but this position shouldn’t be the default. For this step, take time to ask yourself, Given the problem I am seeking to solve and the context I am in, what do I really value from each model? To narrow down to the absolute core, ask, What one benefit would I be loath to give up from each model? If you simply can’t settle on one benefit, consider whether there is an imbalance of sentiment in favor of one model, or an equal split between the two. You may find that there are really only one or two core benefits from each model that most matter to the team, or the group may value many of the benefits of one model and only one essential benefit of the other, or it may be that the group truly values most or all of both models. See figure 6-4 for a simple visualization of these three different conditions. Which most closely fits your situation as you determine what you most value?

  Asking which benefits you most value is an early gut check on the way the group is feeling about each of the models, and a possible indicator of which benefits could figure in the possible integrative solution. It also helps you identify which of the valued benefits are in tension with one another as well as which critical assumptions you want to explore, what cause-and-effect relationships bear more thinking about, and how you might need to reframe the problem you’re trying to solve.

  Figure 6-4. Determining What You Most Value

  Try This

  Going back to the problem you tackled in chapter 5, ask yourself, What is similar in the models? What is different? And what do I most value? Step back to reflect on the ways your thinking has shifted about the models as you have worked through these questions.

  QUESTIONING YOUR THINKING

  Few things are assured in life, but we feel confident in making this assertion: if you think about a problem as you have always thought about it, you will get the answers you have always gotten. New answers require new ways of thinking. The next step of examining the models, after determining what benefits you most value, is to ask new questions about tensions, assumptions, and cause-and-effect relationships. Your goal is to disrupt your current thinking about the opposing models.

  Tensions

  At a high level of abstraction, the sample opposing models we’ve presented may seem to be all tension: How can you possibly be inclusive and exclusive, centralized and decentralized, standardized and customized at the same time? As you dive deeper, though, you can start to see what specifically about the opposing models makes them incommensurable. What aspects of the models are truly at odds? And how might you think differently about that tension? Can you overcome it by thinking about the problem differently? Or can you make the tension irrelevant by structuring the solution in a particular new way?

  At a training we recently held with vice presidents and general managers of a consumer goods company, one of the GMs, who managed the Latin America business, told a story of a creative resolution that began from a place of serious tension. For this company, one of the key priorities for the current year was to increase profitability by driving down manufacturing costs for the product. The profitability challenge in the Latin American market was stark: whereas the global objective was to reduce the cost by $1 per package, for Latin America the target was closer to $3.

  Unfortunately, the Latin America GM was highly dependent on the global team for R&D and technology solutions that would drive down the cost, and, because Latin America was a relatively small market, he could not get the attention of the global team. The global brand team knew that any changes it made in Latin America would have little impact on the global numbers.

  The tension was in the incentives. The local market team had a model that said, “Latin America has a massive relative gap, so we should focus on driving down costs quickly in this region.” In contrast, the global model said, “we have a significant absolute gap to fill globally, so we should focus on our largest markets first to move the needle at scale.”

  As long as the problem was conceived in this way, the Latin America team had little recourse except to wait for global technology to make its way to the smaller markets. But instead, the GM and his team asked, How might we align the global team’s incentives with ours? How might we make it strategically smart for the global team to give cost-cutting technology to Latin America first?

  The leverage point turned out to be a matter of reframing an assumption. The assumption was that Latin America was too small to make a difference in the global numbers. Being small, in this case, was definitely bad. But how might being small actually be an advantage? How might using cost-cutting technologies in a small market first have an outsized effect on profitability?

  The solution in this case was to turn the small size of the Latin American market into an advantage. The team did that by presenting Latin America to the global team as the ideal location for piloting all the potential technologies. Piloting in a small market would reduce the overall risk of the new initiatives and would allow the team to work out any kinks before going global. And because all the technology would go to Latin America first, the cumulative effect on the market’s cost position would help the team meet its own profitability target.

  The team had started with an incommensurable tension, and by questioning a base assumption of the models, team members alleviated the tension and came to a winning solution for the local and global businesses.

  Try This

  Continuing with your own problem, look at the pro/pro chart and at the benefits you have identified as most valuable. Are there elements of the models, and in particular of your valued benefits, that are currently in tension with one another? What makes it hard to overcome the tension in your case?

  Assumptions

  An assumption is only a belief, one that we hold without consciously considering the evidence to support or contradict it. So it stands to reason that reflecting on our assumptions can push us to explore not only what proof stands behind our beliefs but also what might be possible if the assumption did not hold. As we’ve noted throughout this book, one of the fundamental principles that underlies integrative thinking is an acknowledgment that all models are wrong. Questioning assumptions is the natural extension of that stance.

  What does it look like in practice to question assumptions? The Latin American case is one example. Here is another. Recently, a product team decided to use integrative thinking to expand the possibilities for its future strategy. So we encouraged the team to define a series of either-or strategic choices. In particular, the team was struggling with the need to develop a plan for strategic growth. One choice team members articulated was whether to slowly grow from the core in existing markets, or to enter new markets as a fast-moving disruptor. In examining the models, they fell so in love with the be
nefits of operating as a disruptor that they began to question the assumption at the heart of their trade-off: Why would they operate as a disruptor (moving quickly, innovating consistently, operating lean) only in markets where they were new entrants? Why not adopt the same behavior in existing markets, and disrupt themselves? The insight marked a significant shift in mindset for the team and helped generate a slew of new strategic possibilities.

  Try This

  For your current problem, identify at least three assumptions underlying each of your opposing models. Consider assumptions you may be making about your key players, your own company and its capabilities, competitors, and the world at large. What might lead you to believe that these assumptions either do hold or do not hold? What might be possible if an assumption did not hold?