Sean Brown: So how can advanced analytics help overcome these biases?
Nicholas Northcote: In three words, the outside view. The idea was originally proposed by world-famous psychologist and Nobel laureate Daniel Kahneman and his research partner Amos Tversky. They noticed what they called the planning fallacy: the fact that even experienced planners are overly optimistic in their forecasts for some of the reasons I mentioned before. For example, when building a business plan for a capital project or an acquisition, even experienced people underestimate costs, overestimate revenues or synergies, and therefore overestimate returns. Kahneman and Tversky argued, and proved, that by complementing project-specific projections, or business cases, with external data from a distribution of outcomes in similar cases, one can combat the overoptimism.
This can be done in strategy, too. By studying historical data from thousands of companies that describe their strategic initiatives and performance, you can understand the likelihood of a strategy succeeding. This view can reveal that your plan may be too optimistic—in other words, few companies achieved the outcomes you want—which you can use as a fact base to motivate bigger, bolder moves to meet your aspiration or alternately reduce your performance expectations.
Sean Brown: Can you give an example of how the strategic expectations might be adjusted based on this kind of review of past corporate performance?
Nicholas Northcote: If you are a large company with a target to grow economic profit by $100 million per year, we know from the data that only 35 percent of such companies managed to achieve that level of performance over a ten-year period. We can then overlay additional information. For example, we can tell you that companies that implemented a programmatic M&A strategy—meaning they were serial acquirers—and that achieved top-quintile productivity improvement increased their odds of reaching that performance target from the 35 percent to a 52 percent probability. This provides you with a fact base by which to calibrate your strategy and then challenge the organization to be bolder. The analysis can be extended to hundreds of strategic and performance metrics relevant to your company and the planned initiatives.
Take a multinational energy company we served. It had an extremely ambitious profit-growth target, and we used this outside-view approach to show that of all the companies with the same set of strategic moves in the last decade, only 10 percent achieved the level of performance improvement it was targeting. In other words, the strategy was simply too timid. There were not enough big strategic moves, such as substantial capital expenditures or big digitization and productivity-improvement programs. For example, what the company was planning was in line with the kinds of improvements achieved by the industry median company rather than a top-quintile performer on productivity. In the end, the company reassessed the plan and looked for bolder strategic moves, which it then included in its next iteration of the plan to fundamentally improve its chances of achieving its performance aspiration.
On the other extreme, we worked with an industrial company that had a very bold strategy with multiple big moves. In fact, the strategy was almost too bold. Only 5 percent of companies in our database of thousands had managed to successfully execute a plan like that in the last decade. By highlighting the fact that this was such a stretch ambition, we were able to demonstrate the importance of establishing a rigorous execution and performance-management infrastructure to deliver the plan. The company is now in the process of setting up a governance structure that you would typically see in a large operational transformation to ensure that they give this strategy the best possible chance of success, given it is an $8 billion investment.
Sean Brown: You mentioned that these analytics tools can also allow companies to understand complex market dynamics. How does that work?
Nicholas Northcote: This is a catch-all for anything related to forecasting or understanding competitive dynamics in the market. Some companies use AI engines to forecast demand, for example. Another company built a very detailed agent-based model, which models the behavior of individual market actors including customers, competitors, suppliers, even regulators, and effectively allows these agents to interact, helping to map unpredictable behavior in the system. It is similar to epidemiology: how individuals act leads to collective emergent behavior. This company used the model to understand demand for different types of products based on customer searches and buying patterns, among other factors. Electric-power companies do something similar involving fewer agents.
War-gaming, which is a way of understanding how competitors will respond to different strategic moves, is also becoming more analytics-driven. I have seen a cement company considering a merger go through an exercise like that, analytics-enabled, to understand the impact of a low-cost entrant on pricing behavior in the market.