Over the past decade, advances in digital analytics have transformed the way businesses operate. From marketing and pricing to customer service and manufacturing, advanced analytics is now central to many corporate functions. The same, however, cannot be said for strategy—at least not yet.

While strategy development will always require creative and thoughtful executives to set aspirations and make bold choices, analytics tools can give you an edge. Advanced analytics can be used to accomplish the following:Reduce bias in decisions by calibrating the likelihood of your strategy succeeding before you allocate resources.

• Unearth new growth opportunities by complementing traditional brainstorming methods to reveal hidden pockets of growth.
• Identify early-stage trends by painting a real-time picture of how your business context is unfolding so that you can trigger big moves before your competitors do.
• Anticipate complex market dynamics by generating proprietary insights about the combined impact of myriad forces.

Each of these applications can sharpen business leaders’ views of the competitive arena and how they can position themselves to win. But that requires putting advanced analytics front and center in the strategy process.

Reduce bias in decisions

When Daniel Kahneman and Amos Tversky¹ observed that even experienced planners tend to underestimate the cost and time required to complete projects, they termed the phenomenon “planning fallacy.” They argued that this tendency results from people making forecasts based on the specifics of the case at hand combined with their personal experience and intuition (commonly referred to as “the inside view”), without taking into account the distribution of outcomes of similar cases (“the outside view”).² As a result, many forecasts are overly optimistic. The two collaborators went on to propose a corrective procedure called “reference class forecasting” that involves complementing the inside view with data on real-world outcomes, or “base rates,” from a reference class of similar cases.

In the past 20 years, the use of this technique has gathered impressive momentum, with hundreds of articles highlighting the methodology’s application in both academic and practical settings. To date, such calibrations have been limited largely to the field of project management, but forecasts made during strategic planning confront similar challenges. Strategic plans, too, involve estimating the future costs and benefits of investments, making an outside view just as valuable in informing those decisions.

In our recent book Strategy Beyond the Hockey Stick (Wiley, February 2018), we introduced the idea of using data analytics to bring an outside view to strategy. By embracing the outside view, you can estimate your strategy’s odds of success before you allocate resources to that strategy. For example, if your target is to grow economic profit by $100 million per year in the next decade, would it not be helpful to know that only 35 percent of large companies managed to achieve that over a decade? And if we told you that companies which implemented programmatic-M&A³ strategies and reached the top quintile in productivity improvements were 1.5 times more likely to achieve that profit target, would you not consider prioritizing those two areas in your strategic efforts (Exhibit 1)?

Exhibit 1

We have often applied this methodology to calibrate strategies and performance aspirations against data from thousands of publicly listed companies. The approach can be used to motivate bold strategic moves while, in some cases, demonstrating that an ambition is unlikely to be achieved without exceedingly strong execution. For example, when shown this approach, an energy company realized that their planned strategy had achieved the financial performance they were targeting in only 10 percent of historical cases. The strategy was simply too timid. This led the company to reassess the plan and include bigger, bolder strategic moves that would improve their odds of achieving desired gains. At the other extreme, a materials-industry company set a strategy that only 5 percent of companies in a database had managed to execute successfully. Highlighting the stretch of this ambition helped demonstrate the importance of establishing a rigorous execution- and performance-management infrastructure for the plan’s delivery, helping to lower the risk in $8 billion worth of investment.

Unearth new growth opportunities

Advanced analytics can also enhance strategic planning by unearthing growth opportunities that would otherwise be hard to spot, be they attractive industry segments and acquisition targets, ideas for new products or services, or even new applications for existing offerings. The most effective of these algorithms use sophisticated network analysis and natural-language processing to parse and find connections among hundreds of disparate text data sources such as company descriptions, patent filings, M&A data, and academic papers.

For example, a company that specializes in producing brominated compounds (largely used in commodity-chemicals segments such as detergents) scanned all publicly available information on such compounds, including more than 120 million patents and 100 million academic publications, to identify hundreds of potential new applications for its products. The organization prioritized 30 higher-margin applications, such as in niche medical devices, which ended up collectively contributing $50 million of revenue to its growth plan. Similarly, when another company specializing in performance materials wanted to analyze growth opportunities within the gasket and insulation space, it used a text-clustering algorithm and network analysis to pinpoint 45 potential growth areas within five clusters, each with a shortlist of acquisition prospects (Exhibit 2).