by Rita McGrath
These are the risks of overlooking weak or faint signals that other competitors are able to see.
The reason weak signals represent strategic opportunities is that the earlier you can spot an inflection point in progress, the more easily you can design your strategy to deal with it effectively. I am fond of an analogy to driving: When you can see far ahead, you can adjust your trajectory with a small move of the steering wheel. But when you see only after the inflection point is upon you, it requires a big jerk of the steering wheel.
Put another way, when you can see an obstacle far down the road, you need to make a very small adjustment with your steering wheel. But when the obstacle is suddenly in front of your car, you have to quickly and drastically turn the wheel in a big, big way.
Lagging, Current, and Leading Indicators
A good many managers pride themselves on being data-driven, obsessed with hard numbers and fluent with the facts. As a friend of mine who is a senior executive at Infosys is fond of saying, “In God, we trust. Everybody else brings data!” Indeed, in many organizations, the preparation of bulletproof slide decks, exquisitely detailed spreadsheets, and precise references to sources of data occupies stupendous amounts of managerial time.
The difficulty with such an emphasis on facts is that, unfortunately, facts are often a lagging indicator of what could potentially be important. By the time you are dealing with a fact on the ground, whatever led to it has already happened.
Lagging Indicators
A lagging indicator is an outcome or consequence of some activity that came before. Many of our most utilized metrics in business are lagging indicators. Profits, revenues, returns on investment, and even earnings per share are all lagging consequences of decisions made at some previous time.
Many companies I work with are strongly biased toward using lagging indicators to make their most important decisions, which itself can create incredible blind spots. Despite research that shows that even seemingly unambiguous metrics such as accounting numbers are influenced by personal preference, we have an almost magical belief that numbers will not deceive us. Thus, we are systematically biased to prefer lagging indicators in our strategic decision-making, which is problematic. By the time an inflection point has handed you a new reality, it’s a tad late.
Hard numbers can be helpful if they can help you to identify a trend or discontinuity by looking at patterns over time. In and of themselves, however, they are not particularly helpful if your goal is to understand the future and to see around the corner.
Common lagging indicators include:
Operating margins: how much profit you make on sales at the moment
EBITDA: earnings before interest, taxes, depreciation, and amortization (meant to convey how much a company is earning absent extraneous information)
Revenue/turnover: total amount taken in by the company in a given time period
Revenue growth or decline: change from a prior period
Return on net assets: net income divided by the assets used to generate it
Operating income change: change from a prior year period
As you can see, every one of these numbers represents an outcome. It is a reflection of actions taken that produce that number, but it doesn’t shed any light at all on what you might need to do for the number to tell a different story in the future. Focusing so much on lagging indicators is one reason many strategists and long-term thinkers find the quarterly profit obsession of many publicly traded companies to be so depressing. In striving to make a quarterly lagging indicator look good, factors that might make it look better over the long term are sacrificed.
Current Indicators
Current indicators give you data about the current state of things. One reason so many real-time systems are popular is that it is valuable to know exactly where you are—a bit like a directions app showing your location and how long it will take to get to your destination. Entire industries—from sensor-linked monitoring to enterprise resource planning (ERP) systems—have been built on being able to answer the question, What is going on right now?
Many current indicators are based on the proven recipe for success in a given business—that is, on conditions at one point in time. Managers learn to pay attention to these indicators. Analysts are trained to study them. Employees and leaders focus on them. And they are often simply taken for granted as predictors of what will drive success in the future.
For instance, in the traditional energy business, a number of key performance metrics have grown up over time based on the assumption of a centralized, grid-based power supply network. Examples include:
Power cuts and average duration: How much disruption are customers currently experiencing?
Consumption by sector: Who is consuming your energy and when are they consuming it?
Operating cash flow: How much cash are you generating from operations?
Production costs: What is it costing you to produce your energy? How do different sources compare?
Availability factor: How readily can you meet current energy demands?
Asset utilization ratio: How efficiently are you using your assets?
As long as the key constraints of a grid-based, largely regulated utility arrangement for energy companies remain in place, these metrics (key performance indicators) make sense. However, such metrics are not going to tell you very much that will prepare you for the future of the energy business. As we’ll see later in this chapter, the advent of renewables, the idea that power can be generated in a distributed manner and sold back to the grid, the possible implications for energy consumption of electric vehicles, consumption changes led by “smart” thermostats, and the very idea that in the future customers will have a choice of suppliers and interaction mechanisms are not reflected in these current indicators.
With employee incentives often tied to their performance with respect to driving current indicators, it isn’t surprising that many people don’t bother to look beyond them. And yet, this approach can be a source of significant blind spots in a changing environment. As I’ve said previously, an inflection point changes the nature of the key metrics that reflect the taken-for-granted assumptions in your business. Just paying attention to the current indicators is not going to be all that helpful.
Leading Indicators
Leading indicators represent things that are not facts yet in your business. They have the potential to lead to facts later on, but at the moment you’re looking at them, they are only suppositions, conjectures, and assumptions. They are often qualitative rather than quantitative. They are often told as narratives and stories rather than in meticulous PowerPoint charts. For that reason, executives are often wary about basing important decisions on them. This can be folly of the highest order in a world of strategic inflection points, because the leading indicators are where ideas about the future are to be found.
Sanjay Purohit, who was a leading figure at Infosys and head of planning for more than a decade, made it a core part of his role to spend significant amounts of time looking for leading indicators that go beyond today’s key performance indicators. As he told me,
When I have seen an organization anticipate the inflection points, there has always been a proactive effort at modeling leading indicators . . . I’m looking for knowledge that sits at the periphery of the organization. I ask myself: How do you engage with the periphery? I used to spend significant time with the salespeople. It involved being in the market, listening, looking for cues—are they seeing something they can’t necessarily express but lives are starting to change? . . . It’s a lot of signal processing, if you will. I spent considerable time in trying to catch these signals.
Purohit saw Infosys through a number of inflection points, most recently (2014) as the founding CEO of an Infosys-owned startup, EdgeVerve Systems, which was designed to explore the promise of platform strategies as opposed to Infosys’s traditional core business. This was in direct response to the early warnings represented by t
he rise of Amazon Web Services, Google, and other platforms that were becoming increasingly powerful.
Relationship Between Indicators and Outcomes
Let’s have a look at the relationships between these different kinds of indicators and outcomes in the following table. Say the outcome (the fact) that you want to understand is customer churn—how many customers are deserting your business in a given time period. Just knowing that information gives you little guidance on what to do to reduce it. In your quest to get to the bottom of this, you find a distinct correlation between period 1 customer satisfaction and period 4 customer defection. Customer satisfaction might, therefore, be a proximate indicator of future customer intention.
Lagging
Current
Leading
Customer churn
Customer satisfaction
Employee engagement
Employee turnover
Employee engagement
Management effectiveness
Revenue from new products
Customer usage
“Customer love”
What, in turn, underlies customer satisfaction? Many studies suggest a powerful role for employee satisfaction and engagement. Employee engagement thus becomes a leading indicator of later customer propensity to defect, and something you can take action to influence.
If your attempt was to understand a different outcome—say employee turnover—levels of engagement might be seen as a different kind of indicator, in this case a current one. Highly engaged employees are less likely to depart your organization than disengaged ones. A leading indicator might be the effectiveness of how those employees are managed. In particular, the element of psychological safety in a team is highly correlated with employee engagement and propensity to remain, as Amy Edmondson discovered years ago and massive research by Google more recently reconfirmed.
As a third example, consider the tack taken by Satya Nadella, who replaced Steve Ballmer as CEO of Microsoft in 2014. Rather than continuing to focus on Windows-driven profits that characterized the Ballmer era (and which Blank so roundly criticized), Nadella has framed his leadership entirely around leading indicators. As he said in a 2015 interview, “We no longer talk about the lagging indicators of success, right, which is revenue, profit. What are the leading indicators of success? Customer love.”
Essentially, Nadella is making a big bet that if he can get the right leading indicators in place, the rest will follow. Several years into his tenure, he is receiving major kudos for transforming the software giant and making it relevant to the new growth areas in technology, even pitting its Azure cloud offering against the powerful Amazon Web Services.
This brings us to the question, How do you develop leading indicators of a potential inflection point in the early, pre-recognition phase?
The Inverse Relationship Between Degrees of Freedom and Signal Strength
A useful model for thinking about obtaining leading indicators was created by a consulting firm once known as the Futures Group and now known as the Futures Strategy Group. The model begins by assessing the “signal strength” of a given piece of information about the future.
As you can see in Figure 1, in the early stages the signals are weak. The signal-to-noise ratio is quite high, and it would be a mistake to make any big strategic move at this point, because there is still an enormous amount of uncertainty.
Figure 1 Increasing Strength of a Signal over Time
Information, however, has a way of gathering steam in a nonlinear way. Thus, to recall my earlier Internet example, in the 1995–2000 era the signals that the Internet was going to have a major impact on retail (or anything else) were very noisy. Some people believed the hype, others dismissed it, but the reality of the impact of the Internet during that time was that companies were still struggling with ecosystem deficiencies (slow and expensive dial-up modems, limited bandwidth, few lucrative business models, and so on). By the time 2000–2005 rolled around, the survivors of the initial dot-com bust had figured much of this out.
Email had become commonplace. Broadband and always-on connections had become widely adopted. In a relatively short period of time, what was once a question about which reasonable people could reasonably disagree, the signals that the Internet was indeed going to create dramatic change in the world were indisputable. You can think of the rapid increase in signal strength as taking place in that 2000–2005 period. After that, people could easily see that digital technologies were going to have a major impact on the consumption of any kind of good that could be transmitted digitally. By the time period labeled “Time Zero” in Figure 1, the digitization of books, music, and other products would be mainstream.
The dilemma for the strategist is that while we would like to be making decisions with the kind of specific and clear information that will be available at time zero, that is much too late. By that time, the inflection is obvious to everyone, and the chance to respond early has been lost. You can think of this in terms of the model in Figure 2.
Figure 2 Inverse Relationship Between Degrees of Freedom and Signal Strength
As this figure shows, the relationship between strategic degrees of freedom and signal strength is practically inverse. In the unfair way in which life operates, the moment at which you have the richest, most trustworthy information is often the moment at which you have the least power to change the story told by that information.
This brings us to the critical notion of building an intelligence system to detect early warnings. As we’ve already seen, you don’t want to be making big strategic moves when the signal-to-noise ratio is very high, or too early on. You also don’t want to wait until the facts are plainly obvious to everyone. Instead, you need a way to get information with respect to what is sometimes called the period of optimum warning, around the middle of the chart, as shown in Figure 3.
Figure 3 Period of Optimum Warning
This brings us to the role of constructing scenarios to identify potential time zero events.
Expanding the Futures You Consider: Sparking Imagination
Because inflection points undermine the very assumptions on which a business is based and which have come to be taken as “facts” by most decision-makers, it is often difficult for leaders to imagine a different world. It is this failure of imagination that so often leads to strategic surprise.
Consider, for instance, the shift in the US restaurant business toward take-out food that is consumed off premises. Today, some 63 percent of all meals purchased from a restaurant are not actually eaten in that restaurant. This came as a surprise to many restaurateurs who went into the business not just to prepare food but also to offer guests the experience of hospitality. As one restaurant owner has observed, “It’s a completely different business,” adding, somewhat wistfully, “I didn’t do this to put food into boxes.”
What would be useful, therefore, are some straightforward ways of expanding the possibilities you are prepared to consider. Some organizations turn to scenario planning for this.
There is a vast literature on very advanced techniques for scenario planning, and some excellent ideas on how you can envision the future. For our purposes, which is to identify potentially significant future time zero events, elaborate scenario exercises aren’t necessary. What I suggest instead is using simple two-by-two matrices to outline future possibilities. These matrices should be different enough that they point to different time zero outcomes.
Two key elements to avoid in doing this work are (1) imagining a future in which only one thing changes but everything else stays the same, and (2) thinking only in terms of linear change. One of my favorite examples of this is the classic TV show The Jetsons, which featured robot servants and flying cars, but portrayed gender roles and work arrangements firmly rooted in 1962!
Andy Grove’s 1997 discussion of indicators signaling that a change may be afoot is a reasonable point of departure. He suggests considering three future scenarios.
Your k
ey competitor is about to change. If you had only one silver bullet, whom would you aim it at?
Your primary complementor is about to change. The ecosystem will be different.
Management’s ability to make sense of what is going on “out there” has diminished in some significant way.
Let me give you an example. For a traditional energy distribution company I was working with in 2017, two major sources of uncertainty were future demand and future configuration of capabilities.
First, I was surprised to learn that, for a variety of reasons, energy consumption had been in slow-growth mode in much of the world for some time. This was an inflection point that many—even the vaunted General Electric—got very wrong. For GE, the result was massive overinvestment, losses and layoffs in their power business, management churn, and a toppling from their spot as one of America’s best-run companies.
Further, the power distribution model that the sector had used for years—consisting of large generating plants and distributed power lines—was moving toward distributed networks, called distributed energy resources, or DERs. In that model, power comes not only from the traditional power generation plant but also from renewable sources such as wind and solar, making the one-way traditional approach obsolete. Another threat to the centralized grid was presented by advances in battery technology. In many parts of the world, a grid is either unaffordable or will result in infrastructure that will be so vulnerable to theft and malfeasance that it won’t be reliable. Even with today’s battery technology (powered by solar cells), some power for some of the day is better than no power at all, making the grid less desirable for those locations.