Chapter 3. The Strategy Bottleneck
The traditional approach to corporate strategy is a poor fit for this new type of digital-driven business and software development. Having worked in corporate strategy, I find that fitting its function to an innovation-led business is difficult. If strategy is done in annual cycles, predicting and proscribing what the business should be doing over the next 12 months, it seems a poor match for the weekly learning you get from a small-batch process.
A Moment of Pedantry
First, pardon a bit of strategy-splaining. Having a model of what strategy is, however, is a helpful baseline to discuss how strategy needs to change to realize all these “digital transformation” dreams. Also, I find that few people have a good grasp of what strategy is, nor what I think it should be.1
First, stripped down, a corporate strategy determines which markets a company will sell into, how it will do it, and how it won’t do it. This will drive budgets for existing and new product lines, entering new markets, mergers and acquisitions (M&A) to enter new markets and acquire new capabilities, and divestures to offload distracting businesses. However complex the actual strategy might be, when communicated, the strategy needs to be boiled down to something simple that can trickle down to the rest of the organization: “branchless banking,” “become a logistics company,” or, in an example of divesting a business, “focus on retail, not insurance.”
I like to think of all “markets” as flows of cash, big tubes full of money going from point A to point B. For the most part, this is money from a buyer’s wallet flowing to a merchant. A good strategy figures out how to grab as much of that cash as possible, either by being the end-point (the merchant), reducing costs (the buyer), or doing a person-in-the-middle attack to grab some of that cash. That cash grabbing is called “participating in the market.”
When it comes to defining new directions companies can take, “payments” is a good example. We all participate in that market. Payments is one of the more precise names for a market: tools people use to, well, pay for things.
First, you need to wrap your head around the payments industry. This largely means looking at cashless transactions because using cash requires no payment tool. “Most transactions around the world are still conducted in cash,” The Economist explains, “However, its share is falling rapidly, from 89% in 2013 to 77% [in 2019].” There’s still a lot of cash used, oddly in the US, but that’s changing quickly, especially in Asia. For example in China, The Economist goes on, “digital payments rose from 4% of all payments in 2012 to 34% in 2017.” That’s a lot of cash shifting and now shooting through the payments tube. So, let’s agree that “payments” is a growing, important market that we’d like to “participate” in.
There are two basic participants here:
New companies enter the market by creating new ways of paying for things that compete with existing ways to pay for things. For example, new entrants are services like Alipay, Apple Pay, and GrabPay. Although this is the domain of startups in most people’s minds, large companies play this role often.
Existing companies both defend their existing businesses and create new ways of paying for things. For example, Dutch banks launched iDEAL several years ago and you can use a simple person-to-person payment service called Tikkie to pay your babysitter or dry cleaner now. Existing companies often partner with new entrants, for example: Goldman Sachs provides the backend for Apple Pay and Maybank partnered with GrabPay. Incumbents can also accomplish the second goal by just acquiring the new companies: in general banking, Goldman Sachs acquired Honest Dollar to help it get into consumer banking.
“Strategy,” then, is 1.) deciding to participate in these markets, and, 2.) the exact way these companies should participate; that is, how they grab money from those tubes of cash. Defining and nailing strategy, therefore, is the key to success and consequently survival. For example, an estimated 3.3 trillion dollars flowed through the credit card tube of money in 2016.2 As new ways of processing payments gain share, they grab more and more from that huge tube of cash. Clearly, this threatens the existing credit card companies, all of whom are coming up with new ways to defend their existing businesses and new payment methods.
Getting Over Digital Transformation Fatigue
Pronouncements like this chestnut are by now obvious thanks to the many Cassandras who have grown hoarse over the years. As Peter Jacobs, then CIO of ING Netherlands, put it:
We came to the realization that, ultimately, we are a technology company operating in the financial-services business. So, we asked ourselves where we could learn about being a best-in-class technology company. The answer was not other banks, but real tech firms.
This type of thinking has gone on for years, but change in large organizations has been glacial. If you search for the phrase “digital transformation” every day, you’ll find sponsored posts on business and tech news sites preaching this, as they so often say, “imperative.” They’re long on blood-curdling pronouncements, and short on explaining what to actually do.
We’re all tired of this facile, digital genuflection. But maybe it’s still needed.
If survey and sentiment are any indication, digital strategies are not being rolled out broadly across organizations. Instead, much of the focus now and in previous years has been on improving IT processes and infrastructure.3 Although there’s still much work to be done across industries, the premise of this report is that we need to expand beyond just transforming IT and transform the rest of the business.
Early digital transformation applications often focus on business outcomes, like moving people from call centers to apps or improving the store finder app. Indeed, “improving customer experience” is one of the top goals of most app work I see. But, we need to do a lot more. There’s plenty of room for improvement and much work to be done by strategy groups to direct and decide digital strategy and to move beyond these initial apps. Let’s look at a two-part toolkit for how they might do it.
Sensing Your Market
Changing enterprise strategy is costly and risky. Do it too early, and you deliver perfectly on a vision but are unable to scale to more customers: the mainstream is not yet “ready.” Do it too late, and you’re in a battle to win back customers, often with price-cutting death spirals and comically disingenuous brand changes: you don’t have time for actual business innovation, so you put lipstick on discount pigs.
An innovation strategy relies on knowing the best time to enter the market. You need a strategy tool to continually sense and time the market. Like all useful strategy tools, it not only tells you when to change, but also when to stay the same, and how to prioritize funding and action. Based on our experience in the technology industry, we suggest starting with a simple model based on numerous tech market disruptions and market shifts. This model is Horace Deidu’s analysis of the post-2007 PC market. 2007, of course, is the year the iPhone was introduced. I’m not sure what to call this specific point in time, but the lack of a label doesn’t detract from its utility. Let’s call it the Dediu Cliff, which you can see in Figure 3-1.
To detect when a market is shifting, Deidu’s model emphasizes looking beyond your current definition of your market. In the PC market, this meant looking at mobile devices in addition to desktops and laptops. Microsoft Windows and x86 manufacturers had long locked down the definition and structure of the PC market. Analyst firms like IDC tracked the market based on that definition and previous, attempted disruptors like Linux desktop aspirants competed on those terms.
When the iPhone and Android were introduced in 2007, the definition of the PC market changed without anyone noticing. In a short 10 years, these “phones” came to dominate the “PC” market by all measures that mattered: time spent staring at the screen, profits, share increases, corporate stability and high growth, and customer joy. Meanwhile, traditional PCs were seen mostly as work horses, or commodities like pens and copy machines bought on refresh cycles with little regard to differentiation.4
Making your own charts will often require some art. For example, if you wanted to explore the theory that mobile was a more important “storefront” than desktops or in-store browsing, you could compare customer visits and time spent on your mobile app, desktop site, and in-store. Comparing these trends lines over a decade will give you meaningful and easy-to-understand data to drive corporate strategy and funding decisions.
You need to find the type of data that fits your industry and the types of trends you’re looking to base your strategy on. Those trends could be core assumptions that drive how your daily business functions. For example, many insurance businesses are still based on talking with an agent. So, in the insurance industry, you might look at online versus offline browsing and buying. (Surprisingly, there isn’t as steep a drop off into the web as you’d think.)
Nonetheless, tracking these shifts closely and paying attention to the rate of change is critical. As the Deidu Cliff shows, the point isn’t that it happens slowly, but all at once.
Banking offers many examples of the market definition changing. For example, many Asian companies and firms are targeting customers who traditionally have no bank account. Grab, an Indonesian company similar to Uber is dragging in a whole new set of customers to banking as a side effect: the money passengers pay on their credit cards must be deposited somewhere. In China, new banks like WeBank are pursuing the estimated 225 million Chinese adults who haven’t traditionally been part of the banking market—the so-called “underbanked.”
In each of these cases, as with the introduction of the iPhone, the market is being redefined. A new Deidu Cliff is forming that can be tracked and monitored. Strategy groups can then determine when their company should begin pursuing these new customers, hopefully before they are snatched up by competitors and new banks like WeBank.
As Deidu’s analysis of the fall of Microsoft Windows shows, spotting shifts in the very definition of your market is key. Ideally, you want to create the shift. If not, you want to enter the market as soon as the shift is validated, as early as possible, even if the new entrant has single-digit market share. Deploying your corporate resources (time, attention, and money) often takes multiple years despite the “overnight success” myths of startups.5
Timing is everything. Nailing that, per industry, is fraught, especially in highly regulated industries like banking, insurance, pharmaceuticals, and other markets that can use regulations to...uh, artificially bolster barriers to entry. Don’t think that high barriers to entry will save you, though: Netflix managed to wreak havoc in the cable industry, pushing top telcos even more into being dumb pipes, moving them to massive content acquisitions to compete.
In addition to creating all these delightful charts—after all, if there are no charts in a corporate strategy deck, was a deck really created?—strategy groups also need to understand what their organization’s product and services are being hired for: the job to be done.
Know Your Customer
Measuring what your customer thinks about you is difficult. Metrics like Net Promoter Score (NPS) and churn give trailing indicators of satisfaction, but they won’t tell you when your customer’s expectations are changing, and with that, the market.
You need to understand how your customer spends their time and money and what “problems” they’re “solving” each day. For most strategy groups, getting this hands-on is too expensive and not in their skill set. Frameworks like customer journey mapping can systematize this research, as we’ll examine shortly, using a small-batch process to implement your application, allowing you to direct strategy by observing how your customers actually interact with your business day to day.
Case Study: “The Front Door of the Store Is in Your Pocket”—Home Depot
In the ever-challenging retail world, The Home Depot has managed to prosper by knowing its customers in detail. The company’s omnichannel strategy provides an example. Customers expect “omnichannel” options in retail, the ability to order products online, buy them in-store, order online but pick up in-store, return items from online in-store—you get the idea. Accomplishing all of those tasks seems simple from the outside, but integrating all of those inventory, supply-chain, and payment systems is extremely difficult. Nonetheless, as Forrester has documented,6 The Home Depot’s concerted, hard-fought work to get better at software is delivering on its omnichannel strategy: “[a]s of fiscal year 2018, The Home Depot customers pick up approximately 50% of all online orders in the store,” and the company has seen a 28% growth in online sales.7
Advances in this business have been fueled by intimate knowledge of The Home Depot’s customers and in-store staff by actually observing and talking with them. “Every week, my product and design teams are in people’s homes or [at] customer job sites, where we are bringing in a lot of real-time insights from the customers,” said Prat Vemana, then The Home Depot’s chief digital officer.8
The company focuses on customer journeys, the full, end-to-end process of customers thinking, researching, browsing, acquiring, installing, and then using a product.9 For example,10 to hone in on improving the experience of buying appliances, the product team working on this application spent hours in stores studying how customers bought appliances. It also spent time with customers at home to see how they browsed appliance options. The team also traveled with delivery drivers to see how the appliances are installed.
Here, we see a company getting to know on an intimate level its customers and the problems those customers have. This leads to new insights and opportunities to improve the buying experience. In the appliances example, the team learned that customers often wanted to see the actual appliance and would waste time trying to figure out how they could see it in person. So, the team added a feature to show which stores had the appliances they were interested in, thus keeping the customer engaged and moving them along the sales process.
Spanning all these parts of the customer journey gives the team research-driven insights into how to deliver on The Home Depot’s omnichannel strategy. As customers increasingly begin their research on their phone, in social media, go to the store to browse, order online, pick up in-store, have items delivered, and so forth, many industries are figuring out their own types of omnichannel strategies.
All of those different combinations and changing options will be a fog to strategy groups unless they begin to get to know their customers better. As Allianz’s Firuzan Iscan puts it:
When we think from the customer perspective, most of our customers are hybrid customers. They are starting in online, and they prefer an offline purchasing experience. So that’s why when we consider the journey end to end; we need to always take care of online and offline moments of this journey. We cannot just focus on online or offline.
Corporate strategy didn’t sign up for this
The level of study done at The Home Depot may seem absurd for the strategy team to do. Getting out of the office may seem like a lot of effort, but the days spent doing it will give you a deep, ongoing understanding of what your customers are doing, how you’re fulfilling their needs, and how you can better their overall journey with you to keep their loyalty and sell more to them. Also, it’s a good excuse to get out of beige cubicle farms and dreary conference rooms. Maybe you can even expense some lunches!
As we’ll see, when the product teams building these applications are put in place, strategy teams will have a rich source of this customer information. In fact, product teams will often be driving so much product and portfolio strategy that traditional corporate strategy groups will need to reimagine their role. This might seem like a loss of power to the strategy groups, but I believe we’ll see it enhance and grow the importance of corporate strategy, enabling them to focus on larger concerns.
The continuous stream of validated data will give strategists a huge competitive edge over competitors lacking that stream. The benefits of scale will also be amplified, helping existing organizations compete with startups. For example, imagine data collected from tens of millions of banking customers globally versus a mere million customers in limited markets. This is the kind of strategic power companies like Amazon wield to deadly effect: they know the market and the competitive cracks better than most.
If you’re working on corporate strategy, you should be salivating at the analysis and recommendations you can create. Getting there might seem like an endless series of leaps, though. We discuss one of the easiest, initial leaps next: listening to those people yelling and screaming doom and disruption.
In Western mythos, Cassandra (Figure 3-2) was cursed to always have 100% accurate prophecies but never be believed. For those of us in the tech industry, cloud computing birthed many Cassandras. Now, in 2020, the success of the public cloud is indisputable. The on-premises market for hardware and software is forever changed. Few believed that a “book seller” would do much here or that Microsoft could reinvent itself as an infrastructure provider, turning around a company that was easily dismissed in the post-iPhone era.
Despite this, as far back as 2007, cloud Cassandras were pointing out that software developers were using Amazon Web Services (AWS) in increasing numbers. Early on, RedMonk made the case that developers were the kingmakers of enterprise IT spend. And, if you tracked developer tastes, you’d see that developers were choosing the cloud because requisitioning basic infrastructure, like servers, took much longer to do within traditional IT organizations. More Cassandras emerged over the years as the cloud’s market share grew. Traditional companies heard these Cassandras, some eventually acting on the promises.
Finally, traditional companies took the threat seriously. In 2010, it seemed like everyone was in the public cloud market. But as Charles Fitzgerald wickedly chronicled, it was too late. As Figure 3-3 shows, entering the public cloud market at this stage would cost hundreds of billions of dollars, each year, to catch up.11 By 2019, there were just six cloud providers in the Magic Quadrant, with the second tier trailing far behind the triumvirate Amazon, Microsoft, and Google. The traditional companies in the infrastructure market failed to sense and act on The Cliff early enough—and these were tech companies—you know...the outfits that are supposed to outmaneuver and outsmart the market!
Now, don’t take this to mean that these barriers to entry are insurmountable. Historically, almost every tech leader has been disrupted. That’s what happened in this market. There’s no reason to think that cloud providers are immune. We just don’t know when and how they’ll succumb to new competitors or, like Microsoft, need to reinvent themselves. What’s important, rather, is for these companies to properly sense and respond to that threat.
To consider Cassandras, you need a disciplined process that looks at year-over-year trends, primarily how your customers spend their time and money. Mary Meeker’s annual slide buffet is a good example: where are your customers spending their time? A single-point-in-time Cassandra is not helpful, but a Cassandra that reports at regular intervals gives you a good read on momentum and when your market shifts. Analyzing year-over-year trends will give you more confidence in Cassandras. A single point in time might just be lucky—the metaphoric room of randomly typing monkeys that will eventually write Hamlet. You want to discover Casandras with a good track record and putting together year-over-year data will help. Trend data will also build up momentum, allowing you to find the best time to act (not too early, not too late), the market window.
Finally, putting together your own Dediu Cliff can self-Cassandra-ize you. Doing this can be tricky because you need to imagine what your market will look like—or several scenarios. You’ll need to combine multiple market share numbers from industry analysts into a Cliff chart, updating it quarterly. Having managed such a chart, I can say it’s exhilarating, especially if someone else does the tedious work!
Later on, as we’ll see in the leadership section, we can use these charts to fuel a sense of urgency and helpful crisis creation.
Thus far, our methods for sensing the market have been research, even “assume no friction” methods. Let’s look at the final method that relies on actually doing work and then how it expands into the core of the new type of strategy and breaking the business bottleneck.
Try New Things
The best way to understand and call market shifts is to actually be in the market, as a customer and as a producer. Being a customer might be difficult if you’re, for example, manufacturing tractors, but for many businesses, being a customer is possible. It means more than trying your competitor’s products. To the point of tracking market redefinition, you want to focus on the Jobs to Be Done, problems customers are solving, and try new ways of solving those problems. Often, this also means discovering problems they’re solving that your company has never considered solving, let alone known about. If this sounds like it’s getting close to the end goal of innovation, it’s because it is: but doing it in a smaller, lower-cost and lower-risk way.
For example, if you’re in the utility business, become a customer of in-home Internet of Things (IoT) devices and how that technology can be used to steal your customer relationship, further pushing your business into a commodity position. In the PC market, some executives at PC companies made it a point of pride to never have tried, or “understood” the appeal of small screens; that kind of willful, proud ignorance isn’t helpful when you’re trying to be innovative.
You need to know the benefits of new technologies, but also the suffering your products cause. There’s a story that management at US car manufacturers were typically given a company car and free mechanical service during the day while their car was parked at the company parking lot. As a consequence, they didn’t know firsthand how low quality affected the cars. As Nassem Talab would put, they didn’t have any skin in the game...and they lost the game. Regularly put your skin in the game: rent a car, file an insurance claim, fill out your own expenses, travel in coach, and eat at your in-store delis. Eat your own dog food, as Google and others would put it, or drink your own champagne if you prefer puffy vests over toe-shoes.
The key to trying new things is to be curious, not only in finding these things, but in thinking up new products to improve and solve the problems you are experiencing first hand. The smaller batch model and a shorter financing cycle should make it easier to try new things instead of just spending all of your attention on big bets. Large, multiyear projects “inhibit the introduction of new products because the bar is so high in terms of what we’re willing to bet on. The stakes are just too high,” explains Intrado’s Thomas Squeo. “Nobody wants to spend all that time and money on a new product only to find it is obsolete by the time it reaches production.” If that model is removed, you can do a series of small strategic bets, instead.
The goal of trying new things is to experiment with new products, using them to direct your strategy and way of doing business. If you have the capability to test new products, you can systematically sense changes in market definition. Tech companies regularly float new ideas as test products to sense customer appetite and, thus, market redefinitions. If you’ve ever used an alpha or beta app, or an invite-only app, you’ve played a part in this process. These are experiments, ways the company tries new things. We laud companies like Google for their innovation successes, but we easily forget the long list of failed experiments. The website killedbygoogle.com catalogs 171 products that Google killed.12 Not all of these are “experiments,” some were long-running products that were killed off. Nonetheless, as soon as Google sensed that an experiment wasn’t viable or a product no longer valid, the company killed it, moving on.
When it comes to trying things, we must be very careful about the semantics of “failure.” Usually, “failure” is bad, but when it comes to trying new things, “failure” is better thought of as “learning.” When you fail at something, you’ve learned something that doesn’t work. Feeling your way through foggy, frenetic market shifts requires tireless learning. So, in fact, “failing” is often the fastest way to success. You just need a safe, disciplined system to continually learn.
“We have a hard time thinking failure is positive,” says Liberty Mutual’s Pierre Braganza. “We have a hard time feeling like ‘if I fail, it was good.’ And, then, what we’ve learned is that failure is excellent if you can fail fast, learn from it, and make a change.” But, you need to create the right kind of culture for beneficial failure. “We have to give an environment where we give that ability to fail fast,” Braganza goes on, “The trick is, you do it often enough that it’s not a big deal.”
Validation with Small-Batch Strategy
Innovation requires failure. There are few guarantees that all that failure will lead to success, but without trying new things, you’ll never succeed at creating new businesses and preventing disruption. Historically, the problems with strategy has been the long feedback cycles required to tell you if your strategy “worked.”
First, budgets are allocated annually, meaning your strategy cycle is annual, as well. Worse, to frontload the budget cycle, you need to establish your strategy even earlier. Most of the time, this means the genesis of your current strategy was two, even three years ago. The innovation and business rollout cycles at most organizations are huge. Usually a year, but it can be even worse: five years, if not 10 years in many military projects. Clearly, in “fast-moving markets,” to use the cliché, that kind of idea-to-market timespan is damaging. Competing against companies that have shorter loops is key for organizations now.
Your first instinct might be to start trying many new things, creating an incubation program as a type of beta-factory of your own. The intention is good, but the risks and costs are too high for most large organizations. Learning-as-failure is expensive and can look downright stupid and irresponsible to shareholders. Instead, you need a less-costly, lower-risk way to fail than throwing a bunch of things at the wall and seeing what sticks.
As mentioned at the beginning of this report, and worth reviewing, many organizations are using the small-batch cycle, which we revisit in Figure 3-4. This is a feedback loop that relies on four simple steps:
Identify a problem to solve
Create a theory of how to solve the problem
Validate this theory by trying it out in real life
Analyze the results to see whether the theory is valid
This is, essentially, the scientific method. The Lean startup method and, later, Lean design have adapted this model to software development. This same loop can be applied “above the code” to strategy. This is how you can use failure-as-learning to create a validated strategy and then start innovating like a tech company.
Liberty Mutual’s Chris Bartlow describes the core benefit of small batches:
When you get to the stoplight on the circle [the end of a small-batch loop] and you’re ready to make a decision on whether or not you want to continue, or whether or not you want to abandon the project, or experiment [more], or whether you want to pivot, I think [being hypothesis driven] gives you something to look back on and say, “okay, did my hypothesis come true at all? Is it right on or is it just not true at all?”
As described earlier, due to long cycles, most corporate strategy is theoretical; at worse, it’s PowerPoint arts and crafts with cut-and-paste from a few web searches.13 The implementation details can become dicey, and then there’s seeing whether customers will actually buy and use the product. In short, until the first customer buys and uses the “strategy,” you’re carrying the risk of wasting all your budget and time on this strategy, often a year or more.
That risk might pay off, or it might not. Not knowing either way is why it’s a risk. A type of corporate “double up to catch up” mentality adds to the risk, as well. Because the timeline is so long, the budget so high, and the risk of failure so large, managers will often seek the biggest bang possible to make the business case’s ROI “work.” Taking on a year’s time and $10 million budget must have a significant pay off. But with such high expectations, the risk increases because more must be done, and done well. And yet, the potential downside is even higher.
This risky mentality has been unavoidable in business for the most part—building factories, laying phone lines, manufacturing, and so on require all sorts of upfront spending and planning. Now, however, when your business relies on software, you can avoid these constraints and better control the risks.
A small-batch process focuses on shorter release cycles and incremental changes to the application. This means that you get almost constant feedback on the validity of your strategic theories. It also means that you can more quickly sense and respond to changes in customer behavior and market shifts. “[I]t significantly reduces the business risk,” Discover Financial Service’s Ying Zhe explains, “When you start small, you build smaller chunks—it’s much easier for you to build, for you to test, and also for you to validate.”
Done well, software costs relatively little and is incredibly malleable. It’s, as they say, “agile.” You just need to connect the agile nature of software to strategy. Let’s look at an example.
Case Study: Most Viable Strategy: Duke Energy Validates RFID Strategy14
As an energy company, Duke Energy has plenty of strategizing to do around issues like disintermediation from IoT devices, deregulation, power needs for electric vehicles, and improving customer experience and energy conservation. Duke has a couple years of experience being cloud native, getting far enough along to open up an 83,000-square- foot labs building housing 400 employees working in product teams.
They’re applying the mechanics of small batches and Agile software to their strategy creation. “Journey teams” are used to test out strategies before going through the full-blown, annual planning process. “They’re small product-type teams led by design thinkers that help them really map out that new [strategic] journey and then identify [what] are the big assumptions,” Duke’s John Mitchell explained. After they’re identified, the journey teams test those assumptions, quickly proving or disproving the strategy’s viability.
Mitchell gives a recent example: labor is a huge part of the operating costs for a nuclear power plant, so optimizing how employees spend their time can increase profits and the time it takes to address issues. For safety and compliance reasons, employees work in teams of five on each job in the plant, typically scheduled in hour-long blocks. Often, the teams finish in much less than an hour, creating spare capacity that could be used on another job.
If Duke could move those teams to new jobs more quickly, they could optimize each person’s time. “So the idea was, ‘How can we use technology?’” Mitchell explains. “What if we had an RFID chip on all of our workers? Not to ‘Big Brother’ check in on them,” he quickly clarifies, but to better allocate the spare capacity of thousands of people. Sounds promising, for sure.
Not so fast, though, Mitchell says: “You need to validate, will that [approach] work? Will RFID actually work in the plant?” In a traditional strategy cycle, he goes on, “[You’d] order a thousand of these things, assuming the idea was good.” Instead, Duke took a validated strategy approach. As Mitchell says, they instead thought, “let’s order one, let’s take it out there and see if it actually works in plant environment.” And, more important, can you actually put in place the networking and software needed: “Can we get the data back in real time? What do we do with data?” The journey team tested out the core strategic theories before the company invested time and money into a longer-term project and set of risks.
Key to all this, of course, is putting these journey teams in place and making sure they have the tools needed to safely and realistically test out these prototypes. “[T]he journey team would have enough, you know, a very small amount of support from a software engineer and designer to do a prototype,” Mitchell explains. “[H]opefully, a lot of the assumptions can be validated by going out and talking to people,” he goes on, “and, in some cases there’s a prototype to be taken out and validated. And, again, it’s not a paper prototype—unless you can get away with it—[it’s] working software.”
After the strategic assumptions are validated (or invalidated), the entire company has a lot more confidence in the corporate strategy. “Once they ... validate [the strategy],” Mitchell explains, “you’ve convinced me—the leader, board, whatever—that you know what you’re talking about.”
1 For a better treatment of strategy, I found the history of management consulting, Lords of Strategy (Harvard Business Review), incredibly good. Understanding Michael Porter (Harvard Business Review) will lay a good foundation, and then I’d just follow Ben Thompson for an ongoing stream of contemporary, strategy entertainment. You could also get an MBA, but that seems like a lot of work.
2 Consumer Financial Protection Bureau, “The Consumer Credit Card Market” (December, 2017).
4 There’s a pure, “better mouse trap” argument to be made that Ben Thompson and others have suggested, as well. For many years, Microsoft, Nokia, Google, and even Blackberry knew that mobile was going to be big and had been executing on those assumptions. It just turned out that Apple had a better product: for years, “design” was heralded as the key driver for business improvement based on Apple’s success.
5 Also worth looking into is Rita McGrath’s model and set of tactics for detecting and responding to market shifts in business’s “arena,” as she renames the “market.” They’re covered in her recent book Seeing Around Corners (Houghton Mifflin Harcourt).
7 The reality and attention to this technology-driven strategy at the corporate level is indicated not only by its success, but also by the planned budget for The Home Depot’s omnichannel strategy: $5.4 billion over three years. On this topic, see Suman Bhattacharyya’s coverage of The Home Depot’s omnichannel strategy.
9 As you’ll notice in much of the discussion around digital transformation, DevOps, design, and the like focusing on end-to-end processes is a common tactic. For example, DevOps-minded IT departments use value stream maps to discover all the wasteful activities that can be eliminated or automated.
11 “In 2018, Amazon spent $27.6 billion, Google $25.1 billion, and Microsoft $15.8 billion on CAPEX (including capital and build-to-suit leases for both Amazon and Microsoft). Those are year-over-year increases of 19%, 91%, and 39%, respectively (Google went nuts on CAPEX in 2018).” And, further: “The three companies’ cumulative CAPEX spend since 2000 is over $270 billion, with over $116 billion of it in the last two years.” “Follow the CAPEX: Cloud Table Stakes 2018 Edition,” Charles Fitzgerald, February 2019.
13 As a quick-and-dirty sniff test, count how many references and primary data sources in strategy presentations are from industry analyst press releases (there was no funding to buy the actual reports) and other free market analysis like McKinsey, BCG, and the other consulting firms. Marketing can, and often does, lead strategy.