Expanded notes from Chicago Booth Alumni Strategic Management Practices Issues monthly Group Discussion of selected topics and supporting articles
Successful brands have always found influencers and celebrities helpful in selling their product. Sometimes brands embed messages hoping to ride the waves of cultural change all the way to the bank. But when the winds change suddenly, how a brand navigates the wave impacts its survival. Continue reading Brands and Politics, can the mix lead to healthy business growth?
Change, it’s what most business executives recognize as a constant threat. Unfortunately, many find themselves between a rock and a hard place. How might managers ably meet tomorrow’s change in environmental conditions?
Rather than react, plans that consider structure and processes matter. So too does understanding their organization as an integrated and dynamic whole.
Factors that create efficiency and make an organization and its team effective today appear less stable all too quickly. The growing connectivity and access to information affects the pace and scale of technology developments, consumer expectations and competition.
An organization’s survival depends on its ability to adapt and respond to external changes in its environment as well as introduce changes internally.
The connections between strategy, structure and processes not only prove defining for many organizations but also the source of pain or promise to its survival.
Participants in the Strategy discussion took a look in July 2017 at a few articles (posted at the end) that focused on decision-making challenges that make organizations struggle to adapt. Here’s some of what we discussed.
People adapt to expected change by planning. A strategy clarifies expectations making both planning and adapting to change easier. Strategy when well communicated and understood articulates the underlying purpose that drives an organizations’ leadership to make a change. Strategy in this sense, is a choice of action affects the quality and effectiveness of changes and degree the changes stick.
Management theory differs from strategy, though it too creates expectations that make planning and adapting to change possible. Management theory determines the organizations’ structure and processes.
Not every change that occurs is expected, and not every expectation gets realized. Both situations require adaptation but few organizations prove capable consistently of decision-making that continuously holds their competitive advantage.
Why one business proves more resilient, and/or adaptive than others continues to elude the best managers and leaders. Cause and effects are complicated to identify let alone sort out.
Nearly fifty years ago,Raymond Miles, Charles Snow, Anal Meyer and Henry J. Coleman, Jr –four management academics set out to understand the process business use to adapt. They looked for patterns in order to prescribe how existing managers might successfully meet tomorrow’s or different environmental conditions. Their framework remains relevant and informative because it encourages management to see their organization as integrated and dynamic wholes.
For a small, or start-up business this perspective mirrors the intensity of collective singular focus on survival. Once secured, the collective focus naturally diffuses. Accommodating growth each functional role establishes separate priorities that over time compete rather than integrate their resources.
In 1978, existing descriptions of organizations’ ability to adapt were lacking. The authors of Organizational, Strategy structure and process used their Organizational Behavior and Industrial Relations backgrounds to inform and then describe three categories of processes: entrepreneurial, engineering and administrative.
Miles et al break down basic decision making for managers into three categories: administrative, engineering and entrepreneurial. Organizations’ top management limits their choices of adaptive behavior to those they believe allow them effective direction and control of human resources. They observed that whatever theory of management executives endorsed factor significantly in their subsequent analysis of organizations’ ability to adapt to changes in the environment.
Their preliminary research uncovered a series of patterns relating management theory and organizational strategy and structure. Fifty years later, these relationships remain evident though more patterns and interpretations and management theories have emerged. Note, this article predates the work of Michael Porter’s industrial economics inspired descriptive five forces theory of Competitive Strategy.
The evolution of expectations between managers and employees are three components of the patterns based on
In light of the attention currently paid to organizational culture, their observations prove interesting, especially as nowhere in this article does the word even appear. That’s not to say they overlooked this concept. The authors merely draw the attention of management to individual behaviors and expectations with a prescriptive suggestion for their evolution from a traditional model to a human relations and a human resources model.
Perhaps culture issues playing out in many organizations reflect challenging dynamic patterns these authors identify as: Defenders, Analyzers, Prospectors and Reactors.
Here’s my crude descriptive summary
| Defenders | Analyzers | Prospectors | Reactors | |
| Innovation | Low | Moderate | High | Low |
| Efficiency | High | moderate | low | low |
| Centralized Decision-making | High | Low | ||
| P&L autonomy | Low | High |
As you can see, instead of finding a direct alignment between patterns of decision making associated with different management theories and the ability to adapt, the authors found a mix of assumptions, policies and expectations.
Holding together a dominant coalition with mixed views concerning strategy and structure is not an easy task.
Miles et al found managers engaged in new product or service development struggled to function within planning, control and reward systems established for more stable operations. Analyzer organizations must be successfully differentiated into its stable and changing areas and managed accordingly. ….Numerous organizations either led or forced into a mixed strategy (multinational companies, certain forms of conglomerates, many organizations in high technology industries) and their struggles may well produce a new organizational type and demands for a supporting theory of management.
To meet tomorrow’s environmental conditions successfully, manager’s must ably understand organizations as integrated and dynamic wholes.
Here’s Shep’s very cogent reply:
Whether a financial statement analysis, or perhaps a broader 10K, which adds additional content to the financial statements would enlighten the analyst on where the reporting company is in the spectrum described in the article.
First a comment on the article, and then maybe I can shed some light on an answer:
The article categorizes the adaptive capacity of companies more or less as follows:
| Low Flexibility | High Flexibility | |
| High Stability | Defender | Analyzer |
| Low Stability | Reactor | Prospector |
It goes further into its definition by stating that each company must bring some balance to bear on three main problems: entrepreneurial, engineering, and administrative. Finally, the authors point out that managerial theories (and their associated practices) held in high esteem by top management will enhance or confound the company’s ability to adapt.
The main points I [Shep] get from the article are:
What their commentary brings to mind as good examples:
What can you learn from a 10K?
A little financial forensics can go a long way.
Alignment of Strategies
http://smallbusiness.chron.com/alignment-strategies-4649.html
Organizational strategy, structure and process
The Academy of Management Review, July 1978
http://www.wiggo.com/mgmt8510/Readings/Readings5/miles1978amr.pdf
How aligned is your organization
https://hbr.org/2017/02/how-aligned-is-your-organization
OPTIONAL
If you need a focal point to think about the question, consider the activist investor LOEB’s stake in Nestle
Nestle: Third Point Daniel Loeb Activist Investor
The business headlines have been full of news about store closings. Multiple chains are closing 100s of stores. This once flourishing sector employed thousands, and now flounders. What strategic lessons can be learned from US retailers mistakes? The question and the learning opportunities for other sectors became the basis of the April 21, strategy roundtable’s diverse group of attendees. A few articles offered some summary context of the state of the sector, a little history about Macy’s and a series of growth related insights from disruptive innovation strategy firm , published in MIT Sloan Review offered by Fahrenheit212.
Surprisingly, after posting the articles in late March, a flood of store closings and their back stories continue to appear, leaving many to wonder what’s up for several of the biggest brand names in retail. In the short horizon that Macy’s lost $10billion in value, Amazon rose $300bil to reach its current six year low of $6bn, considerably off its peak of $24bn in 2006, Amazon’s current valuation just north of $370bn continues to accumulate market share.
The US Big Box Retailer’s current state isn’t just bad luck and it turns out to be less than easy to pinpoint. As Macy’s and other retailers struggle to maintain a fresh look, their poor performance ripples across the sector as one domino topples over another. No excuse for Management to become such terrible shop keepers, no matter how much online competitors divert their attention. A poorly appealing store worsens its merchandise and thin staffing ratios erode service combine to turn off customers too.
Sears posted year on year decline over 9 percent, and 1.5x losses for its mobile application as it’ digital transformation efforts prove insufficient.
Cost cutting may help a business survive, but won’t deliver growth that makes it thrive. The creative fuel behind the early growth of Sears, Macy’s and the general merchandisers also expanded their market and showed their competitive capabilities. Amazon isn’t a new disruption and the blanket of doom seems to spread uniformly across the sector.
Did you know from 1990 to 2014, US retail employment grew by 2.3 million, of which the vast majority was among non-store retailers? The total sector grew by 17 percent, while non-store employment grew 27 percent. In this same period, a combination of productivity gains and drops in labor compensation reduced the sector’s unit costs.
These reversals in historic trends pointed out by Chicago Booth faculty Chad Syverson and Ali Hortaçsu in their recent review of US retail, should help large retailer’s right? More part-time workers, more automation and lower wages do improve operating margins, but doesn’t mean growth will follow.
The bigger strategic problem is timing and the ability to compete. The efficiency gains across the sector appear just as retail’s share of total US economic activity continues to shrink, and correspondingly its share of total US employment diminishes too.
Syverson and Hortacsu found ample research that relates technology, management, variety and productivity with shaping the success and survival of some retailers. Surprise, greater productivity may or may not result from any one of these factors, and growth from greater productivity seems less causal too. Physical operations, however do prove important to both e-commerce and store based retailers. In other words, the formula for growth has grown more complex. Hold these thoughts.
Many explanations of changes in the sector, they say, “ …build on two, powerful and not fully consistent narratives, a prediction that retail sales will migrate online and physical retail will be virtually extinguished, and a prediction that future shoppers will almost all be heading to giant physical stores like warehouse clubs and supercenters.”
Their extensive data review also makes clear that the data does not support these narratives. Online retail supremacy has not yet arrived, and likewise the scale and influence of the supercenter/Warehouse merchandisers continue to grow.
“Between 2000 and 2014, the fraction of all retail sales accounted for by e-commerce has risen steadily from 0.9 to 6.4 percent…The increasing share reflects an 11-fold increase in nominal annual e-commerce sales, in contrast to a 55 percent increase in nominal retail sales as a whole.”
The latest (4Q2016) US Census reports total retail sales of goods and services at $1,235.5Billion and estimates ecommerce at $102.7Billion (8.3 percent of total). Year over Year growth rates of 4.1 overall were considerably lower than ecommerce, which grew by 14.3 percent. Both grew only 1.9 percent over 3Q2016.
The analysis by category over time and projection of the trend lines summarized below makes the story more interesting.
Syverson and Hortacu’s Projected year that product’s
Table I: Product-specific E-commerce share expected (purple achieved): as a Share of Product Total Sales
The summary resembles an 80/20 analysis. The categories with the biggest ecommerce sales represent the smallest category of total sales.
A recent edition of MIT Sloan Review published three “new” growth-related truths, and framed our discussion. The truths as you see, resonate with many of the common precepts around innovation focused strategy not just those of its authors from Fahrenheit212, part of Deloitte.
First, analysis has broadened in many ways, but its purpose and practice still largely determined by Leadership and its management priorities.
Limiting the scope of Analysis to tactical issues, confirms what works, or drives larger strategic projections as in what’s possible. Today, analysis and analysts review data in every function across the entire enterprise. Organization benefit from analysis when they also commit to standards of consistency and integration, that also assure their results don’t confuse but reveal factors important to business growth.
Successful analysis also relies on the availability of data and analysts capable of its interpretation. Today’s connected world offers ever increasing opportunities to collect, store and process more data cheaply, and Enterprise Resource Planning Software systems greatly simplify and automate the reporting of standard views of activities in every function.
Planning and Process Improvements both suffer from a shortage of analysts capable of integration and interpretation of big data within a business context. The standards for business reporting reinforce old habits, rely on established metrics and existing interpretations, and thus miss cross-functional opportunities to share findings and develop new insights.
Is perspective and Interpretation hard to come by, or just hard to hear?
For example, to reach $150 million in annual sales, took Walmart 12 years and 78 stores. From its inception in 1994, Amazon took only three years. Further, Bezos reported 1998 net income remained close to zero as he his continuous focus on growth tirelessly plowed cash back into business development. Not only did Amazon’s sales success patterns defy conventions of growth metrics, their unconventional use of data, analysis supports their creative capabilities and discoveries to understand what was contributing to their growth and working for their customers too.
In 1999, Forrester Research reported annual web retail sales as a whole jumped from $700 million to $20 billion, though it remained less than 1% of total retail sales. Growth was anything but linear—but the base too small to catch the eye of established, experienced retailers.
In 2001, Border’s CEO Greg Josefowicz was a very experienced and sophisticated retailer and no stranger to Ecommerce having come from Peapod. The fractional contribution of the chain’s online sales however led him to outsource the channel to Amazon. This was the same year, that Apple released the iPod. Unless you were closer to online data, and keenly understood its opportunity to track customer journeys and gain behavioral insights, chances are you too would have overlooked the value of further investment.
A 2016 interview with Michael Edwards, interim CEO Borders from January 2010 through its bankruptcy and liquidation in July 2011 revealed something else. Edwards buys into Fahrenheit212 philosophy that little can prepare you for wholesale disruption. 2010 was a period of widespread economic growth and US retailers sales were growing, but not uniformly; and not if you were in the book and electronics industry (aka ESMOH)—again hold that thought.
“The pivotal moment for me is when Apple launched the iPad,” Edwards said. “That foundationally changed the (book) industry forever.”Essentially, the iPad was a Borders in your hand. It had books, music and video. And people had access to millions of books.”
These hindsight claims made me wonder why Border’s didn’t feel any sales fallout from the iPod or Apple earlier, or when and why they misread Borders’ customers change in shopping patterns?
Is your analysis reporting monitoring activity or action oriented?
What analysis and shared insights did Borders leadership encourage? Were traditional metrics misdirecting their strategic priorities and explain how their widespread physical presence was suddenly without value?
Remember the dominant narrative that Syerson and Horascu found?
Put that thought together with the analysts’ tunnel vision driven by elaborate ERP systems that accurately report established growth metrics. Monitoring Same Store Sales, Sales by channel or category breakdowns do reveal changes in shopping patterns, but are they actionable? Even the Ecommerce reports from outsource vendor Amazon likely to include detail level data and helpful comparisons.
Different stories and trends emerge when analysis incorporates outside reference points. Benchmarking internal data to publicly available government statistics, for example, not just aggregate retail sales vs ecommerce but within their category might have raised alarm bells early. Time pressures and priorities don’t have to stop anyone from creating a look similar to Syerson and Horascu cumulative look.
What are you using to define your market and meet the needs of your customer?
Rethinking how to deal with consumers is more than a marketing plan it’s a strategic imperative.
At least that’s what Mike Edwards realized when he stepped up from the role of Chief Marketing Officer to help turnaround Border’s in 2010.
Conventional analysis techniques and formats don’t address deeper questions that test the validity of your strategy, or draw attention to important indicators affecting your results.
If you are a big retailer, and you moved online, you have big data. Macy’s and Sears both moved somewhat early to create an online presence before 2001. Maybe they saw online as an efficiency improvement to catalog sales, they still kept them independent of ongoing business activities.
Perhaps their experience relied too heavily on mass-market demographic information that large vendors like IRI made easy to digest. Capabilities to analyze the flood of big data and the detail byte size moves of website visitors exceeded the capabilities of the most nimble and agile of digitally born players.
In 1998, the year Google was released, Wired reported the evolving capability of a website to gain intimate knowledge of their visitors. Excite, the leading search engine at the time, collected 40 Gigabytes of data daily in its log files based on 28 million daily Page Views. They only tracked directional patterns, though “for the first time, the continuously updated empirical evidence needed to assess relationships and deliver better experiences was available.”
A gap emerged between traditional marketing training and opportunities the web’s detail user journey tracking revealed. Do you appeal to demographic and assembled personas, OR are you responding directly to individual users’ needs? This gap mirrored the unfolding of a larger competitive divide across all businesses and further segregate online activities into separate operating units.
Bigger organizations’ centrally controlled decision making contrasted sharply with the emergent capabilities of online technologies and few recognized the important tasks required to rethink how to deal with evolving learning by their consumers and suppliers. No problem for Excite, the leading search engine in 2001 and Amazon. “Any active data we get, [Joe Kraus, VP of Excite explained] we put to instant use on the page…simulating personalization such as zip-code based weather forecasts.” Amazon without knowing any personal information, began to pass on simple recommendations based on the cookie data.
Cookies track specific behavioral data online, that was difficult to connect to purchase and profits, but still offered considerable strategic insights to anyone who took the time to look. Ironically, only a handful of advertisers possessed the technical and marketing experience with this growing data, which meant the playing field for ably using the information to optimize profits was wide open. Instead of investing and experimenting, many continued to apply the store sales success criteria to online sales.
I’m no retailer, but I did learn a few things from my father who created a handful of custom drapery stores that flourished in the 50s and 60s only to succumb to changing demographics in the 70s.
Drawing customers in, attractive presentation of merchandise has always helped successful merchants move what had to be moved. It’s been true for sellers regardless of their circumstance and environment.
Every trend has an origination point, successful analysts recognize the significance early because they often understand change as relative. It’s easy to see the internet as a significant force today, but in the mid-1990s, analysis shown earlier documents the case’s weaknesses and risks.
In 1995, Grace Mirabella, former editor of Vogue broadens the context in her memoir In and Out of Vogue. She describes dramatic shifts in the minds of consumers about department stores’ relevance compared to their hey day in the post-war period. 22 years later, her words don’t sound the least bit out of date.
“[B]efore malls and discount outlets and chain stores…[department stores] were the great halls of merchandise, and they provided an enormous variety of goods at much more varied prices than the present. …each store aimed for a certain style, a certain specialty market, and a certain clientele, and you knew the minute that you walked into any one store, and smelled the perfume and saw the flowers and doormen or bargain tables, precisely where you were. “
Each family owned store’s attitudes and sensibilities she explains, accompanied the details that clued in customers, established unique contracts with manufacturers and made evident by the difference in merchandise they carried. In the 1970s, Mirabella remarked on two major shifts:
“[The named department stores] started to take on the feel of the real estate ventures that they had become. They lost their sense of purpose, of conviction.”[p. 45]
In 1994, Jeff Bezos, left his job as hedge fund manager for DE Shaw. Interviews reveal he spotted opportunity in the expanding internet, which led him to start the company he later names Amazon. His analysis skills suggest he was deeply familiar with another trend that began in the 1970s, one, that Mirabella in her backward look from her publishing perch misses–the evolution of Electronic Digital Interfaces (EDI) streamlining procurement.
In his first 1997 letter to shareholders, Bezos lays out his vision and writes:
“Today, online commerce saves customers money and precious time. Tomorrow, through personalization, online commerce will accelerate the very process of discovery. Amazon.com uses the Internet to create real value for its customers and, by doing so, hopes to create an enduring franchise, even in established and large markets.”
Personalization historically differentiated the high end of the market. Sales persons kept coveted black books that contained intimate notations about their customers ranging from size, color and style preferences to special occasion dates and family details. Amazon wasn’t the first to collect user data, and was by no means able to mine it and yet they produced “personal recommendations” beginning in 1998 without investing in developing complex analysis capabilities. That came considerably later.
They took a shortcut that other websites noticed satisfied customers. Chris Bayer writing about personalization for Wired in June 1998 explains it this way:
“The trick is to use technology to achieve the same economies that you have in a mass-marketing model, while delivering some personalized messages to the consumer,” says Rex Briggs of Millward Brown Interactive. A less visionary goal than one-to-one, surely, but far more realistic. It’s called mass customization, and if you can get past the oxymoronic bounce, you can see that its possibilities are not lost on the consumer-products retailers who have carved out a market for themselves on the World Wide Web.”
In 1999, Academics Joe Pine and James Gilmore publish The Experience Economy continues to shape many retailers strategic perceptions. Their thesis builds on the retailer narrative and emotions Mirabella evokes, and connects to my own Dad’s experiences as a lifelong retailer. Experience, they explain is now the metaphor of choice. What else summarizes the combination of factors that attract and convert a visitor into a loyal, frequent customer and/or influencer? Keeping experiences relevant and meaningful amidst the backdrop of rapidly changing forces that impact every aspect of your business model demands rethinking of the employee not just the customer experience.
Unlike the leading CEO retailers failing, Bezos shares more in common with the great merchandisers of the past. His digitally born and situated store front owes its business growth to continuous, bold experimentation as well as deep analysis. I don’t know what metrics are commonplace at Amazon, but their investments in data analysis capabilities and machine learning are self-evident by the efficiency and sideline cloud business they produced.
The speed in which consumers change their behaviors prove challenging for every retailer, non-store or store. The online e-tailers’ unique environment, fully equipped to capture detailed user journey references and history can use the same mechanics to deliver immediate responses ranging from mass personalization to levels of deeper customization.
Amazon’s strategy embraces the principles of continuous learning at its core to control every aspect of the buyer’s experience. Similarly Apple, another company with astronomical market valuations entered the retail market in order to control and enhance the buyer’s experience. Today, both have physical presence that emphasizes service and consumer education.
Retailers who miss the ability to construct a holistic strategy, increasingly are dying in the evolution of responses or deeper customized, delivering valuable feedback enabling the business to continually improve its offerings and willingly take risks associated with invention. Amazon learned quickly how to draw customers online, present the merchandise attractively and yes move what had to be moved. He didn’t have to balance the demands of managing existing outlets, nor accept established practices associated with large scale distribution networks, instead he invented his future.
In 20 years of online commerce, only a few companies strategy match Chris Bayer’s observation that “”serious” companies are rethinking the ways they deal with consumers, and the idea of mass customization ….using the trick of technology to deliver a personalized message that isn’t really personal at all.”
ARTICLES
Reframing Growth Strategy, Sloan MIT Review
http://sloanreview.mit.edu/article/reframing-growth-strategy-in-a-digital-economy/
Contrast in transition: Sears and Macy’s
https://centricdigital.com/blog/digital-strategy/how-sears-and-macys-are-transitioning-into-an-improved-digital-strategy/
Macy’s relationship trouble with Luxury brands
Five Trends driving traditional retail towards extinction
What’s consuming your time and energy these days?
What’s harder, coming up with a plan or the planning? Today, numerous automation technologies make modeling and planning easier and more widely accessible . In fact, automation is just one of the many values upgrading to digital technology tools offer.
This is not why, John Hagel of Deloitte, notably suggests organizations should look ten, if not twenty years out when when developing digital strategies. He found that of the 2% of companies who say their thinking makes use of truly long term horizon, they derive considerable benefit.
Yes, I swapped the word thinking for planning to be truer to Hagel’s observations. Technology changes’ inescapable link to organizations’ Financial Performance forces everyone to keep up, and that’s both good and bad, he recently told the MIT Sloan Management Review:
“[Hagel’s] sense that the old approaches are just not sustainable, and some senior executives have seen the need for fundamental change driven by this technology. The big issue for them is how to get that change to happen in a large traditional organization.”
Wait, he’s not talking about scenario planning, a worthy tool in the strategic tool box that proved advantage to Royal Dutch Shell with lengthy timelines for oil discovery and extraction. This contrasts with Digital technologies timetables, stuff that changes faster than anyone has been able to imagine.
This short term focused impact thinking Hagel and others suggest proves self-limiting and injurious to organizations.Particularly because both changing culture and enabling learning are time intensive activities and rarely get priority, and arguably leads to performance failures in too many organizations.
Global Positioning systems (GPS) technology, for example, everyone now recognizes removes the pain and uncertainty of time, distance and routing. The tools also deliver greater efficiency and optimize resource utilization. In 1973, when the US launched its first GPS satellite project, few organizations began to imagine how geolocation capabilities could enhance their business.
What did they have to understand? What were they waiting for? When did it become obvious and inevitable that Geolocation was an imperative in every business? I imagine thinking through this change might go something like this.
Built in navigation tools became available in the first mobile phones and improvement has been continuous; however the new opportunities and new businesses they have created were described as disruptive. The number of people who fully know or understand how these navigation tools work may be limited but didn’t stop them from successfully scaling and spreading. Geolocation has become so embedded in our lives that many of us feel lost without them. Likewise businesses like UPS, UBER or your local coffee shop depend on them to drive revenue, though only one may have incorporated the development and evolution in their long term planning.
Not everyone recognized how to turn the possibilities Geolocation enabled into successful business opportunities. The questions and issues associated with longer term technology changes were the focus of the January Chicago Booth Strategic Management practices discussion. The following summarizes some of the conversation exploring the merits of planning with exceedingly long horizons, such as 10-20 years versus the common place three to five years.
A few caveats.
The discussion did not reference methods for engaging in really long term planning, and all acknowledged that predicting the future was not really the point. Instead we relied on a few articles (see bottom of the post), participants previewed in advance, to set up the conversation that focused on the necessity and benefits of formulating digital strategy.
We discussed the relevance of the horizon itself, and its power to dictate the frame of reference , “fix” the focus of planning that misses the ripple effects. As one member explained, planning that focuses on the future, should also consider and think through how the choice and change in capabilities that your methods, processes and market dynamics preserve or inhibit your advantage.
In summary, we uncovered several strategic advantaged capabilities enjoyed by only a handful of organizations who do indulge in a holistic long term digital strategy planning. Take a look at what we discussed and tell us what you think.
If we use the example of automated on the ground navigation systems , in hindsight, it’s easy to imagine how many different industries and benefits would benefit from this capability. For example, the benefits to Triple AAA differ than those that would accrue to UPS. Imagine the steps in the plan that the former might have initiated. Digitize the original triptik and triple AAA could distribute route maps cheaper, better and faster, right? Nice plan, but much harder to execute.
The steps we can imagine might have been something like these.
Did you notice the subsystems that make this one idea easier to realize? There are many more than I’ve mentioned, and consideration for each of them expresses why planning takes so long and can easily get very complicated.
Re-use, that was one of the first lessons I learned when I began to code (or write the instructions that allowed the computers to read/write different inputs to generate specific outputs. use, change data into output. To code from scratch was just plain silly. Chances were exceptionally high, that someone before me had code that worked and could accomplish at least part of what I was trying to accomplish. It’s a simple lesson in systems too. Systems, like problems, when broken down into smaller parts become easier to manage when you see and understand how they work.
The plan, or the idea may be inspiring but as Dwight Eisenhower remarked, it’s nothing, as planning is everything.
The value of planning often exceeds the plan because the experience of thinking through the subsystems can be re-used. The experience when shared also builds trust in the method and in your people.
Too often the focus is placed on generating a plan, and then backing out the projects that when successful will produce the results desired. In a rapidly changing and dynamic environment, creating opportunities to challenge and improve our judgement coincide with opportunities to practice and strengthen competitive advantage by strengthening and promoting behaviors such as responsiveness, adaptiveness and risk taking.
Behavioral change, learning and knowledge acquisition take time and improve with practice. Notably it’s difficult to plan for disruptions, but we all accommodate and assimilate changes, the challenge is how to leverage this inevitability at an organizational level.
The obsession for clear return on resource investments compromises organizations’ long range planning or fully work through the possibilities of disruption and its effects. Similarly, few firms create the capacity and preserve time and talent permission for open experimentation and playing out multiple possibilities, such as Google’s Moonshot or Amazon’s generative experimentation. At the other end of the organizational spectrum, start-ups do take considerable risk to prove out their ideas only to change their focus and curtail their experimental capacity once they begin to scale.
So why do organizations continue to engage leadership in near horizon planning and even make use of social network technologies to broaden the input to include customers, employees and/or investors? Perhaps it’s a coping mechanism for the inevitability of change and a response to the dynamics of the marketplace while also garnering investor confidence, customer loyalty and employee engagement. The tools that permit prioritization or even selection are more often based on the ability of the organizations to cope as well as adapt to the suggestions.
Leadership obviously plays a role. Perspective and ability to manage, versus address, the continuum of known possibilities limits their risks and accountability to short term while excusing their failings.
If timing is everything, then dealing with the here and now, and near, minimizes risks and costs necessary for survival but fails to secure competitive advantage.
This folly of short term thinking preserves alternatives over pursuit of a specific scenario of indeterminate future.
It was hard to explain whether the size of the organization determined its immunity from the dangers of failing to engage in planning with a longer horizon. No one had any experience that suggested larger organizations feel the pains of change sooner, or prove more or less resilient, adaptive to long term planning.
All agreed that understanding ecosystems, the dynamics of change warrant every organization to think through how these forces affect their organization not just in the near term but also long term. More over leadership produces benefits when they increase adaptiveness and open mindedness.
Below are the articles we reviewed as the basis of our conversation. Got anything to add, or suggestions for where we might find cases or evidence of the impact of long term planning would be most welcome.
Predicting the future, how to engage in really long-term strategic digital planning
http://sloanreview.mit.edu/article/predicting-the-future-how-to-engage-in-really-long-term-strategic
In the age of Autonomous cars, can your organization also be driverless
http://www.strategy-business.com/blog/In-the-Age-of-Autonomous-Cars-Can-Your-Organization-also-Be-Dr
AI, Self-driving cars and cyberwar, the technologies to watch for in 2017
https://www.theguardian.com/technology/2016/dec/25/ai-self-driving-cars-and-cyberwar-the-tech-trends
Change is something we live with, in large part because when it happens we tend not to notice.
In the 16th century, a philosopher of diverse interests astutely observed change as a relative concept which as an observer in motion makes it difficult to observe. Change, when observed ultimately tests our understanding of time and I suggest adds meaning to much of our actions.
Change we make happen feels different than change that merely happens independent of our actions. What we make possible now becomes different than what’s expected and predictable.
Ever intentionally stare at something that over time gets closer or farther away? You know the object travels distance, yet it’s difficult to perceive or notice its movement. We perceive and understand change similarly, once we notice a difference we know change happened but unable to sense as it happens.
Traveling relative to moving fascinated Einstein too. December marked the 100th anniversary of Einstein’s publication of his theory of general relativity. Naming, and then expressing the idea, requires different capabilities than understanding or theorizing the concept.
Ever try to make someone else understand something you know? Did you only use language? Or did you also use pictures, gestures? Analogy, metaphor and story all help others understand what you know, but until you actually do what you describe your understanding won’t be sufficient.
Scientists experiment in order to test out an idea they think works. In business, customers test the worthiness, or value of what the business offers. In both Science and business, it takes more than knowledge to be willing to test the idea. That extra ingredient I lump into a category I call readiness and its inextricably linked to change.
Change, often defined as the difference between one point and another.
Point A Point B
Take a look, how do you perceive change? General relativity explains that space and time both move, not exactly together, but relatively which like change makes the concept difficult to grasp, let alone measure.
Time keeping, now standardized, makes everything we do on earth measurable by contrasting it to the standard of time. Knowing the amount of waiting time helps us manage our attention by creating an expectation. Standardizing time, allows us to plan and coordinate what we do for ourselves and with others.
For example, few people literally watch while waiting for a kettle of water to boil. The temperature of the water begins to change in the presence of heat. Reaching the boiling point proves to be a relative and meaningful change. Conversely, if the boiled water was used to brew tea or coffee, knowing that without sustained heat, the same water will cool.
We experience waiting, not as the transitional changes in the water’s temperature, but the time difference between the thresholds. Placing a thermometer in the water allows us to observe moment-to moment change in temperature but does little to change what we do while waiting. Similarly, the increasing number of installed sensors in the physical world make it possible to observe the invisible moment-to-moment changes occurring around us. Smart meters measure the amount of energy being consumed in a location, smart phones track distance traveled in greater detail than a car’s odometer.
What value does additional tracking offer? Traveling, or consuming energy doesn’t happen because we measure them, nor does the rate in which change happens once measured.
Ever smile? This change we make in our outward expression isn’t always a conscious action and yet we may first discover by observing the reaction chain our smile produces in others.
If you want to understand change, try thinking about it with respect to time. Actively trying to make a change happen helps us focus and notice the amount of time needed before the desired effect happens. Ever notice how long before others react to your smile, or until you feel water getting cold or getting warm? Feeling the change in sensation doesn’t require quantification, or precision. Time too seems irrelevant, beyond our ability to recall.
When describing invention, as in the mother of necessity, what does this imply about our needs and our existing capabilities?
Polarity, the idea of opposites, turns thoughts and possibilities around like a pendulum always moving from one extreme to the other. That is until the thought runs out of energy or momentum and stops. It rests until some force displaces it.
Recently, a client asked for help explaining the difference between planning for a transition and planning a transformation. Since Transformation seems to be one of the buzz words of the moment, I began to wonder what made the two thinking processes different, and what did they really mean. My polarity thinking friend suggested that transitions plan for certainty, or near certainty and transformation plan for uncertainty. I disagreed.
I think of a transition as the pause between takes, what happens between two clearly defined states. It’s when we assess, evaluate or figure out our position, how close or far. Transformation, that’s the feeling we have on arrival, we made it so now what.
In other words, if the client has a clear objective as in to take a specific distant hill, then transition plans incorporate the certainty that elevations will be changing on route and insures the team’s prepared for the journey. When it knows what changes to expect along the way, then it’s transition ready. Transformation focuses on arrival, different conditions and challenges it doesn’t know, but can imagine arrival makes possible. After all, isn’t that why the objective was to take the hill? Wasn’t it about the advantage that being on top offers?
Put another way, imagine what you want to do is known, like traveling to another location. Transitions focus on the journey, how long will it take, buying tickets or planning the route. Transformation planning asks how the change in location affects your current activities. Transitions are more whole body time shifts, where as transformation puts your head in the future while the rest of your physical body remains grounded in the present.
November’s topic for the monthly strategy discussion focused on Transformation Readiness. Before I managed to summarize the conversation and post notes,news about the sale of Mariano’s to Kroger caught my eye. and then I also spotted an interview with CEO, Bob Mariano on the Chicago Booth website.
If you are not familiar with Chicago, then let me explain that Mariano’s was a new entry into the grocery store business. By coincident, just as they had opened a few stores one of Chicago’s main competitors –Safeway decided to close all of its Dominick’s stores. This meant Mariano’s acquired 10 of the closed stores and their debt fueled expansion took off. That’s when Kroger came calling.
Since I had already been thinking about transformation questions , as in how do you get transformation ready, I thought it worth sharing these responses. Take a peek, and let me know what you think, are the example transitions or evidence of transformation readiness?
Scenario A: I think that Mariano’s namesake, CEO and founder, Bob nailed it when he said:
“At Mariano’s, we tried to push further. We continue to push. What I mean by push is to expose the customer to different and unique things and allow them the opportunity to tell you, ‘No, I don’t like that,’ or ‘Yes, I like that.’”
Scenario B: Or maybe you prefer the spin by CEO of Shazam when asked about the increasing gap between growth in the amount of information and its utilization. ” …How do you improve data intelligence?”
“That’s definitely the case [that there is a data knowledge gap] and for years we have been talking about data warehousing, or capturing that data, but turning information into data intelligence is a new journey for many companies…”
Or, how about the Gambling industry insiders view who characterizes difference between digitizing or converting your industry to the reality post conversion this way:
Advancements in technology has brought about a rapid digitization of gambling and almost every other industry. Some have managed to exploit these developments more than others and I think that the gambling industry is at the forefront of how well technology can be applied to a domain.
As an industry we must be open to change and pro-actively look at how we can exploit such technologies to provide a better and more entertaining experience to our customers. For example, the progress in Touch ID has enabled us to allow LeoVegas iPhone app customers to log in to the casino using only their fingerprint.
Are you wondering why distinguishing between transitions and transformations matter? Or, even better, how your business can take greater advantage of the widespread availability, access and flexibility that a fully digitized world creates?
Great, now you are thinking strategically.
Ever st
op to consider how the ever present changes going on around you make your own transformation easier?
John Hagel relatively recent blog post describes the opposite.
In a world of accelerating change, one of our greatest imperatives is to “unlearn” – to challenge and ultimately abandon some of our most basic beliefs about how the world works and what is required for success.
Accenture a few years ago noticed that many different companies had shifted their approach to strategy. Perhaps, the availability of cheap, powerful computing capacity and Big Data are responsible for driving changes in strategy development as more organizations using technology find it easier to build consideration of the future into their present planning. Hagel, a long time fan of scenario planning would applaud these efforts too.
With the rise of automated business processes, analytics too get incorporated automatically to enhance decision making and may be simultaneously compromising management capabilities to internalize all of these changes or understand the underlying dynamics traditional measures mask. Several articles provided case studies in different industries provided the basis of discussion around transformation (see the bottom of the post for specific article links).
Successful organizations rely on their strategy to put forward action plans, realize new ideas while averting risk. Statesmen and management alike find themselves in precarious places when they assume a trend will continue without change. Many statistical methods and decision-makers use of data remain unchanged from 1954 when Daniel Huff first published How to Lie with Statistics. His timeless book describes very simply the perils of improper use of methods that were designed to capture and explain if not contextualize the significance of singular observations, or data. The current transformations enabled by technology have done more to alter behavior than organizations seem to recognize. That’s the path our discussion took.
Prospective vs retrospective cohort analysis and data mining techniques are far from new. Though the volume and speed of available data to digest and process with ever The increasingly sophisticated tools and the ease with which volume and speed of available can be processed may help as well as hinder their digestion. Sure the time to test alternative scenarios may be faster, but how do you choose the model?
Do you begin with the intended outcome? The scientific method and numerous models from multiple disciplines make it possible to isolate factors, determine their significance, and estimate alternative scenarios and assess how these variations produce changes in impact.
Similarly, the cross pollination of data modeling from one discipline into multiple industries and use cases continue to shift management beliefs regarding the importance of specific factors and interactions in their processes. The perennial blind spot denies many organizations and their leadership the insight necessary to transform both their internal strategic thinking process and business operating models. Last month’s discussion of McDonald’s and Coca-Cola illustrated how easily leadership misinterpreted fluctuating performance as temporal issues versus recognizing structural factors. It’s one thing to balance efficiency and effectiveness, quality and satisfaction and another to manage awareness of change and insights necessary to your continued survival.
“…both the digital world and the physical one are indispensable parts of life and of business. The real transformation taking place today isn’t the replacement of the one by the other, it’s the marriage of the two into combinations that create wholly new sources of value. “
The sudden availability of online data tracking provided many organizations with the proper capability to understand user behavior differently. A whole new industry arose to focus on interpretation while creating of new measures while also introducing new thinking about effectiveness in sales, customer service, training etc. Metrics, once created to prove out a strategy or an idea, now leave many organizations vulnerable until they build up the capacity to understand this new thinking let alone make corresponding operational changes necessary to sustain their business.
This is not the story of companies who fail to adapt such as Kodak who invented digital cameras only to retain their focus on film; but maybe it is.
Computerized reporting dashboards summarize specific indicators or activity associated with managing process or business relevant factors. The time and reporting cost savings that result from the automatic generation and ready access to information by managers and executives reinforce existing thinking and leave little room for understanding wider changes that may be impacting their business. It wasn’t long ago that analysts, and teams of them, spent their entire day pulling data and then calculating critical statistics detailing the effectiveness and efficiency of organizational activities to create reports for senior management. These efforts also made them accountable by insuring the data was clean, verifying whether outliers were real or indicative of a model failing to fully capture the wider dynamics. I was once one of those managers. Today, automated reporting has eliminated many of the people capable of deeper data exploration and who chose what data, which statistics and the context necessary to understand the situation. The second problem is that data shared graphically or in tables never tell the whole story, though infographics do try.
A good analyst is taught to review the data and results, double-check whether the model or calculated results makes sense. Sure managers and executives may be quicker to detect aberrations and then raise questions but , how many of them have the time, patience or skills to test their ideas or intuitions? I imagine very few if any. Where are these available resources and how widely known are they to questioning executives? How might the dashboard provide additional information to help frame the results executives see as they too seek to understand or make sense of the results?
Established data flow processes and automated reporting do deliver great advantages but they may also explain why outsiders find it easier than insiders to create new business models. Where’s the out of the box thinking? And how can different data help?
Sure, it’s easy to blame regulatory requirements or compensation structures incentivized to focus on effectiveness and efficiency that leave little latitude to notice opportunity. For example, in the airline industry route fares were once set by regulations. The minimum fares were intended to cover airlines operating expenses that both insured passenger safety and access to air travel in more locations where market forces may lead airlines to cut corners. Deregulation may have given airlines additional freedom but many manage their business using the same metrics that they report to the Department of Transportation. Likewise in Healthcare, the imposition of new regulatory requirements came with new metrics that forced hospitals to focus on patient outcomes not just their costs.
When executives bottom line focus limits their thinking as an exercise in how making corrections in operation may maximize that number they overlook other contexts. Data quality issues should surface quickly in most organizations, but what if another factor created the data issue? A misplaced data point, or inconsistent treatment of the content of a data field rarely explain all aberrations in the results. Weather, for example exemplifies a ubiquitous, exogenous variable. Observable data fluctuations may be directly or indirectly responsible by affecting other more directly connected factors, such as a snowstorms that change people’s activity plans. I’m not familiar with any automated reporting system that will automatically create a footnote to the data point associated with the arrival of a snowstorm. The reviewer is forced to remember or manually if possible add the footnote for others.
Bain believes there are significant implications for every organization that result from this digital and physical combination of innovations , they call Digical. It’s not easy to keep up with the corresponding behavioral shifts that result from these rapidly changing technological capabilities.
Focusing exclusively on efficiency and cost data helped management measure impact in the old era, though still necessary today they may no longer suffice. Do you know how social behaviors of your customers impact your bottom line? The technologies to support your business, such as your website or your cash register misses out on the social behaviors evident on sites like Facebook, Twitter, Yelp or even their bank. Mapping the ecosystem and then aligning the digital tracking data can now be supplemented with sensor data that may be anonymous to specific customers but can inform movement and actions relevant to your engagement.
Naturally, as mentioned earlier bias plays a role in our inability to notice the significance of new data. The more we automate and configure systems to measure what we always knew mattered, the less likely we are to be able to recognize new data and its significance. What should you the analyst and you the executive do to counteract these factors?
Monitor the activity of smaller companies as they experiment to learn what’s most relevant.
Don’t make assumptions, exercise strategic intentions to become more open receptive and curious about anomalies and be more creativity and persistent in identifying the drivers or possible factors.
Historically, metrics were an output designed to assess the validity of your strategy –did it work and/or deliver value. Not it’s time for strategic thinking to view metrics as an input. The use of statistics enabling analysis tools partnered with business knowledge and acumen must be part of communicating to higher levels in the business.
Often we measure the wrong things because the incentives are misaligned. Am I paid based on my proven ability to produce widgets at specific levels , or to produce effective, sustainable results for the business, not just my business unit?
Computers are useless they can only give you answers. For strategy, validating the questions may be important but so too is taking the time and effort needed to determine even better questions.
Alternative case examples
Bain’s study and understanding of the state of “digical” transformation:
http://www.bain.com/publications/articles/leading-a-digical-transformation.aspx#sidebar
Fast Food
http://www.qsrmagazine.com/reports/drive-thru-performance-study-2014
Wireless
http://www.rcrwireless.com/20140812/opinion/reality-check-new-metrics-for-a-changing-industry-tag10
Television
http://fortune.com/2014/10/23/adobe-nielsen-tv-ratings-system/
Gaming
http://www.gamesindustry.biz/articles/2014-03-10-social-currency-has-real-value
