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Get the most out of your strategy process: Two simple pitfalls to avoid

I’ve noticed a marked improvement over the last decade in the quality of Finnish companies’ strategy work. Strategy processes have gained an institutional role in management systems, increased investment in business intelligence provides a more comprehensive fact base to strategy work and strategy processes are generally well organized.

Yet I still find that strategy processes often fail. That is, they fail in meeting the two primary goals of a strategy process: creating a playbook for beating market performance and elevating the capabilities of the management team to continuously renew the company’s competitive edge in the future.

Too often strategy processes comprise a hasty mechanistic treatment of the topics supposed to be covered (vision, mission, competitive advantage, targets, development programs, financial projections, risks, …) combined to mind-numbing wordsmithing discussions (“do we target being the first, or primary, or preferred, anyone”?). The result is a lot of companies performing below their potential and suffering from poor quality strategy dialog within its management team.

Undoubtedly there are more than a few drivers to this problem, but below I discuss two essential pitfalls to which companies seem to stumble into time and again. I have selected to highlight this pair of issues because they are very straightforward to mitigate and have a disproportionally high reward if recognized and tackled.

PITFALL 1: Insufficient time allocation for strategy discussions and thinking

Strategic thinking and high-quality strategy debate require time. It is not a trivial challenge to define a strategy which gives a company a competitive edge in its market. True value creation in a strategy process requires time investment in many forms.

Firstly, time is required for analytically preparing strategy sessions to ensure the discussion builds on a correct understanding of what is happening within the company and its environment. Secondly, and more importantly, management needs time to study the fact base, prepare insight and participate with full engagement in common work sessions in which shared thinking is further refined. Finally, strategy sessions need to extend over weeks and months to allow the topics and decisions to cultivate in everyone’s subconscious.

When there is too little time to truly debate the strategy, the process can fail to create significant added value for the company. Major trade-off decisions may prove difficult to make, commitment to key outcomes may remain superficial and a large portion of the actual decision-making is in practice delegated to the next organizational layers.

To diagnose whether your management team has this issue ask this question:

What is the total percentage of time spent by the management team on high-quality strategy discussions over a calendar year?

If the answer is in low single digits (as is common) there is most likely room to improve.

The simple solution for this pitfall is to design your next strategy process well in advance with adequate time allocation.

PITFALL 2: Failing to leverage the strategy process as a learning experience

A strategy process is a unique opportunity for the management team to elevate their collective insight on the key value drivers of their business and industry – as well as to develop shared capabilities for problem solving and strategic planning.

Through a well-designed strategy process the management team can define a great strategy, while cultivating their collective capability to identify and tackle opportunities in the future. However, the learning aspect is easily neglected because of the high pressure to quickly develop a plan for seizing immediate opportunities and responding to emerging challenges.

Managers often join strategy work thinking they already know what the key challenges to tackle are and approach the whole process primarily as a chance to persuade others of their diagnosis. This makes learning and developing new thinking rather unlikely. Same managers leave the strategy process frustrated by the rest of the group for “not getting it” and having learned very little.

To identify whether you have been exposed to this pitfall consider the following questions:

  • Do the management team members manage to set aside their roles and responsibilities for the strategy debates and focus objectively on the firm level view as a team?
  • Does the management team’s strategy dialogue build on the thinking of each member in a healthy way – or is it dominated by a few and revolving around same topics?
  • Is the discussion based on a common analytical foundation – or is the discussion often driven by anecdotes?
  • Are sufficient feedback loops and consistent reflection practices in place to ensure continuous learning from past decisions?

Once recognized, this pitfall is also straightforward to tackle. Strategy work participants need to be reminded of the learning aspect of the process. Simple methods are available to help management team members to break out of their organizational roles. This issue is also directly linked to the above discussed time allocation. Fully leveraging strategy work as a learning experience requires investing time into addressing the emotional side of problem solving and decision making as a team. For this purpose, time should also be set aside for collective reflection on the quality of the dialog and defining the key bottlenecks to work on.

As mentioned, there are straightforward actions to mitigate these two pitfalls once they are identified. However, doing so is seldom easy. Management does not have excess time to allocate and no clear activities to de-prioritize to acquire it. Leveraging the strategy process as a learning tool is a lot about the mindset of the executives taking part. This makes it easy to note but very difficult to truly change. Hence, tackling these two issues requires full appreciation of the challenge and an active response from all participants.

Properly addressed, strategy work has the potential to provide an edge for Finnish companies. Finns have an exceptional foundation for it in our fact-based, engineering-oriented thinking, combined with our inclination for straightforward honest communication. All in all, Finnish companies are already on a steep learning curve with strategic planning and it is a privilege to support our clients on their journeys.

AI hype is feeding naïve business analytics

My problem with the current hype is not that AI would not hold the potential to drive the next industrial revolution. Truly amazing progress is being made in AI research labs and companies around the globe. My problem is that a great many things are being dubbed AI and way too often the AI jargon is nothing but a pretty wrapper for sloppy analytical thinking and naïve statistical methodology. Somehow people forget that correlation does not imply causation when they have a cutting-edge deep learning algorithm in their hands.

All too often we see the cool new hammer from the machine learning toolbox being offered as a solution even when the problem looks nothing like a nail. This is particularly problematic when predictive models are being offered as solutions to business problems requiring identification of causal effects. Simple regressions—and more complex predictive models for that matter—are often given causal interpretations without a second thought and marketed as cutting-edge AI or state-of-the-art econometrics. It is important to keep in mind that good prediction accuracy has nothing to do with the validity of the causal interpretation. Weight may be a great predictor of height in your model, but that does not mean you can make a middle-aged consultant any taller by putting him on a high-calorie diet. While this is clear to most people, the lesson that correlation need not imply causation often goes out the window when we turn to business problems.

The confusion around correlation and causation is particularly common in pricing and marketing optimization. Correlations between prices and volumes are often treated as measures of price elasticity, even though the relationship is also driven by supply side pricing responses to shifts in demand. Similarly, associations between marketing spend and business outcomes are used to quantify the impact of marketing, despite the fact that most marketing departments do not pick their spending levels randomly. If prices are raised when demand is high and marketing budgets expanded when good ideas and marketing opportunities abound, the correlations in the data are far from causal. The problem is not about slight biases or small errors either. Naively estimated effects can be off by an order of magnitude or even go in the wrong direction compared to the actual causal effect. For example, it is not uncommon to see positive price elasticity estimates—implying higher volumes at higher prices—even for basic products, when predictive algorithms are haphazardly applied to observational sales data. Yet such money machines hardly exist in practice.

To make progress on causal inference, we need to understand in great detail how the data we are using has been generated. Only then can we identify and isolate random-like variation in the variable of interest to help us uncover its causal effect. And when observational data cannot provide such variation, we should design randomized controlled experiments to help us tease out causal effects. This is important because basing key decisions or actions on naïve approaches and sloppy thinking can be extremely costly. Former Illinois Governor Rod Blagojevich, for example, wanted to spend $26 million a year to send a book per month to every child in Illinois, because children from book-filled homes perform better at school. Luckily for Illinois tax payers, the plan was rejected by the legislature as they undoubtedly realized—channeling their inner economist and Freakonomics author Steven Levitt—that “a book is in fact less a cause of intelligence than an indicator”.

The overselling of predictive models as causal and hyping them up as AI or cutting-edge econometrics is  quite misleading; at the core, these are still the same methods economists have largely dismissed as naïve since the 1980s. To push beyond correlations, the economics profession has focused on developing analytical strategies and statistical methods for credible estimation of causal effects. These strategies and methods—especially when coupled with advances in machine learning—can be very powerful, but are still largely missing from mainstream machine learning and data science toolkits.  Once we combine the power of machine learning with serious identification strategies to estimate causal effects, we can really start expanding the role of AI in solving critical business problems.

Top management must be data smart to get AI right

There is plenty of reason for top management to get excited about AI. The potential is enormous, progress seems spectacular, and quite frankly, artificial intelligence sounds much more exciting than good old analytics or statistics. A wide range of path-breaking applications from autonomous vehicles and chatbots to image recognition and diagnostic algorithms all fall under the broad umbrella of AI. Undoubtedly, AI will play a key role in driving innovation, transforming industries, reshaping the way we work, and putting data and analytics at the heart of several business enterprises. Simply calling something AI, however, does not turn garbage into gold.

Amid all the hype, the AI magic dust has become an easy sell and smart marketers are taking advantage. Venture capitalists, for example, are facing countless startups that are all about AI when raising money and talk the talk about cutting-edge machine learning when recruiting. When actually building their solutions, however, many of these startups still resort to old school statistics and traditional rule-based automation logics. Similarly, corporations must be alert when building or buying their new AI solutions. Most long-standing analytics challenges and pitfalls carry over to the brave new world of machine learning and AI. And with the focus increasingly on integrating AI into business processes and automating important decisions, the quality of the underlying analytics is becoming more important than ever.

One of the most common mistakes in applying AI to business problems is misinterpreting correlations as causal effects. While powerful prediction algorithms have numerous value-adding business applications ranging from content recommendation and promotion targeting to risk scoring and demand forecasting, many fundamental business problems center around causal questions. How does customer demand respond to price changes? How much additional revenue does an extra euro of marketing spend generate? Does early engagement with a digital service cause or simply predict customer loyalty later on? Questions like these cannot be answered by simply applying predictive AI to historical data. For example, higher housing prices might be a great predictor of larger transaction volumes because of their correlation, yet construction companies would clearly be foolish to let algorithms hike up their prices in the hopes of selling more apartments.

Other typical analytics pitfalls include misinterpreting patterns that have been imposed on the data as actual business insights and defining the analytics problems too narrowly to see the forest from the trees. The former pitfall often occurs when granular profitability data is used carelessly in AI applications. Steering sales and marketing decisions based on profitability measures that depend on arbitrary cost drivers and allocation rules is bound to lead you astray, no matter how sophisticated your optimization algorithms are. The latter pitfall, on the other hand, often manifests itself in simplistic optimization exercises. For example, optimizing price quotes for individual contracts without considering their ramifications on the capacity situation and competitor behavior in future negotiations can really hurt profitability.

Understanding what different data can and cannot tell us is a great asset for business executives as they look to harness the power of AI to solve business problems and create new growth opportunities. With cutting-edge analytics becoming a key source of competitive advantage and data assets so strategic that they motivate major transactions such as Microsoft’s $26 billion acquisition of LinkedIn, top executives must get smart about data. This does not mean that CEOs must suddenly become data scientists and learn to code in R or Python. What is needed instead is that they realize how data and analytics are transforming their industries, understand the value and blind spots of their data assets, and recognize business problems and opportunities that can be tackled with AI. Data literate top management is becoming a key advantage for companies as we move towards an increasingly data and AI-powered future.

Are you using analytics to advance business or increase busyness?

Are you using analytics to advance business or increase busyness? Approaching analytics from business questions has been recognized for years  yet many companies still approach it like an IT investment. Why? Read more about how to take the right stance on analytics.

Read more

How Expansive Can Expensive Be?

The cost of living, in particular the price of new homes, has risen and the trend shows no signs of slowing down. Especially large construction companies are boosting this trend by designing and building residences directed primarily to a wealthy clientele. This may not be a fully deliberate decision, but rather a consequence of a somewhat fragmented progression from lot acquisition through design, building, marketing and sales of the finished residences. Without shared information and objectives, the design phase may produce premium specifications whose construction costs far exceed their value to the end-users. The siloed process ends up with sales struggling to find affluent buyers in a market where they don’t necessarily exist. Is it really the most profitable, sustainable operating model for major construction companies?

The high-income target market can be very profitable, but is also risky and limited in size. The high-end segment may demand massive marketing efforts and extended time-on-the-market unless the demand and supply in each specific situation is well understood. And even so, the high-income segment is present in only a limited share of the national housing market.

Rather than focusing exceedingly on the higher income segment, large construction companies can find growth and profitable business by other means. Informed insight of the surrounding community, population and trends, as well as existing projects already under way in the area can be utilized to define the target groups and create concepts suitable for the specific situation. When potential buyers and their needs are taken into account early on, the whole process from design to sales can be fitted to support it.

There is plenty of internal and market data available to generate educated decisions. What’s needed is the identification of the right questions, data needs and sources to support the decision making, drawing of the right conclusions and crafting of the most functional and desirable recipe in an economically feasible way.

There’s a real win-win opportunity at play here. The needs and desires of individuals and communities are taken into account and fulfilled better. And a great starting point for a growing, profitable business for construction companies is established.

The change is significant, but essentially pretty simple. It requires that the construction companies put more effort into understanding the circumstances they intend to serve and tailor their sales and operations based on that knowledge. The answer is in the combination of strategic thinking, economics, data analytics and integrated operations. The end result is the right home, at the right time, in the right place, for the right price, for the right buyer. And good business for the construction company.

Transforming productivity in a stagnant industry by building a culture of trust and cooperation

Now after Slush 2017, it’s easy to remember how technology development during the past 20 years or so has greatly changed the ways how people live, work and recreate. However, there’s one industry which has stagnated in its renewal: construction sector.

Since the 1970s, construction sector’s labor productivity has remained almost flat. This poses a positive opportunity for change and as stated in the article in Helsingin Sanomat: “It’s funny to imagine that the outcome is something else if things are done in the same way.” (https://www.hs.fi/talous/art-2000005350624.html)

Some companies are already capturing this opportunity. According to August analysis, mid-sized general contractors and special contractors have clearly outpaced the market growth in Finland as the incumbent TOP 5 general contractors have stayed flat. One of the success factors for mid-sized contractors´ high performance is their innovative service concepts and business models both for new build and renovation. Consequently, the mid-sized contractors now capture an equal share of the construction profit pool with the large ones – a dramatic shift in just five years.

Does the poor productivity development matter to anyone outside the construction industry? Well it does since we all are customers and end-users for built environment. Finnish consumers have experienced constantly increasing housing expenses taking a larger share of their disposable incomes, despite the negative interest rates. Government holds a huge renovation debt in the public buildings and infrastructure, needing to choose which mold-school or hospital to renovate, while new infrastructure is desperately needed in the growth centers. These issues are difficult to resolve unless construction sector can produce more valuable outputs with the same inputs.

So, what can construction companies do to achieve their productivity leaps? According to August experience, the answers include well-managed design and planning, higher level of industrialization, lean construction site operations, technology and data utilization, aligned objectives across the parties, and a culture of collaboration. One thing that’s common to the most productive construction organizations I have worked with is that they identify themselves closer to running a repetitive production business rather than practicing the craft of construction in unique projects. Should I need to prioritize only one means for productivity improvement, I would choose culture: trustworthiness and collaboration are self-evident building blocks of many high-performing teams. Sometimes these traits are difficult to see in construction, but organizations which do succeed in embedding trust and collaboration into their DNA have a healthy foundation for experimenting new things, learning and becoming slightly better every day. Just like the cool tech start-ups in Slush.

Get Your Facts to Pitch Finland Right!

August Associates, in cooperation with Amcham Finland, has put together a Finland Fact Pack setting out the rationale for foreign businesses to invest in Finland and its people. Aimed at country managers and others dealing with investments, it showcases Finland’s stable and growing economy, and its tech-savvy, reasonably priced, highly educated and skilled workforce.

Like other Fact Packs produced by August Associates, putting the Finland Fact Pack together involved breaking complex problems down into manageable chunks before embarking on fact-finding interviews and other data collection strategies. The result is a one-stop shop containing all the information a company might need to provide the grounds for potential business here.

As the name suggests, the Finland Fact Pack contains hard facts and figures, such as taxes, labor costs, industry break-down, and so on. However, it also communicates something rather less tangible but no less important — the high level of trust that permeates throughout both the public and private sector in Finland. As Le Thuy, Analyst at August and creator of the Fact Pack, says, “If you say something will happen, [in Finland] it does.”

Read more about the creation of the Fact Pack from Tomi Ere´s and Le Thuy´s blog post:

Download your own copy of the Fact Pack by clicking the picture below or this link: http://goo.gl/UWq1xH


Change requires dialogue

In cooperation with Directors’ Institute Finland (DIF), August Associates hosted a breakfast seminar on November 17, 2017 for DIF members that addressed the topic of organizational change management as part of corporate transformations. The opening words for the seminar were held by Risto Murto, CEO of Varma. August Associates’ partner Pasi Torppa presented a perspective on how to identify the true drivers of organizational behavior in transformation situations (further on the approach in this blog post), and Timo Vuori, assistant professor in strategy at Aalto University, presented his latest research on how board of directors influence on the success of strategic initiatives. Following the presentations, Heikki Kapanen, Country Manager of Nets Finland, provided a DIF member comment on the topic, before opening the floor for discussion.

Pasi Torppa comments: “My expectations for the seminar were high, and I am pleased to say they were exceeded. Improving the understanding of to how drive change in a pragmatic way and catalyze transformation are essential topics for companies. I was hoping for discussion on board’s role in catalyzing the transformation and also more strategic considerations on corporate transformations and shareholder value. It was great to get to hear the thoughts and comments of DIF members on this fascinating topic, the level of discussion was truly impressive.”

Read more about the seminar on DIF’s website (in Finnish): http://dif.fi/ajankohtaista/muutos-edellyttaa-dialogia/  


What truly drives organizational behavior and decision making?

The board receives its information from the management, but what happens if the management does not understand the factors truly driving organizational performance?

Even if it sounds absurd, we have observed in several cases that the management, with a good understanding of the market trends and strategy, is not actually aware of what truly drives the performance of its middle management and personnel. This weakens the management’s ability to develop the business and to implement change.

Thus, the facts that the management relays to the board about the culture, motives and performance of the organization might be based on illusions or narrow key performance indicators that do not reveal the reality. As a result, the advice that the board, in turn, provides to the management regarding, for example, change management or post-merger integration, might be reactions to the false notions of the management and not fit to the actual situation.

The reasons behind the top management’s inaccurate understanding can be many, starting from a complicated organizational structure and unclear responsibilities to an extreme pace of change. When assessing organizational performance, one also often forgets the significant role of intuition and emotions as a factor that governs human actions alongside rational thinking. Traditionally, the focus has been on the latter, and management will optimize the rational incentives of the organization, even if the middle management is aligning their own behavior in accordance with what they think feels the best. No one in the management necessarily knows how the middle management is really feeling or what affects these feelings.

Analysis brings surprising results

There are new ways of forming a clear picture of the factors that guide behavior and decision-making. Our approach is based on in-depth interviews and their detailed analysis. In practice, we examine from the point of view of the individuals what motivates their actions, what helps them in carrying out their work well, and what in turn prevents them from achieving top performance. By comparing, analyzing and combining these descriptions we can form a picture of the most essential issues affecting performance on the company level. The method is called grounded theory, and is also used by the world’s leading management researchers. Based on our experience, the analysis often provides surprising results for the management.

A company’s recently appointed managing director was planning actions to turn around his organization’s financial performance. One idea was to give local units direct profit and loss responsibility, but our analysis showed that this would have been a mistake. There was no unified organizational culture in the company and the strong local focus led to blatant suboptimization, which the management’s original idea would only have made worse. The benefits of the ‘one company’ program which the company had undergone had not been realized either. To correct the situation, the role of local performance measurement was decreased, work rotation was increased and communication was improved, so that people could form a complete picture of the whole and act as a part of it.

In another company, employee engagement surveys had led to the finding that poor engagement was due to dissatisfaction with how the organization was managed. The senior executives assumed that the cause would lie in the management system and initiated a project to improve it. The management system was examined in comprehensive detail focusing on processes conducted by finance and HR functions, from performance measurement and appraisal practices to the reward systems. However, urgent improvement needs were not discovered. Our analysis, however, revealed that the true challenge lay within the interaction between the head office functions and its various units. The centralized functions of the group were trying to launch concepts that did not work in the local, decentralized organization. The concepts would not work, because there were significant differences in the local conditions, which the management was not sufficiently familiar with. However, due to pressure from the head office, the units ended up implementing concepts that were ill-suited to their business conditions. This incurred significant additional costs and satisfaction among employees went down. With our analysis, the management discovered the reason behind poor employee engagement and could initiate corrective actions.

Experience and intuition are not enough

One of the strengths of our method is that it produces a comprehensive description of people’s experiences at different levels of the company and of the factors affecting these experiences. This means that the management no longer needs to rely on a vague description of the organization’s culture in discussions with the board. Instead it can use concrete terms, descriptions and examples of behaviors that impact organizational performance, as well as of the factors that are affecting them. This makes it possible for the board to give more specific advice, instead of very generic guidance or reiterating previous more generic learnings.

What is essential is the identification of the levers that will enable implementing the desired change and improving organizational performance. Even the most experienced board professional’s intuition is not always enough in order to identify the right decisions in new circumstances. Forming a clearer and more granular picture of the factors affecting organizational performance will quickly pay off.

Pasi Torppa
August Associates

Timo Vuori
Assistant Professor in Strategy
Aalto University


Don’t Compromise on Compromise

Years of consulting on strategy and organization have taught me the value of balancing between different factors and interests to achieve progress. There is always a degree of compromise in the air, from first planning exercises to final implementation. In my personal experience, it is always more or less a bitter, if sometimes unavoidable pill. Converting aspirational into reasonable.

Diving into Finnish football as a member of FA council I discovered even higher levels of tolerance. Decades of accumulating good faith and compassion, devotion to seeking an all side encompassing balance of interests. Real empathy for the unselfish sacrifices of individuals, excusable even in the rare extremes that have led to inflexible positions producing no evident common value. All human.

The final outcome: compromise feeding on compromise.

After a long, taxing and successful organisation design process the implementation stage cannot be subjected to compromise. Quite the contrary, it should be a critical part of planting the seed of a new reconciled, open and broadminded culture. With aim tirelessly fixed on the collectively set goal.