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There’s one thing we can all agree on –  management in a time of VUCA is an arduous task. So, there is a great temptation to be too eager to reach for the latest “hammer” (i.e. the latest management methods) in the naive assumption that every problem is a nail to be hit. And because managers are taking less and less time to think about things, they are morphed into agitated actors, naively hoping they will achieve their goal more quickly. It would be much better, however, to have some practical and user-friendly differentiation tools at hand to avoid falling into these traps. So, if you want to use your time and energy wisely, it is a good idea to know the difference between “blue” and “red”. Curtain up on the colour theory of transformation.

New colour theory

Colours have always been good orientation aids. Red and green traffic lights are not just for road traffic. Some companies have literally appropriated a colour for their brand so they can burn themselves into the customers’ memories. Think about German Telekom’s magenta or the Nivea blue. In the area of organisational development, too, there are colour codes that help to differentiate initial situations so that the most suitable and effective option for action can be selected.

We owe the categorization according to blue and red to the systems theorist Gerhard Wohland. He has thus described two very different problem categories, the differentiation of which is becoming increasingly important in the VUCA and transformation age. Because in fact, the world isn’t just about nails.

Blue stands for the world we are familiar with. Under this heading you can find everything that has a linear causality: Action A leads to result B – even the tenth time it is repeated. Blue challenges are relatively stable and at the same time quite complicated – think of a faulty movement, a technical problem or an upcoming tax audit.

Conversely, red stands for everything that is highly dynamic and complex. In the red world there are no predictable causalities, so action A sometimes leads to result B, but sometimes to result C or D. Red problems are also subject to constant change. Anyone who has ever dealt with an angry customer or the introduction of a transformation project knows what I mean.

Hammers and screwdrivers

The two colours do not just define different starting situations or problems. They also distinguish the tools of the trade that are the most useful for dealing with these scenarios.

Very few of us would think of tightening a loose screw with a hammer, yet in 2019, it is common for managers to work on red problems using blue tools. This is why so many transformation projects hit their limits and produce sub-optimal results and lots of frustration. Increasingly, we observe just the reverse, namely that blue problems are being tackled with red tools (“We’re working agile now!”). Then we wonder why the so-called promise of salvation in New Work doesn’t work out and instead, we are just becoming more and more inefficient.

The difference between skill and knowledge

The blue tools include above all knowledge. Without sound knowledge, no movement can be repaired, no technical problem solved and no tax audit carried out. You analyse, then you fall back on tried and tested methods. Solutions flow into processes, checklists, regulations and manuals. Project planning, stable structures and hierarchies enhance efficiency. The question now is: how does that work? So the expert is the king and knowledge makes all the difference.

In contrast, the red tools are mainly about ability, a mixture of creativity, intuition (a good feel for the situation), talent and a high level of dialoguing skills. This is what is needed when new, previously unimagined quantum leap solutions are on the agenda in a highly dynamic context. Instead of standardised processes, agile methods come into play, testing and observing in iterative cycles. Instead of rules, principles, i.e. attitudes made visible, come into play. Because there is no operating manual for solving the exceptional problems.

Rigid hierarchical models hit their limits and are replaced by more agile decision-making systems and organisational designs. Leadership occurs in the sense of social legitimation and ‘followership’: It drives forward the person that the others want to follow. Decisions and the resulting actions take place under ignorance, i.e. the precise consequences of an action cannot be predicted. The key question is: Who can handle it well? And so, the interdisciplinary team of experts becomes the king and the human being makes the difference.

Fatal errors and their ramifications

In fact, reality is usually much more complex than theory. There are many situations that involve both blue and red components, such as the global launch of powerful standard software by a customer. On the one hand, a demanding consulting project requires a great deal of standardised specialist knowledge (blue problem) and, on the other hand, a highly dynamic mix of reactances, power games, personal sensitivities and interdependencies with other transformation projects is high on the agenda (red problem).

In situations such as these, it is all the more important to differentiate very precisely and to use your blue and red tools in the right place, because if blue and red tools are exchanged due to ignorance or carelessness, this is what happens: For the blue portion, the result is a mediocre solution, because everyone has a say regardless of their level of expertise – and true experts are not usually the most extroverted speakers. At the same time, there is increased frustration at wasting time and energy, because there is unnecessary discussion about the “right” solution, even though “good practice” already exists. Meanwhile, the red part of the challenge blows up in the faces of the implementation team, because they are trying to manage complex dynamics with plans and insisting on rules and standard processes, so they inevitably fall flat on their faces.

A specific practical example:

Would you like to know how the colour theory of transformation is successfully applied in practice? Let’s take a look at Allsafe a Baden-Württemberg-based manufacturer of load securing systems for lorries and aircraft. Detlef Lohmann, the managing partner of the medium-sized company, already started over ten years ago to very systematically and successfully eliminate mechanisms that prevent value creation – and resisted the temptation to use the same tool to develop the optimal organisational setup. Instead, he made very careful observations, analysed the particular context and made repeated adjustments.

Two concrete examples that illustrate this: Today the organisational design for the sales process, which is primarily dominated by red, is strongly oriented towards the concept of networking and the principle of self-organisation. There is no sales manager and the team is responsible for defining its own annual targets. At Allsafe, the production process, on the other hand, has considerably more blue aspects. Consequently, the organisation relies on clearer structures and more hierarchical elements and chooses a lower degree of self-organisation (even though this is significantly higher than in some very conservatively-run organisations because the staff organise their own shifts). As well as this, there is great importance attached to systematically building up knowledge using a skill matrix, in order to ensure that there is a high degree of flexibility in terms of production.

Conclusion: each area has found its optimal setup and both solutions operate equally side by side. Their success has proven them right. The company has already been awarded top employer status four times and its profits are consistently on the rise.

Farewell to Aristotle

We are living in a time when the red parts of the world are increasing significantly. But the blue parts won’t all disappear overnight. Therefore, the ability to differentiate between red and blue value creation domains is a skill of elementary importance that is a challenge for us all: We are called upon to become bilingual and to alternate between the blue and red options for action, based on our needs. We must always be mindful that blue tools are not automatically bad and red is not automatically good. In other words: The screwdriver is not inherently better than the hammer.

The colour theory of transformation teaches us to detach ourselves from Aristotelian thinking in the sense of either-or, and instead to familiarize ourselves with thinking both ways. This is an attitude that can benefit us in many other situations. Let’s get started right now.

 

 

Programmatic Advertising

Programmatic advertising (PA) is a multi-faceted term. Many market players use it as a buzzword, a label for the hype that has at times raised very high expectations among lots of market players, especially advertising customers. Others frequently use PA as a synonym for automation projects that are several years overdue, especially in so-called classic media, but in which nothing is “programmatic”. At mediascale, we generally define programmatic advertising as data-driven media-buying and as a process we are only just beginning.

Therefore, the disappointment that may have occurred with one or other advertisers is not a fundamental issue for programmatic advertising. Rather, it should be an incentive to take programmatic to the next level: on the one hand, by rethinking the set-up of service providers and technology; on the other, individual expectations should be reasonably calibrated.

In recent years, it has mainly been venture capital-financed players, who wanted to be part of the media value chain, who have fuelled the programmatic hype starting from their own interests, which has led to high expectations. And of course they have claimed “their” part of the supply chain. But those who worked more intensively with the market knew that the quantity and quality of the available profile data for programmatic campaigns is limited. However, only good data can increase the efficiency of campaigns significantly enough so that the additional costs for the additional members of the value chain are reintegrated. A possible disappointment was thus an announcement or based on unrealistic expectations.

In many conversations with our customers, we have realistically presented both the possibilities as well as the limits of data-driven advertising in order to rule out exaggerated expectations of PA from the outset. In doing so, the following assumptions were made, which our customers, sometimes against their initial will, have been getting along well with so far:

  • Programmatic advertising is not a new channel with completely different rules to traditional display business. Also when auctioned and backed by data, a content ad remains a content ad and will not develop the advertising impact of an instream pre-roll large format
  • Meaningful, validated data is the indispensable basis for programmatic advertising. Here it is important to look carefully and carry out comprehensive auditing of the available data offers. At first glance, the data market in DMPs seems expansive. But data segments that deliver what they promise (delivering a corresponding uplift to campaigns) are by no means abundant. And they have their price.
  • An impression that cannot be assigned to valuable data should not be bought programmatically. As meaningful as it is to uniformly track all advertising contacts and accumulate all campaigns and pseudonymous profile data in one system, it makes little sense to put untargeted campaign volume into systems just to have bought it “programmatically”. This results in costs and technical performance losses that are not offset by financial added value.
  • The open market, open to all, originally proclaimed by many to be programmatic’s central promise of salvation, has created more problems than it solves, as it has also opened up the market to a plethora of dubious players. The efforts of the large, open sell-side platforms to push the black sheep out are commendable, but unfortunately not always successful. That is why we only buy inventories that we can thoroughly test, both technically and commercially. Furthermore, whenever possible, we buy from partners (often in private marketplaces) that we know and have established business relationships with – including any sanction options which may be necessary in the interest of the customer in an emergency.
  • And we’re not forgetting the creation: What use is the most sophisticated planning on a profile basis, if only one means of advertising is available? That’s why data driven creativity is, in our view, the indispensable fourth pillar of programmatic advertising – alongside technology, media space and data.

Today, programmatic has already caused major changes in the digital media business. But we are sure that this transformation process is far from finished yet. And it will encompass more and more media types in the future: TV, out-of-home, audio, cinema and eventually also print. In five years at the latest, we will be able to plan, book and control more and more channels via programmatic. In addition, people’s media use is evolving, new, relevant platforms are being created at ever-increasing speed, and data protection requirements also require fundamental and sometimes new solutions. All these challenges keep us busy and agile. Staying at the current level of development is not a solution. Particularly as we are just scratching the surface with programmatic.

This article was first published in adzine.

Joana Stolz talks about her job as Cultural Strategist at the Serviceplan Group and gives insights into her “every day work”.